Most curricula are still planned in advance and delivered at one pace for a whole group, even though students arrive with different prior knowledge and learn at different speeds. Digital learning platforms now record a steady stream of data about how each student works, which opens the possibility of curricula that change their structure and pathways while learning is happening. This article asks how such real-time adaptive curricula can be designed so that they are effective, fair and understandable to the people who use them. Using an integrative review of recent research in #learning_analytics, #adaptive_learning, knowledge tracing, intelligent tutoring and human-centred design, the article develops a design framework with five parts: continuous data capture, a learner model, a curriculum model expressed as a network of competencies, an adaptation engine, and a layer of human oversight and governance. The framework separates three levels of adaptation (micro, meso and macro) that work on different time scales and call for different amounts of teacher control. The review finds that adaptive systems often improve achievement and engagement, but effects vary by context, many proposed systems are never tested in real classrooms, and dashboards alone rarely change outcomes. The article argues that adaptation should be built around learning goals and cognitive load rather than around fixed labels of ability, and that teachers and learners should be able to see, question and override the decisions a system makes. Practical design principles and a worked course example are offered for students, teachers and designers. Keywords: learning analytics, adaptive curriculum, personalised learning pathways, knowledge tracing, cognitive load, human-centred design, educational data ethics 1. Introduction Think about a typical first-year university course. The syllabus is written months before the term begins. Every student receives the same readings in the same order, attends the same lectures, and sits the same tests on the same dates. Some students find the first weeks too easy and lose interest. Others fall behind in week three and never fully recover, because week four assumes that week three was understood. The course moves forward on a timetable, not on evidence about what each student has actually learned. This is not a failure of individual teachers. It is a feature of how curricula have been designed for a long time: as fixed plans for groups, not as living structures that respond to individuals. Over the last decade this situation has started to change. Much of learning now happens on digital platforms, and these platforms record what students do: which questions they answer, how long they spend on a task, where they hesitate, which hints they request, and how their answers change over time. The field of #learning_analytics grew out of the idea that this trace data can be measured, analysed and reported in order to understand and improve learning and the settings in which it happens (Lang et al., 2022). At first, much of this work was descriptive. It produced reports and dashboards that showed teachers what had already happened. A more ambitious idea is now taking shape: that the same data can be used to change the curriculum itself, while the student is still learning. This article calls that idea a real-time #adaptive_curriculum. In such a curriculum, the order of topics, the pace of progress, the difficulty of tasks, and the amount of support a student receives are not fully fixed in advance. They are adjusted continuously, based on a running estimate of what the student knows and how they are coping. A student who shows strong mastery early can skip practice they do not need and move on to harder material. A student who struggles with a key concept can be routed through extra explanation and worked examples before continuing. The curriculum becomes less like a straight road and more like a map with several routes to the same #learning_outcome. 1.1 The problem with one-size-fits-all pacing The argument for adaptation rests on a simple observation that every teacher knows: learners differ. They differ in prior knowledge, in how quickly they consolidate new ideas, in how much information they can process at once, and in how well they plan and monitor their own study. A fixed curriculum treats these differences as noise. Adaptive systems treat them as information. Reviews of AI-based adaptive learning describe this shift as moving from content designed for an average learner to content and pathways that respond to each learner's needs (Gligorea et al., 2023; Strielkowski et al., 2025). There is also a cognitive reason to care. Cognitive load theory holds that working memory is limited, and that the same material can be helpful for a beginner but redundant or even harmful for a more advanced learner (Sweller, 2023). A task that is well pitched for one student overloads another and bores a third. If a curriculum cannot respond to these differences, it will always be wrong for a large share of the class. #Personalised_learning, in this sense, is not a luxury. It is a way of keeping each student within a range where learning can actually happen. 1.2 Why real time matters Personalisation is not new. Teachers have always adjusted their teaching after a test or a conversation. What is new is the speed and scale at which adjustment can happen. When data arrives continuously, a system does not have to wait for the end of a unit to discover that a student is lost. It can notice repeated errors on a prerequisite skill within minutes and respond before the gap grows. Lim et al. (2023) showed, in a controlled study, that scaffolds generated from the real-time analysis of students' learning traces changed how students regulated their learning during a writing task. The timing of support, in other words, is part of what makes it work. At the same time, speed brings risks. Decisions made in seconds by an algorithm can be wrong, biased or impossible to explain. A student who is routed into a slower track because of a few early mistakes may never be given the chance to show what they can do. Real-time adaptation therefore raises design questions that go well beyond technical accuracy. Who decides how the curriculum changes? On what evidence? Can the student see why? Can a teacher stop it? 1.3 Aim, questions and contribution The aim of this article is to set out a clear, research-informed framework for designing real-time adaptive curricula driven by #learning_data. Three questions guide the discussion. First, what does recent research tell us about the effectiveness and limits of adaptive learning systems and analytics-based personalisation? Second, what components and design decisions are needed to build a curriculum whose structure and pathways can adapt in real time to a student's pace and cognitive needs? Third, how can such curricula remain fair, transparent and under meaningful human control? The article makes three contributions. It brings together several research strands that are often discussed separately: learning analytics, knowledge tracing, intelligent tutoring, learning path recommendation, cognitive load theory, and human-centred design. It proposes a layered design framework, illustrated with figures, that students and practitioners can use to analyse or plan an adaptive curriculum. And it offers a set of practical design principles, with a worked example, that translate the framework into decisions a course team can actually make. The article is written for students who want to understand this field, but it follows the structure of a research paper so that it can also support coursework and further study. The rest of the article is organised as follows. Section 2 reviews recent literature. Section 3 explains the method used to build the framework. Section 4 sets out the theoretical foundations and the core adaptive loop. Section 5 describes the framework in detail. Section 6 discusses evidence, risks and design principles, and works through an example. Section 7 concludes with implications and limitations. 2. Literature Review 2.1 From descriptive analytics to adaptive action Learning analytics began largely as a reporting activity. Institutions collected data from learning management systems and turned it into charts showing logins, submissions and grades. A recent overview of #learning_analytics_dashboards found that most dashboards still offer descriptive analytics, telling users what happened rather than predicting what will happen or advising what to do, and that their effect on #student_learning is rarely evaluated (Masiello et al., 2024). A systematic review by Kaliisa et al. (2024) went further and asked whether dashboards have lived up to the hype. Their answer was cautious: evidence that dashboards improve student achievement is limited, effects are mostly small, and results for motivation and participation are mixed. These findings matter for adaptive curricula because they show that information on its own is not enough. Showing a student a chart of their progress does not automatically change what they do next. This has pushed the field towards what is sometimes called prescriptive analytics: systems that not only predict outcomes but recommend or take action. Susnjak (2024) proposed a framework that combines predictive models with explainable AI and prescriptive analytics, and used a large language model to turn these outputs into readable, actionable feedback for students at risk. The direction of travel is clear. Analytics is moving from the rear-view mirror to the steering wheel. 2.2 Adaptive learning systems Adaptive learning systems are platforms that change content, sequence or support according to data about the learner. Kabudi et al. (2021) carried out a systematic mapping of AI-enabled adaptive learning systems and found a notable gap: many systems had been proposed in the literature, but far fewer had been implemented and evaluated in real educational settings. This gap between design and practice is a recurring theme and a useful warning for anyone planning an #adaptive_system. A clever algorithm on paper is not the same as a working curriculum in a classroom. More recent reviews paint a broadly positive but uneven picture. Gligorea et al. (2023) reviewed AI-based adaptive e-learning and reported that personalising content and pathways can improve engagement and outcomes, while noting challenges with data privacy and implementation. In higher education, a scoping review by du Plooy et al. (2024) found that most included studies reported improvements in academic performance, engagement or both when personalised adaptive learning was used. Strielkowski et al. (2025) framed AI-driven adaptive learning as a possible driver of sustainable change in education, while also stressing ethical and equity concerns. Taken together, these reviews suggest that adaptation can help, but that how it is designed and introduced matters a great deal. 2.3 Intelligent tutoring systems and evidence from practice Intelligent tutoring systems are a closely related tradition. They model a learner's knowledge step by step and give tailored hints and feedback. Wang, Tlili and colleagues (2023) reviewed intelligent tutoring systems used in real educational contexts and showed that their effects depend heavily on the context of implementation. In a review of AI-driven #intelligent_tutoring_systems in schools, Letourneau et al. (2025) found that effects on learning and performance were generally positive, but smaller when the systems were compared with non-intelligent tutoring. This is an important detail. Part of the benefit of tutoring software may come from structured practice and immediate feedback, not from the intelligence of the adaptation itself. Direct comparisons with ordinary teaching are also informative. Wang, Christensen and colleagues (2023) compared an adaptive learning system with teacher-led whole-class instruction in mathematics and found larger learning gains in the adaptive condition. Contrino et al. (2024) reported that an adaptive learning tool was associated with better performance and higher satisfaction in both online and face-to-face university courses. Such studies support the case for adaptation, but they should be read with care. Results from one subject, one age group or one platform do not automatically transfer to others. 2.4 Modelling what the learner knows Any adaptive curriculum needs a way to estimate what a student knows at a given moment. The main family of methods for this is called #knowledge_tracing. Knowledge tracing models take a student's sequence of responses and estimate the probability that they have mastered each skill. Abdelrahman et al. (2023) surveyed the field and described its development from Bayesian and factor-analysis models to deep learning models that can capture more complex patterns over time. Shen et al. (2024) organised knowledge tracing research into core models, variants and applications, and outlined open problems for future research. For curriculum design, the key point is that knowledge tracing turns a stream of answers into an evolving picture of mastery. This picture is never certain. It is an estimate that improves as more evidence arrives. A well-designed adaptive curriculum treats it that way, using thresholds and confidence levels rather than treating a single estimate as a final verdict on a student. 2.5 Sequencing pathways: from rules to reinforcement learning Knowing what a student knows is only half the problem. The other half is deciding what they should do next. Early adaptive systems used hand-written rules: if a student fails twice, show a worked example; if they pass a test, unlock the next unit. Newer research treats pathway choice as a sequential decision problem. Amin et al. (2023) framed learning path recommendation as a Markov decision process and used #reinforcement_learning to recommend personalised sequences of activities, reporting improvements over baseline methods. Li et al. (2023) used two levels of reinforcement learning, one to choose sub-goals and one to choose learning items, together with a knowledge graph that limits choices to items related to the learner's goal. Their results came from simulated learners. Zhou and Wang (2025) combined a #knowledge_graph of prerequisite relations with deep reinforcement learning and tested it with 200 learners of English over three months, reporting better recommendation accuracy than several comparison methods. These studies show that algorithms can learn effective routes through a body of content. However, they also show a pattern noted earlier: much of the most advanced work is tested in simulations or in single settings, and is reported in terms of recommendation accuracy rather than long-term learning. For a student or teacher, the lesson is to ask not only whether an algorithm picks good next steps, but whether the curriculum as a whole leads to durable understanding. 2.6 Self-regulation and hybrid human-AI control A further strand of research asks what happens to students' own ability to manage their learning when a system makes decisions for them. #Self_regulated_learning refers to the way learners set goals, plan, monitor and adjust their study. Molenaar (2022a) proposed the idea of hybrid human-AI regulation, in which an AI system first takes over parts of a learner's regulation and then gradually hands that control back as the learner becomes more capable. In a related paper, Molenaar (2022b) described six levels of automation that show how control over learning tasks can be shared between teachers, learners and AI. Ouyang and Jiao (2021) offered a similar distinction between three paradigms of AI in education: AI-directed, where the learner is a recipient; AI-supported, where the learner is a collaborator; and AI-empowered, where the learner leads. These ideas are central to adaptive curriculum design. A system that adapts everything for the student, all of the time, may produce good short-term results while leaving the student dependent and passive. Gasevic et al. (2023) argued that education in the age of AI should build learners' agency and their ability to work alongside AI, not only make learning more efficient. An adaptive curriculum should therefore be judged partly by whether students become better at steering their own learning. 2.7 Human-centred design, explanation and trust Because adaptive systems make consequential decisions, researchers have argued strongly for #human_centred_design. Buckingham Shum et al. (2024) reviewed the growth of human-centred learning analytics between 2019 and 2024 and its focus on involving educators and learners in designing analytics systems. Alfredo et al. (2024) found that stakeholders are mostly involved in the early design stages of such systems, while the question of how to balance human control with automation receives less attention. Martinez-Maldonado (2023) identified four challenges for this work, including achieving representative participation and handling power dynamics in decision-making. Closely linked is the need for #explainable_AI. Khosravi et al. (2022) proposed a framework for explainable AI in education that considers who needs an explanation, what benefit it brings, which methods and interface designs can provide it, and what pitfalls to avoid. For adaptive curricula, explanation is not an extra feature. If a student is told to repeat a unit, they deserve to know why, and a teacher needs enough insight to agree or disagree. 2.8 Ethics, fairness and privacy Continuous data extraction raises serious ethical questions. Tzimas and Demetriadis (2021) mapped the main ethical issues in learning analytics, including privacy, informed consent, transparency, data ownership and data security. Baker and Hawn (2022) reviewed evidence that educational algorithms can perform worse for some groups of students, for example by race or ethnicity, gender or nationality, and set out steps for moving towards fairer systems. The edited volume by Holmes and Porayska-Pomsta (2022) examines these concerns from several disciplines, including questions of human rights, accountability and pedagogy. For adaptive curricula, #algorithmic_bias is a particular danger because pathway decisions accumulate. A small bias in an early routing decision can lead to a large difference in what a student is eventually taught. The arrival of large language models adds a new layer. Kasneci et al. (2023) argued that these models offer real opportunities for education, but require new skills from teachers and learners and safeguards against bias, misuse and over-reliance. Yan et al. (2024) reviewed practical and ethical challenges and found low technological readiness, problems with replicability and transparency, and too little attention to privacy. Any adaptive curriculum that uses generative AI to produce explanations or tasks inherits these issues. 2.9 Gaps in the literature Four gaps emerge from this review. First, much of the research focuses on individual components, such as a better knowledge tracing model or a better path recommender, rather than on the curriculum as a whole system. Second, there is a persistent gap between proposed systems and systems evaluated in real teaching (Kabudi et al., 2021). Third, the role of teachers and learners in controlling adaptation is under-specified (Alfredo et al., 2024). Fourth, the link between adaptation and cognitive theory, especially cognitive load, is often implicit rather than designed in. The framework in this article is an attempt to address these gaps at the level of design. 3. Methodology This is a conceptual article. It does not report a new experiment. Instead, it builds a design framework through an integrative review of recent research, a method suited to topics where evidence comes from several disciplines and where the goal is to combine ideas rather than to calculate a single pooled effect. The review drew mainly on peer-reviewed journal articles, conference papers and scholarly books published from 2021 onwards, with a focus on work about learning analytics, adaptive learning systems, intelligent tutoring, knowledge tracing, learning path recommendation, self-regulated learning, cognitive load theory, human-centred design and the ethics of AI in education. The sources were read with three questions in mind: what components an adaptive curriculum needs, what evidence exists about effectiveness, and what risks and design requirements are reported. Findings were grouped into themes, and the themes were then organised into a layered framework. The framework was checked against the evidence in two ways. Each component had to be supported by at least one strand of the literature, and each major risk identified in the literature had to be addressed by at least one design feature. Finally, the framework was applied to a hypothetical course to test whether it leads to concrete and sensible design decisions. This approach has clear limits. It depends on the selection of sources and on the interpretation of the author. It cannot show that the framework will produce better learning; only empirical studies can do that. Its value lies in giving structure to a complex design problem and in pointing to the decisions that matter most. 4. Theoretical Framework A real-time adaptive curriculum is not only a technical system. It rests on assumptions about how people learn. This section sets out four theoretical foundations and then brings them together in a single model, the adaptive curriculum loop. 4.1 Cognitive load and the right level of challenge #Cognitive_load_theory begins from the idea that working memory is small and that learning suffers when it is overloaded. Sweller (2023) describes how the theory has grown, partly through failed replications that revealed new effects and boundary conditions. Two of these are especially useful for adaptive design. The first is element interactivity: some material is hard because many elements must be held in mind at once. The second is the expertise reversal effect: support that helps beginners, such as detailed worked examples, can become unhelpful or even harmful as learners gain expertise. For an adaptive curriculum, the expertise reversal effect is almost a design instruction. It says that the right form of support depends on the learner's current level, and that support should fade as expertise grows. A fixed curriculum cannot do this for every student. An adaptive one can, if it has a reasonable estimate of each learner's expertise and a set of task formats at different levels of guidance. In this article, adapting to a student's cognitive abilities means adapting to their current knowledge and load, not sorting them by a fixed idea of intelligence. 4.2 Mastery and the structure of knowledge Many subjects have a structure in which later ideas depend on earlier ones. In mathematics, a student cannot handle equations confidently without understanding variables. In statistics, interpreting a confidence interval depends on understanding sampling. This dependency structure suggests that progress should be based on #mastery of prerequisites rather than on time spent. It also suggests that a curriculum can be represented as a network: nodes for competencies and links for prerequisite relations. Recent work on path recommendation uses exactly this kind of knowledge graph to limit and guide choices (Li et al., 2023; Zhou and Wang, 2025). Seen this way, a curriculum is not a list but a graph with many possible routes. The fixed syllabus is just one route through that graph, chosen in advance for an imagined average student. An adaptive curriculum keeps the graph and chooses the route as the student moves through it. 4.3 Self-regulation and shared control The third foundation is self-regulated learning. Students who plan, monitor and adjust their learning tend to learn more effectively, and these skills are themselves a goal of education. Molenaar's (2022a) idea of hybrid human-AI regulation suggests that an adaptive system can temporarily take on some regulation, for example by choosing the next task, while helping the student to build the ability to make such choices themselves. Over time, control should shift back to the learner. This idea of #shared_control is built into the framework below, which gives learners real choices and increases those choices as they show readiness. 4.4 Feedback loops and human oversight The fourth foundation comes from the idea of a feedback loop. An adaptive curriculum observes the learner, updates its beliefs, makes a decision, acts, and then observes again. Each cycle is an opportunity to correct earlier errors. Human-centred learning analytics adds a crucial element to this loop: people must be able to see and influence it (Buckingham Shum et al., 2024). A loop with no human involvement can drift without anyone noticing. A loop with clear points for teacher review and learner choice can catch mistakes and keep decisions aligned with educational values. 4.5 The adaptive curriculum loop Figure 1 combines these foundations into a single model. The loop has five steps. In the first step, the system captures learning traces such as answers, response times, hint requests and navigation. In the second, it updates a learner model, which estimates mastery of each skill, the student's pace, and signs of overload or disengagement. In the third, it decides what should happen next, using a combination of rules, predictions and the curriculum map. In the fourth, it adapts the pathway by changing sequence, pace, difficulty or support. In the fifth, the learner acts on the new task, which produces new data and starts the loop again. At the centre sits human oversight: teachers can review and override decisions, and learners can make choices within the options offered. Figure 1. The adaptive curriculum loop: continuous data capture, learner modelling, decision, adaptation and learner action, with human oversight at the centre. The loop runs at different speeds. A hint can be offered within seconds of a wrong answer. A decision to add a review unit may be made after several days of evidence. A change to the course structure may only happen between terms. This difference in time scale is important and is developed further in Section 5.6. 5. Designing the Framework: Components and Decisions This section turns the adaptive loop into a design framework. The framework is organised in layers, from data at the bottom to governance at the top. Figure 2 shows the six blocks of the framework. Data flows upward, from raw traces to decisions. Rules, limits and values flow downward, from governance to the algorithms that act on them. Figure 2. A layered design framework for real-time adaptive curricula. 5.1 Layer 1: What data to capture and why The first design decision is what to measure. It is tempting to collect everything a platform can log, but more data is not always better. Every data point brings privacy costs and a risk of misinterpretation. A better approach starts from the questions the curriculum needs to answer. Does the student know this skill? Are they moving faster or slower than expected? Are they struggling with load or with motivation? Each question suggests specific data. For mastery, the most useful data is usually the correctness of responses on well-designed tasks that are clearly linked to specific skills. For pace, useful signals include time on task and the number of attempts before success, interpreted carefully, since a slow response can mean careful thought rather than confusion. For cognitive load, signals may include long pauses, repeated hint requests, rapid guessing, or short self-report questions such as asking students how hard a task felt. For engagement, patterns of logging in and dropping out of tasks can help. This continuous stream of #performance_data is the raw material of adaptation. Two practical rules follow. First, each type of data should be linked to a decision it can inform; if no decision depends on it, it should probably not be collected. Second, students should know what is collected and why. Tzimas and Demetriadis (2021) list transparency and informed consent among the central ethical issues in learning analytics, and these principles apply with special force when data drives decisions about a student's pathway. 5.2 Layer 2a: The learner model The #learner_model is the system's current belief about the student. At a minimum, it should hold an estimate of mastery for each skill in the curriculum, along with how confident the system is in that estimate. Knowledge tracing methods provide well-studied ways to do this (Abdelrahman et al., 2023; Shen et al., 2024). Simpler approaches, such as a rolling percentage of correct answers on recent items, can also work, especially in a first version. Beyond mastery, the learner model can include an estimate of pace, signs of load and engagement, and the student's own stated goals and preferences. Including the student's voice in the model matters. A system that only infers what students need, without asking them, can easily misread their situation. A short check-in question such as whether the student wants more practice or feels ready to move on adds information that no click log can provide, and it supports self-regulation. The learner model should also forget, at least partly. A mistake made three weeks ago should count for less than a correct answer today, because learning changes the student. Models that never discount old evidence can trap students in an outdated picture of themselves. 5.3 Layer 2b: The curriculum model The second half of Layer 2 is the curriculum model. This is a structured description of the course: its competencies, the prerequisite links between them, the tasks and resources attached to each, the assessments that show mastery, and the alternative routes students may take. In technical terms it is often a graph. In practical terms it is the course team's best statement of how the subject fits together. Figure 3 shows a simple example. A core spine runs from an entry check through three units to a #mastery_gate at the end. Around this spine are optional routes. Students who struggle with Unit A or Unit B can be routed into short support loops, such as worked examples or smaller steps, before returning to the core. Students who show strong prior knowledge at the entry check can take a skip test and, if they pass, move straight to Unit C. Students who finish early can take an enrichment route into an open project. Every route leads to the same final standard. This is the essential feature of a well-designed adaptive curriculum: flexibility in path, not in destination. Figure 3. An example curriculum graph with a core spine, support loops and acceleration routes leading to a common mastery gate. Building the curriculum model is mainly a job for teachers and subject experts, not for algorithms. It requires decisions about what really depends on what, which tasks give good evidence of mastery, and what kinds of support help at different levels. This is also where the idea of #learning_pathways becomes concrete. If the curriculum model is poor, no amount of clever adaptation will rescue it. 5.4 Layer 3: The adaptation engine The adaptation engine combines the learner model and the curriculum model to decide what happens next. There are three broad approaches, and most real systems mix them. The first approach is rule-based. Course designers write explicit rules, such as: if estimated mastery of a skill is below a threshold after a set number of attempts, route the student to a support loop; if mastery is high with good confidence, offer the skip test. Rules are easy to explain and easy for teachers to check and change. Their weakness is that they cannot easily handle complex patterns. The second approach is predictive. Models estimate the probability of future outcomes, such as failing the next assessment, and the engine acts on those predictions. Susnjak (2024) showed how predictive models can be combined with explanation methods so that the reasons behind a prediction can be shared with students. Predictive approaches can spot risks earlier than rules, but they bring a risk of bias if the data used to train them reflects past inequalities (Baker and Hawn, 2022). The third approach is optimising, using methods such as reinforcement learning to learn which sequences of tasks lead to the best results (Amin et al., 2023; Li et al., 2023; Zhou and Wang, 2025). These methods can discover effective routes that designers would not think of. However, they need a lot of data, they are harder to explain, and they depend on what the system is told to optimise. If the reward is defined as quick task completion, the engine may learn to give students easy tasks. If it is defined as performance on a test, it may learn to teach to the test. A sensible design is layered. Rules set the boundaries: the mastery gate cannot be skipped, no student can be held in a support loop indefinitely, and the student always has at least one choice. Within these boundaries, predictive or optimising methods can suggest the best next step. Every adaptive decision should be logged with its reason, so that it can be explained and audited. 5.5 Adapting pace, sequence, difficulty and support An adaptation engine can change four main things. Pace refers to how quickly the student moves through the curriculum. In a #self_paced design, a student who masters content quickly moves on, while a student who needs more time gets it without falling out of the course. Sequence refers to the order of topics, which can change when the curriculum graph allows several valid orders. Difficulty refers to the challenge level of tasks, ideally kept in a zone where the student succeeds often but not always. Support refers to hints, worked examples, explanations and #formative_feedback. These four levers do not have equal value. Research on cognitive load suggests that changing the form of support, for example moving from worked examples to independent problems as expertise grows, is one of the most reliable ways to match teaching to the learner (Sweller, 2023). Changing pace is powerful but has social costs, since students in a class may end up far apart. Changing sequence is useful only when the subject structure really allows different orders. Designers should therefore choose the levers that fit their subject and setting, rather than adapting everything because it is technically possible. 5.6 Three levels and three time scales A common source of confusion is that adaptation happens at very different levels. Figure 4 separates three. At the micro level, the system chooses the next item, offers a hint or shows a worked example. These decisions happen in seconds or minutes, and it is reasonable for the system to make them automatically, with the learner able to ask for more or less help. At the meso level, the system changes the order of units, the pace, or whether a student takes a support loop or a skip route. These decisions develop over days or weeks and are serious enough that the system should propose and a teacher should be able to approve, adjust or reverse them. At the macro level, the course structure, learning goals and assessment plan change. These decisions happen between terms and belong to teachers and institutions, informed by aggregated analytics. Figure 4. Three levels of curriculum adaptation, their time scales and the appropriate locus of decision. This distinction links directly to Molenaar's (2022b) levels of automation. It does not make sense to ask whether adaptive curricula should be automated in general. The better question is which decisions should be automated, at which level, and with what human checks. Real-time adaptation is most appropriate at the micro level and should become more cautious and more human-led as decisions become larger and harder to reverse. This principle of #human_in_the_loop control is one of the main contributions of the framework. 5.7 Layer 4: The human interface Layer 4 is where people meet the system. For teachers, this usually means a dashboard. Given the mixed evidence on dashboards (Kaliisa et al., 2024), the dashboard in an adaptive curriculum should do more than display data. It should show which students the system has rerouted and why, flag decisions that need approval, highlight students the model is uncertain about, and make it easy to override a decision. Teachers should also be able to see patterns across the class, such as a support loop that many students are entering, which may point to a problem in the original teaching rather than in the students. For learners, the interface should explain their current pathway in plain language and offer real choices. A message such as "You have shown strong understanding of sampling, so you can take a short check and move ahead, or continue with practice" respects the learner's agency. A message that simply moves them without explanation does not. Khosravi et al. (2022) stress that explanations should be designed for specific users and purposes. A student does not need to know how a model works internally; they need to know what it concluded, on what evidence, and what they can do about it. This kind of #transparency builds trust and supports self-regulation. 5.8 Layer 5: Governance and ethics The top layer sets the rules within which everything else operates. It includes policies on consent and data use, limits on how long data is kept, regular checks for bias in model outputs across student groups, procedures for students to question or appeal decisions, and clear statements of who is responsible when things go wrong. The ethics literature makes clear that these questions cannot be left to technical teams alone (Holmes and Porayska-Pomsta, 2022). #Data_ethics in adaptive curricula is a shared responsibility of designers, teachers, institutions and, ideally, students. One useful governance practice is the #fairness_audit. At regular intervals, the institution checks whether students from different groups are routed into support loops or acceleration routes at different rates, and whether those differences are explained by actual differences in mastery. If one group is sent to remediation more often without matching evidence, the model or the rules need to change. Baker and Hawn (2022) describe this kind of work as part of moving from unknown bias to systems that are known to be fair. 5.9 Evaluating the adaptive curriculum A framework is incomplete without a way to judge whether it works. Evaluation of an adaptive curriculum should happen at three levels, matching the levels of adaptation. At the micro level, designers can check whether hints and worked examples are followed by better performance on the next similar task. At the meso level, they can check whether students who took a support loop later reach the mastery gate at similar rates to those who did not need it, and whether students who took the skip test perform well on later units. If students who skipped ahead struggle later, the skip test is too easy. If students in support loops rarely catch up, the loops are not working or are being used too late. At the macro level, evaluation asks bigger questions. Did the course as a whole lead to better and more lasting learning than before? Did gaps between groups of students narrow or widen? Did students become more confident and more able to plan their own study? These questions need more than platform data. They call for delayed tests, surveys, interviews and comparisons with earlier cohorts or with similar courses. Kabudi et al. (2021) showed how often adaptive systems are proposed without being properly evaluated in practice. Building #evaluation into the design from the start is one way to avoid that pattern. It is also important to evaluate the model itself. A learner model that often misjudges mastery will make poor routing decisions however good the rules are. Course teams can compare the model's estimates with later performance and with teachers' own judgements. When the model and the teacher disagree, the reasons are often instructive. Sometimes the teacher has noticed something the data cannot show, such as illness or a family problem. Sometimes the data shows a pattern the teacher had missed. Either way, the comparison improves both the model and the teaching. 6. Discussion 6.1 What the evidence supports The literature reviewed here supports several cautious conclusions. Adaptive learning systems and intelligent tutoring often improve achievement and engagement compared with fixed instruction (du Plooy et al., 2024; Letourneau et al., 2025; Wang, Christensen et al., 2023). Real-time, analytics-based support can change how students regulate their learning (Lim et al., 2023). Algorithms for knowledge tracing and pathway recommendation are becoming more accurate (Shen et al., 2024; Zhou and Wang, 2025). These are encouraging signs for #data_driven_education. The evidence is weaker on other points. The effects of adaptation vary widely by context (Wang, Tlili et al., 2023). Comparisons with structured non-adaptive practice show smaller gains than comparisons with no tutoring at all (Letourneau et al., 2025), which suggests that some of the benefit comes from practice and feedback rather than from adaptation as such. Many proposed systems are never tested in real classrooms (Kabudi et al., 2021). And dashboards, a common way of presenting analytics to teachers and students, show limited effects on their own (Kaliisa et al., 2024). For a student reading this research, the right attitude is interest combined with scepticism. Adaptive curricula are promising, not proven. 6.2 Pace and ability: avoiding the labelling trap The title of this article speaks of matching a student's pace and cognitive abilities. This phrase needs careful handling. There is a long history in education of sorting students into fixed groups based on judgements of ability, and such sorting can limit opportunities, especially for students from disadvantaged backgrounds. An adaptive curriculum could repeat this history in digital form if it treats early performance as a permanent label. The framework avoids this in three ways. First, it adapts to current knowledge and current load, both of which change, rather than to a fixed trait. Second, all routes lead to the same mastery gate, so adaptation changes the journey but not the expectation. Third, the learner model discounts old evidence and gives students repeated chances to show progress, for example through skip tests that remain available. These choices express a simple principle: #educational_equity requires that adaptation opens doors rather than closing them. 6.3 Risks of real-time adaptation Several risks deserve attention. The first is surveillance. Continuous data extraction can make students feel watched, a form of #student_surveillance, which may change how they learn and reduce their willingness to explore or make mistakes. Clear limits on data collection and honest communication help, as does giving students access to their own data. The second risk is narrowing. A system optimised for measurable short-term outcomes may push students towards tasks that are easy to score and away from open-ended thinking, discussion and creativity. The enrichment route in Figure 3 is one way to protect space for these activities, but the deeper solution is to keep macro-level curriculum decisions in human hands. The third risk is gaming. When students learn how a system responds, some will try to manipulate it, for example by guessing quickly to reach hints, or by deliberately failing to get easier tasks. Good design uses several signals rather than one, and focuses rewards on genuine mastery. The fourth risk is fragmentation. If every student follows a different path at a different speed, the shared experience of a class can disappear. Discussion, group work and peer learning depend on students working on related material at roughly the same time. A sensible compromise is to allow flexibility within units while keeping some shared checkpoints, such as weekly seminars where students at different points bring their questions together. The fifth risk comes from generative AI. Systems that produce explanations or tasks on the fly can save time, but they can also produce errors, and their outputs are hard to check at scale (Kasneci et al., 2023; Yan et al., 2024). In an adaptive curriculum, generated content should be reviewed or drawn from a checked bank, at least for anything that counts towards assessment. 6.4 The changing role of the teacher A common fear is that adaptive curricula will replace teachers. The evidence and the framework suggest the opposite. Celik et al. (2022) found that AI can support teachers in planning, teaching and assessment, but that its reliability depends on context and its limitations are real. In the framework, teachers design the curriculum model, set the rules, approve meso-level changes, interpret class-level patterns and decide macro-level changes. What changes is the nature of their work. Less time is spent delivering the same content to everyone at once; more time is spent diagnosing problems, supporting individuals and small groups, and improving the course. The teacher becomes the designer and supervisor of a learning system, not only its presenter. #Teacher_agency is not reduced by good adaptive design. It is redirected. 6.5 Design principles The analysis can be summarised in ten design principles for real-time adaptive curricula. Principle 1: Start from learning goals. Define the competencies and mastery standards first, and build adaptation around them. Adaptation should change the route, not lower the destination. Principle 2: Model the subject before modelling the student. A clear curriculum graph with sound prerequisite links is the foundation of everything else. Principle 3: Collect only data that informs a decision, and tell students what is collected and why. Principle 4: Treat mastery estimates as uncertain. Use confidence thresholds, and let recent evidence count more than old evidence. Principle 5: Use cognitive load as a guide. Fade support as expertise grows, and watch for signs of overload as well as boredom. Principle 6: Match automation to the level of the decision. Automate micro-level choices, require teacher approval for meso-level changes, and keep macro-level decisions with people. Principle 7: Give learners real choices, and expand those choices as they show they can manage them. Principle 8: Explain every adaptive decision in plain language to the people it affects. Principle 9: Audit regularly for bias, and check that routing differences between groups are explained by real differences in learning. Principle 10: Evaluate in real settings, and measure long-term learning and student agency, not only short-term task success. 6.6 Common design mistakes Experience with adaptive systems, and the gaps identified in the literature, point to several common mistakes that student designers and course teams should avoid. The first mistake is to start with the technology. A team buys or builds an adaptive platform and then looks for content to put into it. The result is usually a collection of tasks with weak links to learning goals. The framework reverses this order: goals and the curriculum model come first, and technology is chosen to serve them. The second mistake is to adapt only difficulty. Many systems make tasks harder after correct answers and easier after wrong ones, and stop there. This can keep students busy without addressing why they are struggling. A student who keeps failing because of a missing prerequisite needs to be routed back to that prerequisite, not given easier versions of the same task. The curriculum graph is what makes this kind of #diagnostic_routing possible. The third mistake is to hide the logic. When students do not understand why the system has changed their path, they may lose trust, try to game it, or simply ignore it. Short, honest explanations cost little and protect the relationship between students and the course. The fourth mistake is to forget the teacher. Systems that send all decisions to students directly, bypassing teachers, remove the person best placed to notice when something has gone wrong. Even a simple weekly review of routing decisions by a tutor can catch many errors. The fifth mistake is to treat the first version as final. Adaptive curricula improve through use. The data they produce shows which explanations work, which tasks confuse students, and which prerequisite links were wrong. Teams that plan for regular revision, guided by #continuous_improvement, get far more value from adaptation than teams that switch it on and leave it. 6.7 A worked example: an introductory statistics course To show how the framework works in practice, consider a twelve-week introductory statistics course for first-year students in a social science programme. The class is large, and students arrive with very different mathematical backgrounds. Some took advanced mathematics at school; others have not studied it for years. The course team begins with the curriculum model. They identify about twenty competencies, from reading tables and graphs to interpreting regression output, and map the prerequisite links between them. They group these into six units forming a core spine, with a final mastery assessment. For each unit they prepare core tasks, a support loop with worked examples and shorter steps, and a skip test. They also design two enrichment projects using real data sets. Next, they decide what data to collect. Students complete short online practice tasks linked to specific competencies. The platform records correctness, number of attempts, hint use and time on task. At the end of each practice set, students answer one question about how difficult it felt and one about whether they want more practice. No other personal data is used for adaptation. This is explained to students in the first week, and they can view their own data at any time. The learner model estimates mastery for each competency using a simple knowledge tracing approach, with more weight on recent attempts. It flags students whose estimates are uncertain or who report high difficulty several times in a row. The adaptation engine works at three levels. At the micro level, it chooses the next practice task within a unit, increasing difficulty after correct answers and offering a worked example after repeated errors on the same competency. Students can always ask for an easier or harder task. At the meso level, it proposes routing decisions: a student with low mastery on probability after two attempts is offered the support loop; a student with high mastery on the first three units at the entry check is offered the skip test. These proposals appear on the teacher's dashboard, where tutors can approve them, change them, or add a note. Students see a short explanation of each proposal and can choose to accept it or stay on the standard route. At the macro level, the course team reviews aggregated data at the end of term. If many students needed the probability support loop, they may redesign the original teaching of that topic for the next year. Shared experience is kept through weekly seminars, where students working at different points discuss a common real-world case. Every four weeks, there is a shared #checkpoint that all students complete, so no one drifts too far from the group. Halfway through the term, the course team runs a simple fairness check, comparing routing rates across groups of students and discussing any unexplained differences. This example is deliberately modest. It uses simple models and keeps humans in charge of significant decisions. That is the point. A real-time adaptive curriculum does not have to rely on the most advanced algorithms to be useful. It needs a clear structure, honest data practices, sensible rules, and people who can see and shape what the system is doing. 6.8 Implications for students For students, adaptive curricula bring both opportunities and responsibilities. The opportunity is a course that responds to what you actually know, lets you move faster where you are strong and gives you more help where you need it. The responsibility is to engage honestly with the system, to use the choices it offers, and to question decisions that seem wrong. Students who understand how adaptive systems work, what data they use and how to read their recommendations critically are better placed to benefit from them. In this sense, understanding #educational_technology is becoming part of being a capable learner. 6.9 Implications for institutions and future research For institutions, the framework suggests that adopting adaptive curricula is less a matter of buying software and more a matter of curriculum redesign, staff development and governance. Course teams need time to build curriculum models. Teachers need training to interpret dashboards and to manage classes where students are at different points. Institutions need clear data policies and processes for fairness audits and appeals. For researchers, several questions remain open. We need more studies of adaptive curricula in real courses over full terms, measuring long-term learning, transfer and student agency. We need research on how different levels of automation affect students' self-regulation over time. We need evidence on how adaptive pathways affect the social side of learning. And we need better methods for explaining complex models, such as those based on reinforcement learning, to teachers and students. The field of #AI_in_education has produced strong technical tools; the next step is to show how they work as part of whole curricula, in ordinary classrooms, for all students. 7. Conclusion This article asked how real-time adaptive curricula, driven by continuous learning data, can be designed to match students' pace and cognitive needs while remaining fair, understandable and under human control. Drawing on recent research, it proposed a layered framework with five main parts: data capture, a learner model, a curriculum model, an adaptation engine, and layers for human interaction and governance. It described an adaptive loop in which learning traces are turned into decisions and decisions into new learning experiences, with teachers and learners able to see and influence each step. Three main findings emerge. First, the evidence that adaptive learning can improve outcomes is encouraging but uneven, and depends heavily on design and context. Second, the most important design decisions are not only about algorithms. They concern how the subject is structured, which data is collected, what the system is allowed to decide alone, and how its decisions are explained. Third, adaptation should be organised by level. Small, reversible decisions can be automated in real time, while larger decisions about pathways and course structure should involve teachers and, where possible, learners. The article also argued that adapting to cognitive abilities should mean adapting to a student's current knowledge and load, not to a fixed label. Every pathway should lead to the same standard, and every student should have repeated chances to show progress. This principle protects equity and keeps the focus on learning rather than sorting. The framework has limitations. It is conceptual and based on a selective review of recent literature, and it has not been tested empirically. Many of the studies it draws on come from specific subjects, platforms or countries, and some advanced methods have only been tested in simulation. The worked example is illustrative rather than evidence. Future studies should implement and evaluate frameworks like this one in real courses, with attention to learning, fairness and student agency. Despite these limits, the central message is clear. Real-time adaptive curricula are no longer a distant idea. The data, models and platforms exist. The challenge now is to design them well: to build curricula that respond to each learner, keep humans meaningfully involved, and use data in ways that students can understand and trust. If that challenge is met, #adaptive_education can help close the gap between how fixed curricula are planned and how real students learn. References Abdelrahman, G., Wang, Q., & Nunes, B. (2023). Knowledge tracing: A survey. ACM Computing Surveys, 55(11), Article 224. https://doi.org/10.1145/3569576 Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gasevic, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. 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Interactive Learning Environments, 31(2), 793-803. https://doi.org/10.1080/10494820.2020.1808794 Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gasevic, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/10.1111/bjet.13370 Zhou, L.-Y., & Wang, Y.-Y. (2025). Simulation of personalized English learning path recommendation system based on knowledge graph and deep reinforcement learning. Scientific Reports, 15(1), Article 34554. https://doi.org/10.1038/s41598-025-17918-x #AdaptiveCurriculum #learning_analytics_research #real_time_learning #personalized_pathways #EdTech #adaptive_learning_design #student_success #smart_learning #future_of_education #curriculum_design #AIinEducation #data_informed_teaching #higher_education #STULIB #learning_science

Many of the problems that today's graduates will face, from climate adaptation to the fair use of artificial intelligence, do not respect the boundaries between university departments. Yet most students still learn science, technology and ethics in separate courses, taught by separate staff, and assessed in separate ways. This article asks which curriculum models are best able to break down these academic silos and help students solve complex problems that require scientific knowledge, technological skill and ethical judgement at the same time. Using an integrative review of recent peer-reviewed research published mainly between 2022 and 2026, the article builds a conceptual framework that places systems thinking at the centre of integration, and then uses it to evaluate seven curriculum models: parallel add-on courses, integrated STEM units, socioscientific issues teaching, challenge-based learning, embedded ethics modules, competence-based spiral curricula, and transdisciplinary studio or STEAM courses. Six criteria guide the evaluation: depth of integration, explicit systems thinking, the weaving of ethics into tasks, real-world authenticity, assessment alignment, and ease of adoption. The analysis finds that no single model is strong on every criterion. Models that integrate most deeply tend to be the hardest to assess and to scale, while models that are easy to adopt often leave ethics and systems thinking at the margins. The article argues that systems thinking works best as a shared language that connects disciplines, and it proposes a six-step design cycle that teachers and students can use to build more connected learning. Implications for assessment, teacher collaboration and the teaching of emerging technologies are discussed. Keywords: systems thinking, interdisciplinary curriculum, integrated STEM, ethics education, challenge-based learning, complex problem solving, curriculum design, higher education 1. Introduction Imagine a group of final-year students asked to advise their city on whether to install an artificial intelligence system to manage traffic lights. The engineering students quickly start to talk about sensors and data speeds. The environmental science students ask how the system would change air quality near schools. The philosophy students ask who will be held responsible if the system gives priority to some neighbourhoods over others. Each group is asking a good question, but none of them is asking the whole question. The real problem sits in the spaces between their subjects, and their education has given them very few tools for working in those spaces. This small scene captures a large issue in modern education. Universities and schools are still mostly organised into departments, faculties and subjects. These structures have real strengths. They protect deep expertise, they give students a clear sense of what they are learning, and they make it easier to plan timetables and award degrees. But they also create what many educators now call #academic_silos, where knowledge is stored in separate containers and rarely mixed. Students may finish a degree knowing a great deal about one area but knowing very little about how that area connects to others, and even less about how to make sound judgements when scientific facts, technological options and human values pull in different directions. The problems that matter most in the coming decades are exactly of this mixed kind. Scholars of public policy describe them as #wicked_problems, meaning problems that are marked by deep uncertainty, high complexity and strong disagreement about values (Head, 2022). #Climate_change, pandemics, food security, and the governance of new digital technologies all fit this description. They cannot be solved by science alone, by engineering alone, or by ethical argument alone. They call for people who can see how parts of a system affect each other and who can weigh technical evidence alongside questions of fairness, rights and responsibility. Two ideas have become central in the search for better ways to prepare students for this kind of work. The first is #systems_thinking, a way of looking at the world in terms of connected parts, feedback loops, delays and boundaries rather than isolated events. The second is #interdisciplinary_curriculum design, which tries to bring different subjects together around shared problems, projects or themes. Both ideas are popular in policy documents and conference talks. But it is much less clear which specific curriculum models actually help students combine science, technology and ethics, and under what conditions. This article addresses that gap. It asks three linked questions. First, what does recent research say about the strengths and weaknesses of curriculum models that aim to break down silos between science, technology and ethics? Second, how can systems thinking be used as a framework for judging these models? Third, what practical design principles follow for teachers, curriculum planners and students themselves? The article makes three contributions. It offers a simple conceptual framework, shown later in Figure 1, that places systems thinking at the centre of the relationship between science, technology and ethics. It uses this framework to evaluate seven common curriculum models against six clear criteria, giving students and educators a structured way to compare options. And it turns the findings into a practical design cycle that can be used in courses at different levels. The article is written for students as well as teachers, because students are not only the people who receive a curriculum. They are also the people who must make sense of it, move between its parts and, increasingly, help to shape it. The rest of the article is organised as follows. Section 2 reviews recent research on academic silos, systems thinking, integrated STEM education and ethics education. Section 3 sets out the conceptual framework. Section 4 explains the review approach and the evaluation criteria. Section 5 evaluates the seven curriculum models. Section 6 discusses cross-cutting issues, including assessment, teacher collaboration and the special case of artificial intelligence, and presents the design cycle. Section 7 concludes with implications and limitations. 2. Literature Review 2.1 The problem of academic silos The idea that disciplines can become isolated from each other is not new, but it has gained new urgency as societies face problems that cross every boundary at once. Head (2022) explains that wicked problems combine three features: uncertainty about facts and future effects, complexity in the way causes and consequences are linked, and divergence in the values and interests of the people involved. Because of this mix, such problems resist neat technical fixes, and any response tends to reshape the problem itself. Educators have recognised that university courses are not yet well designed for this kind of work. Wren et al. (2025) note that higher education institutions increasingly face difficulties in designing courses that give students the transdisciplinary knowledge, skills and understanding needed to engage with complex wicked problems. When students meet such problems without preparation, they may try to reduce them to something simpler that fits the subject they already know best. Recent work has also drawn attention to the moral side of these problems. In a scoping review of how higher education deals with the moral dimensions of wicked problems, Schmitz et al. (2026) identified four recurring themes: fairness and ethics in assessment, social justice and fair access to higher education, ecological responsibility, and the management of uncertainty in teaching and learning. Importantly, they found that although moral wicked problems are increasingly mentioned in the higher education literature, systematic approaches to building them into teaching and curriculum design remain limited. In other words, universities talk about these problems more than they design for them. Studies in specific settings echo this pattern. In engineering, for example, ethics and sustainability are often delivered as supplementary content, separate from the core technical courses that take up most of a student's time (Huang et al., 2026). In computing and engineering more broadly, calls to integrate ethics into technical teaching have multiplied, yet the learning goals of such efforts are often underspecified and different kinds of ethical concern are sometimes mixed together (Ferdman and Ratti, 2024). The result is that students may learn ethics as a separate topic that has little to do with the practical work they will do in their careers. 2.2 Systems thinking in education Systems thinking has a long history in fields such as engineering, ecology and management, but over the past few years it has become a major goal for education at every level. In #sustainability_education, it is now treated as one of the most important competencies that learners need. Redman and Wiek (2021) carried out a large systematic review of more than 270 peer-reviewed articles on sustainability competencies. They found strong agreement among scholars on five key competencies: systems thinking, anticipatory (future-oriented) thinking, normative competence, strategic competence and interpersonal competence. They also identified three emerging competencies: intrapersonal competence, implementation competence and integration competence. Their framework is useful here because it shows that systems thinking does not stand alone. It sits alongside normative competence, which is closely related to ethics, and alongside integration competence, which is about bringing different kinds of knowledge together. A scoping review of sustainability competencies in secondary education by Sposab and Rieckmann (2024) screened 2,659 publications from 2003 to 2023 and reached a similar conclusion. They found a global consensus on the importance of #critical_thinking, systems thinking and #action_competence, and they called for a holistic approach that includes cognitive, emotional and behavioural sides of learning. They also noted that most studies came from Europe, especially Germany and Sweden, which is a reminder that our picture of good practice is still shaped by a limited set of contexts. Systems thinking is not only for science and engineering students. Baena-Morales et al. (2023) argued that even physical education can be used to mobilise critical and systemic thinking in relation to the #Sustainable_Development_Goals, by helping university students see their subject in terms of health, community and environment rather than only physical activity. This is a helpful reminder that systems thinking is a general way of seeing, not the property of any single discipline. A major challenge in this area is assessment. Dugan et al. (2022) systematically reviewed systems thinking assessments in engineering. They found a wide range of tools, from tests to #concept_maps to scenario tasks, but very few that gave equal weight to the technical and the contextual sides of a problem. Many assessments measured either the technical understanding of how a system works or the awareness of social context, but not both together. This matters a great deal for the present article, because integrating science, technology and ethics is precisely about holding the technical and the contextual in view at the same time. Norris et al. (2022) added a further insight. They asked twenty undergraduate engineering students to work through two established scenario-based assessments of systems thinking while thinking aloud. They found that the way a scenario was presented strongly shaped how students approached it, which makes it hard to know whether a test is measuring systems thinking or the student's response to a particular story format. Students also reported that their courses had given them few chances to practise solving #ill_structured_problems. The authors concluded that curricula need to offer more intentional opportunities to work on such problems throughout a degree, not only in a final project. 2.3 Integrated STEM and interdisciplinary curriculum A second body of research focuses on #integrated_STEM education, which brings together science, technology, engineering and mathematics. Integrated STEM has been strongly promoted by governments and education systems around the world. A systematic review by Portillo-Blanco et al. (2024) examined how integrated STEM is defined and described in research. They found areas of consensus about its principles and characteristics, but also considerable diversity in how the term is used. This diversity matters, because a curriculum that calls itself integrated may in practice be only lightly connected, with subjects sitting side by side rather than truly combined. The position of each discipline within integration is also a concern. In an overview of research on mathematics in interdisciplinary STEM education, Goos et al. (2023) discussed how mathematics is positioned when it is combined with other subjects and pointed to the importance of keeping disciplinary depth while making connections. This tension between depth and breadth runs through almost all the literature on interdisciplinary teaching, and it is especially relevant when ethics is added to the mix, since ethics can easily become a short discussion at the end of a technical unit rather than a real partner. Sustaining integration over time is a further challenge. Han et al. (2022) studied secondary school STEM classes after an integrated STEM project had ended. Their work looks at what remains in classrooms once project support is withdrawn, and it highlights that integration depends heavily on teachers, their collaboration and the support around them. #Teacher_preparation therefore matters. Chamo and Broza (2025) examined how a group of second-career STEM teacher trainees worked through an interdisciplinary curriculum development process, showing that designing integrated curricula is itself a learning experience that shapes how new teachers see their role. Interdisciplinary curriculum work has also moved strongly into the field of sustainability. Ahmad et al. (2023) presented the CoDesignS framework for embedding #education_for_sustainable_development in university curriculum design. Evaluated through focus groups and interviews with academics, curriculum designers, students and government officials, the framework was found to make sustainability explicit in the curriculum and to support the competencies students need to bring about change beyond the classroom. Lewis and Bader (2026) went a step further by showing how the Redman and Wiek competency framework could be turned into a real master's programme at the University of Bern, using a spiral structure in which systems thinking and related planning competencies come early and integration competence comes at the end, in the master's thesis. 2.4 Ethics in science and technology education A third body of research deals with how ethics is taught to students of science and technology. Ferdman and Ratti (2024) observed that embedded ethics programmes in engineering often suffer from two problems: their learning goals are not clearly specified, and they confuse ethics at the level of individual conduct with ethics at the level of society. To address this, they proposed a framework with three dimensions. The ethical dimension concerns the learning goals themselves. The moral dimension concerns whether an engineer's individual conduct is right or wrong. The political dimension scales these questions up to the level of institutions, laws and public life. This three-part framework is useful because it shows that teaching ethics is not one task but several. In school science, #socioscientific_issues (SSI) teaching has become one of the main ways of bringing ethics and values into science lessons. A systematic review by Hogstrom et al. (2024) examined 157 studies of SSI teaching drawn from more than 5,000 articles published between 1997 and 2021. They found that SSI teaching mostly aims to develop higher-order thinking and science content knowledge, that the topics fall mainly into two areas, environment and sustainability on the one hand and health and technology on the other, and that group discussion is by far the most common teaching method. SSI teaching therefore offers a well-tested way of linking science and ethics, although its connection to technology design and to systems thinking is less developed. Applied studies show what deep integration can look like. Huang et al. (2026) evaluated a curriculum reform of a building water supply and drainage engineering course that wove sustainability, ethical responsibility and human-centred values into technical teaching, using the CDIO approach (Conceive, Design, Implement, Operate). Working with a full cohort of 100 undergraduate students in a pre-test and post-test design, they reported statistically significant improvements in sustainability awareness, ethical responsibility, human-centred design thinking and systems thinking. They also stressed that these gains were achieved without adding extra load to the curriculum, because the values were built into the existing technical course rather than added on. Ethics education is also being reshaped by the rapid spread of #artificial_intelligence. A systematic review by Memarian and Doleck (2023) looked at fairness, accountability, transparency and ethics in research on AI and higher education. They found that the literature contains more definitions of these ideas than actual studies, and that accountability and transparency in particular need more research. Dabis and Csaki (2024) analysed the first policy responses of thirty leading universities to generative AI. A central theme was that human beings keep moral and legal responsibility for wrongdoing related to AI, and that clear communication about AI use in course syllabi is an emerging good practice. Giannakos et al. (2024) described a mood of cautious optimism about generative AI in education, but warned against adopting such tools hastily without thinking about their effectiveness, their wider effects on the education system, their ethics and their teaching value. 2.5 Gaps in the literature Taken together, these studies show a rich and growing field. However, three gaps stand out. First, the literatures on systems thinking, integrated STEM and ethics education often develop separately, which is ironic given that all three are concerned with breaking down silos. Second, there are few accessible comparisons that help educators and students judge the relative strengths of different curriculum models for integrating science, technology and ethics specifically. Third, assessment remains a weak point across the field, with tools that tend to measure either technical or contextual understanding, but rarely both together (Dugan et al., 2022). This article responds to these gaps by bringing the three literatures together under a single systems thinking framework and by using that framework to compare curriculum models. 3. Conceptual Framework: Systems Thinking as the Connecting Lens 3.1 Why a systems lens? Most attempts to integrate subjects begin with a theme or a topic. A school might run a unit on water, or a university might offer a module on smart cities. Themes are useful starting points, but they do not by themselves tell students how to connect what they learn in one subject to what they learn in another. A theme can easily become a label placed over three separate sets of lessons. What is needed is a way of thinking that actively links the parts. Systems thinking offers such a way of thinking. At its simplest, it asks four questions about any situation. What are the parts? How are they linked? What feedback loops, delays or unexpected effects arise from those links? And where have we drawn the boundary of the system, and who or what have we left outside it? These questions are not owned by any one discipline. A biologist can ask them about an ecosystem, an engineer about a power grid, and an ethicist about the distribution of benefits and harms in a community. Because the questions are shared, they can act as a common language that allows students from different backgrounds to talk to each other. The fourth question, about boundaries, is where systems thinking most clearly meets ethics. Deciding what lies inside or outside a system is never purely technical. When engineers design a new technology and count only its direct users, they leave out the people affected by its waste, its energy use or its social side effects. Choosing a boundary is therefore partly a moral choice about whose interests count. This is one reason why systems thinking is so useful for connecting science and technology with ethics. It turns ethical questions from an extra topic into a normal part of describing the problem. 3.2 The framework Figure 1 presents the conceptual framework used in this article. It shows three domains, science, technology and ethics, arranged around a central core of systems thinking. Each domain brings something distinct. #Science contributes evidence, models and an understanding of natural systems. #Technology contributes design, tools and an understanding of engineered systems. #Ethics contributes values, rights and a sense of responsibility. The double arrows between the core and each domain show that systems thinking both draws on and feeds back into each one. Figure 1. Conceptual framework: systems thinking as the connecting lens between science, technology and ethics. The lines between the domains represent three zones of integration. Between science and technology lies socio-technical modelling, where students use scientific understanding to predict how a technology will behave in the real world. Between science and ethics lies evidence-informed moral reasoning, where students use data and scientific findings as part of an ethical argument, while recognising that facts alone do not settle questions of value. Between technology and ethics lies responsible design and impact assessment, where students consider how design choices create or reduce harm. The outcome at the top of the figure is the capacity to frame and act on complex, value-laden problems. This capacity draws on what Redman and Wiek (2021) call integration competence, the ability to bring different kinds of knowledge and competence together in a coherent way. It also draws on the political dimension of ethics described by Ferdman and Ratti (2024), since many complex problems require students to think not only about what an individual should do but also about how institutions and policies should respond. 3.3 Levels of integration A second part of the framework concerns how deeply subjects are combined. Figure 2 shows a continuum that is widely used in the literature on interdisciplinary education. At one end is disciplinary teaching, where each subject keeps its own methods and problems. Next comes multidisciplinary teaching, where subjects sit side by side around a shared theme but exchange little. Then comes interdisciplinary teaching, where concepts and methods from different subjects are combined to address a shared problem. At the far end is transdisciplinary teaching, where disciplines work together with people outside the university, such as communities, industry or government, to create knowledge and action together. Figure 2. The integration continuum and the typical place of ethics at each level. The bottom line of Figure 2 shows how the place of ethics tends to change along this continuum. In disciplinary programmes, ethics is often a separate course. In multidisciplinary programmes, it may appear as a case study added to a technical unit. In interdisciplinary programmes, it can be embedded in the tasks themselves. In transdisciplinary programmes, ethical questions are often shared and negotiated with communities and stakeholders. This pattern is a simplification, and real programmes can mix levels, but it helps to explain why some curriculum models are better than others at weaving ethics into problem solving. It is worth stressing that moving along the continuum is not always better in every respect. #Disciplinary_depth remains essential. A student cannot reason well about the ethics of a vaccine programme without understanding some immunology, or about the fairness of an algorithm without understanding how it is trained. The goal of the framework is not to replace disciplines but to connect them, so that students can use deep knowledge from several fields when a problem demands it. 4. Approach and Evaluation Criteria 4.1 Review approach This article uses an #integrative_review approach. Integrative reviews bring together different kinds of evidence, including empirical studies, systematic reviews and conceptual papers, in order to build a new understanding of a topic. This approach was chosen because the research on interdisciplinary curriculum is spread across several fields, including science education, engineering education, sustainability education, higher education research and the ethics of technology, and because it uses many different methods. Sources were identified through searches of scholarly databases using combinations of terms such as systems thinking, interdisciplinary curriculum, integrated STEM, embedded ethics, socioscientific issues, challenge-based learning, transdisciplinary education and wicked problems. Priority was given to peer-reviewed journal articles and scholarly books published within roughly the last five years, so that the review reflects current thinking. Older classic works were not cited directly, although many of the recent sources build on them. Each source used was checked against its publication record to make sure that its details were accurate. The review is interpretive. It does not calculate effect sizes or pool data across studies, because the studies differ too much in their aims, settings and measures for such pooling to be meaningful. Instead, it reads the studies through the framework in Section 3 and draws reasoned judgements about the strengths and weaknesses of each curriculum model. Readers should treat these judgements as a structured argument based on the literature, not as statistical findings. 4.2 Evaluation criteria Six criteria were used to evaluate each curriculum model. They were derived from the framework and from recurring concerns in the reviewed literature. The first criterion is depth of integration. This asks how far the model moves along the continuum in Figure 2, from subjects sitting side by side to subjects that genuinely combine their concepts and methods. The second criterion is explicit systems thinking. This asks whether the model directly teaches students to identify parts, links, feedback and boundaries, or whether it simply hopes that systems thinking will emerge from working on a broad theme. The third criterion is the weaving of ethics into tasks. This asks whether ethical reasoning is part of the main problem-solving work, or whether it is placed in a separate course or a final discussion. It draws on the distinction made by Ferdman and Ratti (2024) between individual, moral questions and societal, political ones. The fourth criterion is #real_world_authenticity. This asks whether students work on problems that are genuine, open-ended and connected to actual people, places and decisions. The fifth criterion is assessment alignment. This asks whether the model comes with assessment approaches that capture integrated thinking, rather than measuring each subject separately. The importance of this criterion follows from the findings of Dugan et al. (2022) and Norris et al. (2022) on the weaknesses of current systems thinking assessments. The sixth criterion is ease of adoption. This asks how much institutional change, staff time and coordination the model requires. A model that is excellent in theory but almost impossible to run in a typical university or school will have limited impact. For each model, the analysis in Section 5 gives a judgement of low, medium or high on each criterion. These judgements are summarised in Figure 3 at the end of that section. They are the author's interpretive appraisal, based on the reviewed studies, and are offered as a starting point for discussion rather than a final verdict. 5. Evaluating Curriculum Models 5.1 Model one: parallel add-on courses The most common way of bringing ethics and wider perspectives into science and technology degrees is to add separate courses. An engineering student might take a required course in #professional_ethics. A biology student might take a module on science and society. A computer science student might attend a short course on data protection. This model keeps the main technical curriculum largely unchanged and places the new content alongside it. The strengths of this model are clear. It is easy to adopt, because it does not require technical teachers to change what they do. It allows ethics to be taught by specialists who know the field well. It is also easy to assess in the usual way, through essays, exams or case analyses. For these reasons it often satisfies #accreditation requirements and is popular with administrators. The weaknesses are just as clear. Because ethics is taught separately, students may come to see it as unrelated to their real technical work, something to be completed for a grade and then set aside. Huang et al. (2026) made a related point when they observed that ethics and sustainability are often delivered as supplementary content, which limits their connection with core engineering instruction. The add-on model also does little to develop systems thinking, since the separate course rarely asks students to model how a technical system and its social context interact. In terms of the framework, the add-on model sits at the disciplinary or multidisciplinary end of the continuum. It scores low on depth of integration, explicit systems thinking, ethics woven into tasks and authenticity. It scores medium on assessment alignment, because each course can be assessed well on its own terms but integration is not assessed at all, and high on ease of adoption. It is a reasonable first step for institutions with few resources, but it is unlikely on its own to produce graduates who can handle complex, value-laden problems. 5.2 Model two: integrated STEM units Integrated STEM units bring together science, technology, engineering and mathematics around a shared #design_challenge or investigation. A typical example might ask secondary students to design a water filter, combining chemistry, engineering design and data analysis. In universities, integrated units might appear in first-year design courses or in project modules that span several departments. The great strength of this model is that it genuinely connects scientific understanding with technological design. Students must use scientific concepts to make design decisions, and they see the results of their decisions in working prototypes or simulations. This is the zone of socio-technical modelling shown in Figure 1. Integrated STEM also has a large and growing research base, and it is supported by many education systems. However, the review by Portillo-Blanco et al. (2024) showed that integrated STEM is defined and practised in many different ways, so the label alone tells us little about how deep the integration really is. Goos et al. (2023) highlighted the challenge of keeping each discipline visible and meaningful within an integrated unit. Han et al. (2022) showed that keeping integrated STEM alive after a project ends depends on teacher collaboration and ongoing support. Most importantly for this article, integrated STEM does not automatically include ethics. The acronym itself names four technical fields and leaves out values, rights and social consequences. Unless teachers deliberately add ethical questions, students may design an efficient water filter without ever asking who can afford it or who decides where it is installed. In terms of the framework, integrated STEM scores medium on depth of integration and explicit systems thinking, low on ethics woven into tasks, and medium on authenticity, assessment alignment and ease of adoption. It is a strong foundation for connecting science and technology, but it needs to be extended if it is to connect both of them to ethics. 5.3 Model three: socioscientific issues teaching Socioscientific issues teaching starts from controversial questions where science and society meet, such as genetic modification, vaccination policy, climate action or the use of personal data in health research. Students learn the relevant science, but they also discuss values, weigh evidence, consider different viewpoints and make reasoned decisions. The main strength of this model is that it brings ethics into the heart of science learning. The review by Hogstrom et al. (2024) showed that SSI teaching usually aims to develop higher-order thinking and scientific knowledge together, and that topics often concern the environment, sustainability, health and technology. These are exactly the kinds of topics where science, technology and ethics overlap. SSI teaching also has a well-developed set of methods, especially structured #group_discussion, and it can be introduced within existing science courses without major structural change. Its limitations are related to its origins in school science. SSI teaching tends to focus on debate and decision making rather than on designing technological solutions, so the technology domain in Figure 1 is often weaker than the science and ethics domains. It also does not always teach systems thinking explicitly. Students may debate whether a technology is good or bad without mapping the system of actors, flows and feedback loops that shape its effects. Finally, the strong reliance on group discussion, while valuable, means that assessment often focuses on argument quality rather than on the ability to integrate knowledge into a workable plan. In terms of the framework, SSI teaching scores medium on depth of integration and explicit systems thinking, high on ethics woven into tasks, and medium on authenticity, assessment alignment and ease of adoption. It is especially valuable as a way of teaching evidence-informed moral reasoning, the zone between science and ethics in Figure 1. 5.4 Model four: challenge-based learning #Challenge_based_learning asks students to work in teams, often across disciplines, on open-ended challenges drawn from society, industry or the community. Unlike traditional #problem_based_learning, where the teacher usually sets a defined problem, challenge-based learning often starts with a broad theme and asks students to identify and frame a specific challenge themselves, and then to develop and test solutions with real #stakeholders. Challenge-based learning has grown quickly in higher education, especially in engineering and technology programmes. Van den Beemt et al. (2023) developed the CBL-compass, a framework for analysing the characteristics of challenge-based learning within and across courses, and argued that variety in how it is implemented is not a weakness but a feature that should be understood and planned. A systematic review by Galdames-Calderon et al. (2024) identified 20 relevant studies published between 2013 and 2023 and grouped teaching practices into four dimensions: pedagogical approaches, technological integration, industry engagement, and support for student development. They found a clear shift from traditional teaching towards facilitating roles that encourage innovative problem solving, but they also found that research on specific teaching practices was scarce and that educators need specialised training. At Malmo University, an interdisciplinary group of researchers described challenge-based learning through eight key elements grouped into three domains: diversity and inclusion, co-creation and collaboration, and change agents working on contextual challenges (Christersson et al., 2022). This framing is important because it brings values such as inclusion and social change directly into the definition of the model. Kasch et al. (2023) studied a ten-week online, interdisciplinary and inter-university course on sustainable cities that used challenge-based learning. Students from three universities reported low perceived distance and a strong sense of presence, valued the open and interactive character of the course, and reflected on the interdisciplinary competences they had developed. The strengths of challenge-based learning are its high level of authenticity and its deep integration, since students must draw on whatever knowledge the challenge requires. Ethics often enters naturally, because real challenges involve real people with different interests. However, ethics is not guaranteed to be treated carefully. Without guidance, teams may focus on technical feasibility or business value and treat ethical concerns as a box to tick. Systems thinking may also remain implicit unless teachers provide tools such as system maps. The biggest weaknesses lie in assessment and adoption. Open-ended challenges produce very different outcomes for different teams, which makes fair assessment difficult, and running such courses requires coordination across departments and partnerships with outside organisations. In terms of the framework, challenge-based learning scores high on depth of integration and authenticity, medium on explicit systems thinking and ethics woven into tasks, and low on assessment alignment and ease of adoption. 5.5 Model five: embedded ethics modules #Embedded_ethics is a model in which short ethics components are placed directly inside technical courses. Instead of sending students to a separate ethics course, an ethicist or a trained technical teacher introduces ethical questions within, for example, a #machine_learning course, a circuit design course or a bioengineering laboratory. The ethical content is linked to the technical content of that specific course. The appeal of this model is that it shows students, week after week, that ethical questions arise in the middle of technical work and not only at the end. Huang et al. (2026) offered evidence that this kind of integration can work. By weaving sustainability and ethical responsibility into a core engineering course, they reported significant gains not only in ethical responsibility but also in systems thinking and human-centred design thinking, without increasing the overall course load. Their study suggests that embedding values in technical teaching can strengthen, rather than weaken, technical competence. Ferdman and Ratti (2024) warned, however, that embedded programmes often have vague learning goals and tend to mix individual and societal levels of ethics. Their three-part framework of ethical, moral and political dimensions offers a way to fix this. A well-designed embedded module would make clear whether it is asking students to consider their own conduct as professionals, or to consider how a technology should be governed by institutions and laws, or both. Without such clarity, embedded modules risk becoming a series of disconnected case studies. Embedded ethics also tends to be weaker on systems thinking. A module placed inside a single technical course may focus on one product or decision and not on the wider system in which the product operates. This is where the boundary question from Section 3 becomes useful. Teachers can ask students not only whether a design is ethical but also where they have drawn the boundary of their analysis and who has been left out. In terms of the framework, embedded ethics scores medium on depth of integration, low on explicit systems thinking, high on ethics woven into tasks, and medium on authenticity, assessment alignment and ease of adoption. 5.6 Model six: competence-based spiral curricula The sixth model works at the level of a whole programme rather than a single course. A #spiral_curriculum returns to the same core competencies again and again across a degree, each time at a higher level of complexity. When the competencies chosen include systems thinking, normative or ethical competence and integration competence, the result is a programme in which integration is planned from the first semester to the last. The clearest recent example is the master's programme in sustainability transformations at the University of Bern described by Lewis and Bader (2026). Through a participatory co-design process involving staff and stakeholders, the programme mapped the competencies from the Redman and Wiek (2021) framework onto a sequence of modules. Early modules focus on planning competencies such as systems thinking, futures thinking and strategic thinking. A special module is dedicated to intrapersonal competence. The final master's thesis emphasises integration competence. Disciplinary, general and professional competencies are included throughout. The authors stressed the importance of collaborative development, constructive alignment between goals, teaching and assessment, and repeated refinement. A smaller-scale example comes from school chemistry. Nguyen et al. (2026) developed a sequence of five chemistry lessons that integrated education for sustainable development through a systems thinking approach, tested with 44 ninth-grade students in Vietnam. They found a positive trend in most students' systems thinking skills across the five lessons, but the paths of development differed between skills. Importantly, they also found that strict alignment with mandatory learning outcomes in the national curriculum may have limited the development of some systems thinking skills. This is a useful warning: a spiral design can only work if the wider curriculum gives it room to grow. The strengths of the competence-based spiral model are its explicit focus on systems thinking, its strong assessment alignment, since competencies can be assessed repeatedly at rising levels, and its depth of integration across a whole programme. Ethics is included through normative competence, although it may be framed mainly in terms of sustainability values rather than the full range of ethical questions raised by technology. The major weakness is ease of adoption. Redesigning an entire programme requires time, leadership and agreement among many staff members. In terms of the framework, the competence-based spiral model scores high on depth of integration, explicit systems thinking and assessment alignment, medium on ethics woven into tasks and authenticity, and low on ease of adoption. 5.7 Model seven: transdisciplinary studio and STEAM courses The final model goes furthest along the integration continuum. #Transdisciplinary courses bring together students and teachers from science, engineering, the arts, the humanities and the social sciences, often working with community partners, to address a real wicked problem. Many of these courses use a studio format, in which students work intensively on projects in a shared space, and many describe themselves as #STEAM, adding the arts to science, technology, engineering and mathematics. Wren et al. (2025) developed and tested a framework for STEAM higher education in which #design_thinking is combined with four features of creative pedagogy: transdisciplinarity, embodied dialogue, empowerment and agency, and ethics and trusteeship. Using the framework in four international short courses with 82 participants in total, each focused on a different local wicked problem, they found that the creative pedagogy features and the design thinking approach worked together in productive patterns. Notably, ethics and trusteeship appear here as a core feature of the pedagogy, not an add-on. Allen et al. (2025), writing as educators in transdisciplinary higher education at an Australian university, examined how embodied approaches to teaching can help students engage with wicked problems and societal transitions. They identified four dimensions of their practice: transdisciplinary sensemaking, emotion-driven creativity, empathetic enactment, and embodied boundary-spanning. Their work suggests that integrating knowledge is not only an intellectual task. It also involves feelings, #empathy and a willingness to step outside one's comfort zone, all of which matter when ethical questions are at stake. The strengths of this model are its very high depth of integration, its strong treatment of ethics and its high authenticity. Its weaknesses are the same as those of challenge-based learning, only more so. Assessment is difficult because outcomes are diverse and often creative or embodied. Adoption is hard because such courses need flexible spaces, cross-faculty staffing and strong partnerships. Systems thinking is often present but not always taught explicitly, since the emphasis may fall on creativity and dialogue rather than on formal tools such as system maps or #causal_loop_diagrams. In terms of the framework, the transdisciplinary studio model scores high on depth of integration, ethics woven into tasks and authenticity, medium on explicit systems thinking, and low on assessment alignment and ease of adoption. 5.8 Comparing the models Figure 3 brings the seven evaluations together in a single matrix. Several patterns stand out. Figure 3. Interpretive appraisal of seven curriculum models against six criteria, based on the reviewed literature. First, there is a clear #trade_off between depth and ease. The models that integrate most deeply, challenge-based learning, competence-based spiral curricula and transdisciplinary studios, are also the hardest to adopt. The model that is easiest to adopt, the add-on course, integrates least. This pattern is not surprising, but it is important for students and educators to see it plainly, because it means that choosing a curriculum model is always a matter of balancing ambition against what is realistically possible. Second, explicit systems thinking is the weakest column overall. Only the competence-based spiral model scores high on this criterion. Most other models assume that students will develop systems thinking simply by working on complex problems. The research on assessment reviewed in Section 2 suggests that this assumption is risky. If systems thinking is not taught and assessed directly, students may not develop it, or teachers may not know whether they have. Third, ethics is handled very differently across the models. Socioscientific issues teaching, embedded ethics and transdisciplinary studios all treat ethics as central, but integrated STEM and add-on courses tend to keep it separate or leave it out. This suggests that the simple act of combining science and technology, as integrated STEM does, is not enough to bring ethics into problem solving. Ethics must be designed in deliberately. Fourth, assessment alignment is weakest in exactly the models that are most authentic. Challenge-based learning and transdisciplinary studios produce rich and varied student work, but this richness is hard to capture with conventional assessment. Only the competence-based spiral model, with its planned progression of competencies, combines depth of integration with strong assessment. Taken together, these patterns suggest that no single model is sufficient. The most promising path is to combine models across a programme, for example by using embedded ethics and integrated STEM units in early years, a competence-based spiral to give structure across the whole degree, and challenge-based or transdisciplinary courses in later years. Systems thinking, taught explicitly, can serve as the thread that ties these parts together. 6. Discussion 6.1 Systems thinking as a shared language, not a separate subject One of the main arguments of this article is that systems thinking is most useful when it is treated as a shared language across subjects rather than as a separate subject in its own right. When systems thinking is taught only in a specialist course, it risks becoming yet another silo. When it is used in every course as a common set of questions about parts, links, feedback and boundaries, it can help students carry ideas from one subject to another. This view fits well with the findings of Redman and Wiek (2021), who placed systems thinking alongside normative, strategic and integration competencies rather than above them. It also fits with the evidence from Huang et al. (2026), where systems thinking improved as a result of weaving ethics and sustainability into a technical course, not as a result of a separate systems course. In other words, systems thinking seems to grow best when it is put to work on real questions that cross boundaries. For students, this has a practical meaning. Whenever they meet a problem in any course, they can ask themselves the four systems questions. What are the parts? How are they linked? What loops or delays might produce surprises? Where is the boundary, and who is outside it? Asking these questions regularly, even in courses that do not require it, is one of the simplest ways for a student to begin breaking down silos in their own learning. 6.2 The assessment challenge The evaluation in Section 5 showed that assessment is a weak point for the most deeply integrated models. This finding matches the wider literature. Dugan et al. (2022) found that very few systems thinking assessments in engineering gave equal weight to technical and contextual aspects of a problem. Norris et al. (2022) showed that the way a scenario is presented shapes how students approach it, which makes it hard to compare results across different tasks. Several responses are possible. One is to assess the process as well as the product. In a challenge-based or transdisciplinary course, teachers can ask students to submit system maps at different stages, along with short reflections explaining how their understanding of the system and its boundary changed. These artefacts show integrated thinking more directly than a final report alone. A second response is to use #rubrics that name integration explicitly, with criteria such as the use of evidence from more than one field, the identification of feedback loops, the recognition of affected groups outside the obvious boundary, and the quality of ethical reasoning at both individual and societal levels, following the distinction drawn by Ferdman and Ratti (2024). A third response is to give students repeated, low-stakes practice with ill-structured problems throughout their studies, as Norris et al. (2022) recommended, so that a single high-stakes assessment does not carry all the weight. It is also worth asking whose judgement counts in assessment. In transdisciplinary courses, community partners and stakeholders may be well placed to judge whether a proposed solution is realistic and fair. Involving them in feedback, even informally, can make assessment more authentic. At the same time, it raises its own ethical questions about fairness and consistency, which Schmitz et al. (2026) identified as one of the moral dimensions of wicked problems in higher education. Assessment design is therefore not only a technical task but also an ethical one. 6.3 Teachers, institutions and the conditions for integration No curriculum model works without teachers who are able and willing to make it work. The research reviewed here repeatedly points to the role of teachers and institutions. Galdames-Calderon et al. (2024) emphasised the need for specialised training for educators adopting challenge-based learning. Han et al. (2022) showed that integrated STEM depends on ongoing teacher collaboration and support. Chamo and Broza (2025) showed that the process of designing an interdisciplinary curriculum is itself formative for new teachers. Lewis and Bader (2026) stressed collaborative development and repeated refinement in building a competence-based programme. These findings suggest that #teacher_collaboration is not a side issue but a central condition for breaking down silos. If teachers from different departments never meet, the curriculum they produce will reflect their separation. Institutions that want integrated learning need to create structures that support joint planning, shared teaching and mutual learning among staff. This might include #co_teaching arrangements, shared course design time, or cross-department teaching teams that stay together over several years. Institutional structures also matter in other ways. Nguyen et al. (2026) found that strict alignment with mandatory learning outcomes may have constrained the development of some systems thinking skills. Similar pressures exist in universities, where accreditation rules, credit systems and departmental budgets can all make integration harder. Curriculum reform that aims to break down silos must therefore look beyond individual courses to the rules and incentives that shape them. In systems thinking terms, the curriculum is itself a system, and changing one part without considering the others is likely to produce disappointing results. 6.4 Artificial intelligence as a test case for integration Artificial intelligence provides a powerful test case for the ideas in this article. AI systems are built on scientific methods such as statistics and data analysis, they are technological products, and they raise urgent ethical questions about #algorithmic_bias, #privacy, accountability and power. They are also changing education itself. Giannakos et al. (2024) described both the promise and the challenges of #generative_AI in education, warning against hasty adoption without careful thought about effectiveness, ethics and teaching value. Dabis and Csaki (2024) found that early university policies on generative AI placed moral and legal responsibility firmly with human beings and encouraged transparency about AI use. Memarian and Doleck (2023) showed that research on fairness, accountability, transparency and ethics in AI and higher education still contains more definitions than studies. Figure 4 shows how a simple systems map can help students study an AI case in an integrated way. The example is a hypothetical hiring algorithm, used here for teaching purposes rather than as a report of real data. Historical hiring data are used to train a screening algorithm, which influences who is shortlisted and hired. If the historical data reflect past bias, the algorithm may reduce workforce diversity and fairness. Over time, the new hires become part of next year's data, creating a reinforcing loop that can deepen the original bias. On the other side of the map, concerns about fairness can affect public trust and regulation, which can lead to bias audits and data correction, which in turn can improve the quality of the data. This balancing pathway shows how ethical and institutional responses can change the behaviour of a technical system. Figure 4. Illustrative systems map of a hiring algorithm, showing how science, technology and ethics interact through feedback loops. Working with such a map helps students in each domain of Figure 1. Science students can examine how data are measured and what statistical patterns mean. Technology students can explore how the algorithm is designed and how it could be changed. Ethics students can ask what fairness means in this context, who is harmed, and who should be accountable, using both the moral and the political dimensions described by Ferdman and Ratti (2024). Most importantly, the map makes visible the feedback loop that none of the groups would see on their own. This is the essence of what systems thinking adds to interdisciplinary learning: it reveals the connections that silos hide. The AI case also shows why #responsible_innovation needs to be taught as an integrated competence. A graduate who understands the mathematics of machine learning but not its social effects, or who understands the ethics of fairness but not how algorithms are built, will struggle to contribute to good decisions about AI. The models evaluated in this article, especially embedded ethics combined with challenge-based learning and an explicit systems thinking thread, offer practical ways of preparing students for this kind of work. 6.5 A design cycle for systems-integrated curriculum Drawing the findings together, this article proposes a six-step design cycle for building learning experiences that integrate science, technology and ethics through systems thinking. The cycle is shown in Figure 5. It can be used by teachers designing a course, by teams designing a programme, or by students designing their own projects. Figure 5. A six-step design cycle for systems-integrated curriculum. The first step is to choose an anchor problem with real stakes. The problem should be open-ended, connected to real people and places, and complex enough to require more than one discipline. Local problems often work well, because students can see and speak with the people affected. Examples include the design of a flood warning system for a coastal town, the introduction of facial recognition in a public space, or the reduction of plastic waste on a university campus. The second step is to map the system. Students identify the main actors, flows of materials, energy, money or information, and feedback loops. A simple diagram like Figure 4 is often enough at first. This step makes systems thinking explicit, which addresses the weakest column in Figure 3. The third step is to assign disciplinary lenses and ethical questions. For each part of the system map, the team asks which scientific knowledge, which technological skills and which ethical questions are relevant. This step protects disciplinary depth while making sure that ethics is woven into the analysis from the start rather than added at the end. Teachers can use the three dimensions proposed by Ferdman and Ratti (2024) to make sure that both individual conduct and wider societal questions are covered. The fourth step is to co-design tasks with teachers and partners. Teachers from different departments plan the tasks together, and, where possible, community or industry partners help to shape the problem and the expected outputs. This step draws on the strengths of challenge-based learning and transdisciplinary courses, and on the evidence that teacher collaboration is a key condition for integration. The fifth step is to assess integration, reasoning and judgement. Assessment should capture not only technical correctness but also the quality of the system map, the use of evidence from several fields, and the depth of ethical reasoning. Process artefacts and reflective writing can supplement final products. The sixth step is to reflect, revise and widen the boundary. After each cycle, students and teachers ask what the system map missed, who was left outside the boundary, and what should change next time. Widening the boundary in this way is both a systems thinking move and an ethical move, because it brings previously ignored groups and effects into view. The cycle then begins again, ideally at a higher level of complexity, in the spirit of a spiral curriculum. 6.6 What students can do now Although curriculum design is usually seen as the job of teachers and institutions, students are not powerless. Several practical steps are open to any student who wants to build integrated problem-solving skills, even within a siloed programme. Students can choose elective courses deliberately from outside their main field, especially courses in ethics, philosophy, social science or design if they study science or engineering, and courses in science or technology if they study the humanities or social sciences. They can join interdisciplinary student projects, competitions or #hackathons, which often function as informal challenge-based learning. They can practise drawing system maps for topics in their own courses and discuss them with classmates from other fields. And they can ask, in any course, the simple question of who is affected by the knowledge or technology being studied and who decides how it is used. Students can also take part in shaping their curriculum. Many of the curriculum projects reviewed here, including the CoDesignS framework (Ahmad et al., 2023) and the Bern programme (Lewis and Bader, 2026), drew on stakeholders in their design or evaluation, and the CoDesignS framework was evaluated with students among its participants. Student feedback, student representation on curriculum committees and student-led initiatives can all push institutions towards more connected learning. In this sense, #student_agency is itself part of the system that produces a curriculum. 7. Conclusion 7.1 Main findings This article set out to evaluate curriculum models that break down academic silos and help students solve complex problems requiring the integration of science, technology and ethics. Using an integrative review of recent research and a conceptual framework that places systems thinking at the centre of integration, it compared seven models against six criteria. Four main findings emerged. First, no single model performs well on every criterion. The models that integrate most deeply and authentically, namely challenge-based learning, competence-based spiral curricula and transdisciplinary studio courses, are also the hardest to assess and to adopt. Second, explicit systems thinking is the weakest element across most models, which suggests that many programmes rely on the hope that students will develop systems thinking by themselves. Third, combining science and technology, as integrated STEM does, is not enough to bring ethics into problem solving. Ethics must be designed in deliberately, through approaches such as socioscientific issues teaching, embedded ethics or transdisciplinary pedagogy. Fourth, assessment remains the central practical challenge, because current tools rarely capture technical and contextual understanding together. 7.2 Implications For curriculum designers, the main implication is that a combination of models, tied together by an explicit thread of systems thinking, is likely to work better than any single model. Early courses can use integrated STEM units and embedded ethics to build connected foundations. A competence-based spiral can give structure across the whole programme. Later courses can use challenge-based or transdisciplinary formats to give students experience with real wicked problems. The design cycle in Figure 5 offers a practical way to plan such learning. For teachers, the main implication is that integration depends on collaboration. Breaking down silos in the curriculum requires breaking down silos among staff. Joint planning, co-teaching and shared assessment rubrics are practical places to start. For institutions, the main implication is that curriculum reform must consider the wider system of rules, credits, accreditation requirements and budgets that shape teaching. Without changes to these structures, even well-designed integrated courses may struggle to survive. For students, the main implication is that they can begin building integrated problem-solving skills now, through elective choices, interdisciplinary projects, the habit of asking systems questions, and active participation in shaping their own education. 7.3 Limitations and future research This article has several limitations. First, it is an interpretive review rather than an empirical study, and the ratings in Figure 3 are reasoned judgements based on the literature, not measured results. Other scholars might weigh the evidence differently. Second, the review focused mainly on recent research published in English, and much of that research comes from Europe, North America and parts of Asia. As Sposab and Rieckmann (2024) noted for sustainability competencies, our picture of good practice may be shaped by a limited range of contexts. Third, the seven models are ideal types. Real programmes often blend features of several models, which makes clean comparisons difficult. Future research could address these limitations in several ways. Comparative studies that test different curriculum models in similar settings, using shared measures, would make it possible to move from interpretive judgement to stronger evidence. New assessment tools that capture technical, contextual and ethical reasoning together are urgently needed. #Longitudinal_studies that follow students across whole programmes, and into their working lives, would show whether integrated learning leads to better decisions in practice. Finally, more research from regions that are currently under-represented, including the #Middle_East, Africa and Latin America, would help to ensure that ideas about interdisciplinary curriculum are tested across a wide range of cultures and education systems. The problems of the coming decades will not wait for universities to reorganise their departments. But curriculum design can begin to change how students think, one course and one programme at a time. By treating systems thinking as a shared language, weaving ethics into technical work, and giving students repeated chances to work on real problems with people from other fields, educators can help prepare graduates who see the whole question and not only their part of it. References Ahmad, N., Toro-Troconis, M., Ibahrine, M., Armour, R., Tait, V., Reedy, K., Malevicius, R., Dale, V., Tasler, N., and Inzolia, Y. (2023). 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Research Papers in Education, 40(5), 742-771. https://doi.org/10.1080/02671522.2025.2493622 #SystemsThinking #interdisciplinary_learning #curriculum_design #breaking_silos #integrated_STEM_education #ethics_in_technology #complex_problem_solving #wicked_problems #higher_education_reform #challenge_based_learning #transdisciplinary_education #responsible_AI #sustainability_competencies #future_skills #STULIB

Universities across the world now speak about decolonising the curriculum, yet there is still little agreement about what this means in daily teaching or how it can be done well. This article offers a critical study of the main mechanisms that universities use to diversify knowledge sources in the humanities and social sciences, with the aim of making curricula more inclusive and more open to scholarship from many cultures. Using a conceptual framework built on epistemic injustice, the coloniality of knowledge and the idea of an ecology of knowledges, the article reviews recent research published mainly between 2021 and 2026. It identifies six mechanisms: reading list audits and citation review, reframing the canon and the history of theory, attention to language and translation, engagement with Indigenous and community knowledge holders, student co-creation, and the redesign of pedagogy and assessment. The analysis shows that these mechanisms work at different depths. Many initiatives remain additive, adding a few new names while leaving the structure of a course unchanged. Deeper change happens when new sources reshape course questions, methods and assessment, and when institutions support this work with time, money and fair rules for publishing and quality. The article also takes seriously the main criticisms of the decolonial agenda, including the risk of tokenism, of performative policy and of treating non-Western societies as passive. It concludes that epistemological pluralism is best understood as a demanding standard of intellectual honesty rather than a slogan, and it proposes a four-level model that students and teachers can use to judge how far a curriculum has really changed. Keywords: epistemological pluralism, decolonising the curriculum, epistemic justice, higher education, humanities, social sciences, inclusive pedagogy, knowledge diversity 1. Introduction Every university course makes choices about knowledge. Someone decides which books go on the reading list, which thinkers are called founders, which methods count as proper research and which questions are worth an essay. These choices are often presented as neutral, as if they simply follow the natural shape of a discipline. In practice they reflect history. Many of the disciplines taught today in the humanities and social sciences took their modern form in Europe and North America during the age of empire, and the stories they tell about themselves still carry traces of that period. A first-year sociology student, for example, may meet a set of founding fathers who were all European men writing in the nineteenth century, while learning very little about how those same thinkers wrote at a time when much of the world was under colonial rule (Bhambra and Holmwood, 2021; Meghji, 2021). Since the student protests at South African universities in 2015 and 2016, and again after the global protests for #racial_justice in 2020, the call to #decolonise_the_curriculum has spread from a few campuses to many universities in Africa, Europe, the Americas, Asia and the Middle East (Abu Moghli and Kadiwal, 2021; Shahjahan et al., 2022). The phrase has become common in strategy documents, teaching awards and library guides. Yet its popularity has created a new problem. As the term travels, it is used to describe very different things, from adding one non-Western author to a reading list to rebuilding the whole logic of a programme (Moosavi, 2023; Phiri et al., 2023). Some critics fear that it has become a fashionable label that changes little. Others argue that the language of decolonisation can itself misrepresent the people it claims to support (Taiwo, 2022). This article approaches the debate through the idea of #epistemological_pluralism. In simple terms, epistemology is the study of knowledge: what counts as knowing, how we justify claims and whose experience is treated as evidence. Epistemological pluralism is the view that there is more than one valid way of producing knowledge, and that a good curriculum should bring different knowledge traditions into real conversation rather than treating one tradition as the measure of all others. This is not the same as saying that anything goes. Pluralism asks for more careful judgement, not less, because it requires students and teachers to understand the reasons and standards that other traditions use. The central question of the article is practical. What mechanisms do universities actually use to diversify the knowledge sources in their curricula, especially in the #humanities_and_social_sciences, and how far do these mechanisms succeed in creating #inclusive_perspectives that take scholarly contributions from different cultures seriously? To answer it, the article does three things. First, it reviews recent research on decolonising the curriculum. Second, it builds a conceptual framework that links epistemic injustice, the coloniality of knowledge and the ecology of knowledges to concrete teaching choices. Third, it analyses six mechanisms of diversification and assesses their strengths, limits and risks. The argument can be stated briefly. Most mechanisms in use today are helpful but shallow when used alone. Diversifying a reading list matters, yet it does not by itself change what a course asks students to do with new sources. Real epistemological pluralism appears when new sources change the questions, the methods and the forms of assessment, and when the institution changes the conditions under which teachers work. The article therefore proposes a four-level model of curriculum change, moving from additive to integrative, transformative and finally structural change. The aim is to give students a clear and honest tool for judging the courses they take and, in time, the courses they may design. The article is written for university students, particularly those studying education, sociology, history, literature, politics, philosophy and related fields. It uses plain language, but it follows the structure of a research article so that readers can see how the argument is built and how the evidence supports it. 2. Literature Review 2.1 From protest to policy The modern wave of curriculum decolonisation is often traced to #student_movements in South Africa, where students argued that universities had changed their admissions after apartheid but not their ideas. They asked why so much of what they studied came from Europe and so little from Africa, and why #African_languages had little place in academic life. Research since then has followed these demands into many settings. Adefila et al. (2022), a team based in Africa, Europe and Latin America, examined published voices in the decolonisation debate using bibliometric analysis. They showed that the debate is shaped by questions of who speaks and from where, and that the voices of historically oppressed groups do not always lead the conversation that is meant to serve them. In England, Shain et al. (2021) studied how universities responded to calls for decolonising. They found a wide range of responses, from silence to what they called strategic advancement, where decolonising is folded into existing plans for equality, reputation and student satisfaction. Their work suggests that the same word can hide very different levels of commitment. Hall et al. (2021) studied an institution-wide project at one English university and argued that formal structures, such as accreditation, risk management and data reporting, tend to absorb and slow down radical energy. In their view, real progress depends on authentic relationships and not only on formal procedures. Abu Moghli and Kadiwal (2021) wrote about what they called the surge of interest after 2020. They warned that rapid enthusiasm can produce quick, visible changes that do not last, and they called for careful attention to how decolonising is conceptualised, to the positionality of those who lead it and to the way the work is carried out. Their emphasis on conduct is important for students, because it shows that how change is made matters as much as what is changed. 2.2 Mapping what decolonising means A major review by Shahjahan et al. (2022) compared writing on decolonising curriculum and pedagogy across disciplines and across global higher education contexts. They found that the term is used in many different ways and that its meaning depends on place. In settler colonial societies such as Canada, Australia and the United States, decolonising is closely tied to Indigenous land, sovereignty and knowledge. In postcolonial societies in Africa and Asia, it is often tied to questions of language, national identity and the legacy of colonial education systems. In former imperial centres such as Britain, it is often linked to racial inequality among students and staff. This variety is not a weakness in itself, but it means that students should always ask what a particular project means by the word. Hayes, Luckett and Misiaszek (2021), introducing a special issue on decolonising higher education, stressed the gap between theory and practice. They argued that decolonial theory is rich and ambitious, while practice is often constrained by institutional rules and by the training and habits of academics. Morreira et al. (2020) made a similar point about the complexities of decolonising curricula and pedagogy, showing that teachers face difficult choices about how far they can go within existing programmes. 2.3 Knowledge, power and the cognitive empire A second strand of literature focuses on the deeper politics of knowledge. Ndlovu-Gatsheni (2021) described what he calls the cognitive empire, meaning the lasting control that colonial and Western forms of thought hold over how the world is understood, even after political independence. For him, the struggle for #epistemic_freedom is the struggle to think, theorise and interpret the world from one's own location without being forced into borrowed categories. Santos (2018) developed a related idea. He argued that modern Western thought draws an invisible line between knowledge that counts and knowledge that is dismissed as superstition, opinion or tradition. His proposal of an #ecology_of_knowledges asks universities to place different knowledges in dialogue, recognising that each has strengths and limits. These ideas connect to the philosophical debate on #epistemic_injustice. This concept describes a wrong done to someone in their capacity as a knower, for example when their testimony is given less credibility because of prejudice, or when a group lacks the shared concepts needed to make sense of its own experience. Byskov (2021) clarified what makes such cases unjust rather than simply unfortunate, arguing that the concept should be used with care so that it keeps its critical force. For curriculum studies, the relevance is clear. When whole traditions of thought are left out of courses, or appear only as objects to be studied rather than as sources of theory, the curriculum itself can become a site of epistemic injustice. 2.4 Disciplines and their canons Research on specific disciplines shows how strongly the canon shapes teaching. Bhambra and Holmwood (2021) re-read classical social theory and showed that thinkers such as Tocqueville, Marx, Weber and Durkheim developed their ideas in a world structured by empire, yet the standard version of #social_theory often treats colonialism as a side issue. Meghji (2021) offered students an introduction to decolonising sociology, showing how the discipline can be rebuilt by taking seriously scholars from the Global South and from colonised communities, as well as critical thinkers who were long pushed to the margins. Choat, Wolf and O'Neill (2024) compared economics and politics in UK universities. Using an audit of undergraduate courses and a survey of academics, they found that neither discipline had made much progress in decolonising its curricula. Politics had moved further than economics, and politics staff were better informed and less hostile to the idea. The authors linked resistance in economics to the dominant neoclassical paradigm, which presents itself as a universal technical science. Their study is a useful reminder that the humanities and social sciences are not one block, and that each discipline has its own obstacles. Other fields show similar patterns. Radcliffe (2022) traced how geography grew alongside exploration and empire and how it can be reworked. Adebisi (2023) explored decolonisation in legal education, showing how the law school curriculum carries assumptions about whose legal traditions are real law. Phiri, Sajid and Delanerolle (2023) argued that in psychology many proposed changes are surface adjustments, such as adding a few minority authors, organising a one-off guest lecture or using a checklist, and that these do not reach the root of the problem. 2.5 Teachers, students and institutions A growing body of research looks at the people who carry out curriculum change. Papen and Atanasova (2025) interviewed thirty-four academics in arts, humanities and social science disciplines at an English university. They found strong commitment among individual teachers, but also reluctance among some colleagues and an ambiguous institutional response, especially about resources. A key finding was that decolonising rested on the shoulders of individuals, while collective agency was missing. Takhar (2023) reported on a student voice project at a British university, which sought views from students about their experience of the curriculum. The project linked curriculum change to the racial #awarding_gap, the difference in final degree results between groups of students, and it argued that student input is an important step toward inclusive teaching. Arday, Belluigi and Thomas (2021) connected curriculum reform to wider racial inequality in the academy. They argued that inclusive pedagogy and decolonising the curriculum need to address who teaches, who is promoted and whose experience is reflected in teaching, not only what appears in a syllabus. 2.6 Critical voices The literature also contains strong criticism, and a fair study must include it. Moosavi (2023) called for decolonial reflexivity, asking scholars who promote decolonising to examine how their own work might repeat colonial patterns, for example when academics in the Global North become the leading voices of a movement that began in the South. Taiwo (2022) went further, arguing that the language of decolonisation can deny the agency of African thinkers who chose to adopt and adapt ideas such as democracy, constitutionalism and modern science. In his view, labelling such ideas as colonial impositions can be both historically wrong and disrespectful. Gopal (2021), writing from literary studies, argued that decolonising is often misunderstood as removing Western writers. She suggested instead that it means rethinking the hierarchies through which knowledge is ordered, and recovering the long history of #anticolonial thought that already shaped Western intellectual life. 2.7 The gap this article addresses Taken together, the literature is rich in theory and in case studies, but it is less clear about how the different practical mechanisms compare with each other. Many studies examine one initiative in one institution. Fewer step back to ask which mechanisms reach deep into the curriculum and which stay at the surface. Muhr (2026), in a semi-systematic review of literature in English and Portuguese, showed that curriculum decolonising is shaped both by the global geopolitics of knowledge and by the #neoliberal pressures on universities, such as competition, metrics and marketisation. This article builds on that insight by offering a mechanism-focused analysis with a simple model of depth that students can use. 3. Conceptual Framework 3.1 Three linked ideas The framework of this article joins three ideas, shown in Figure 1. The first is epistemic injustice, which helps us see the harm done to individuals and groups when their knowledge is ignored or discounted. The second is the #coloniality_of_knowledge, a term used in decolonial theory to describe how ways of thinking that grew from colonial power continue to shape what is treated as universal and what is treated as local, even after colonial rule has ended (Ndlovu-Gatsheni, 2021). The third is the ecology of knowledges, which offers a positive vision: different knowledges can meet, learn from each other and correct each other without one being reduced to the other (Santos, 2018). These three ideas lead to a single principle, epistemological pluralism, which this article treats as a standard for curriculum design. The principle can be stated as follows. A curriculum in the humanities and social sciences should present more than one knowledge tradition as a genuine source of concepts, methods and evidence, and it should help students understand and evaluate the standards that each tradition uses. Figure 1. Conceptual framework: from three linked ideas to epistemological pluralism as a curriculum principle working through content, pedagogy and structures. 3.2 Three areas of curriculum The principle works through three areas of the curriculum. The first is content: what is read, cited and taught. This is where most diversification efforts begin. The second is pedagogy and assessment: how students learn and how their learning is judged. A course might include diverse readings but still assess students only through a single form of essay that rewards one style of argument. The third is structures: who decides what is taught, who is hired, how teaching time is funded, which journals count in research evaluation and how quality is assured. Change in content without change in structures is likely to be fragile. 3.3 Why pluralism rather than replacement It is important to be clear about what epistemological pluralism does not mean. It does not mean replacing one canon with another, or rejecting all ideas that came from Europe. Such an approach would simply reverse the hierarchy and would fall into the problem that Taiwo (2022) identified, treating cultures as closed boxes rather than as traditions that have always borrowed from and argued with each other. Pluralism also does not mean #relativism, the view that all claims are equally valid. A pluralist curriculum still asks for evidence and argument. The difference is that it recognises more than one way of producing evidence and argument, and it invites students to compare them. In this sense, epistemological pluralism is a demanding standard. It asks teachers to know more, not less. A teacher who wants to bring #Ibn_Khaldun into a course on social theory, or African philosophy into a course on ethics, or Indigenous #oral_history into a course on #research_methods, must understand those traditions well enough to teach them with the same care given to familiar authors. This is one reason why change is slow, and why institutional support matters. 3.4 Depth as the key variable The framework adds one further idea, which is depth. Mechanisms for diversifying knowledge can work at different depths. Some change only the surface of a course. Others change its questions and methods. Others change the conditions under which the course is designed and taught. This idea of depth is developed in Section 5 and summarised in Figure 3. It allows the analysis to move beyond simple praise or blame and to ask a more useful question: how far does a given mechanism go, and what would it take to go further? 4. Methodology This study is a critical conceptual review rather than an empirical survey. It draws on peer-reviewed articles and scholarly books published mainly between 2021 and 2026, with a small number of earlier works where they provide key concepts. Sources were selected for their relevance to three questions: what mechanisms are used to diversify knowledge sources in higher education curricula, what evidence exists about their effects, and what criticisms have been made of them. Priority was given to studies from the humanities and social sciences, but some studies from medicine, psychology and public health were included where they offered transferable methods, such as the computational audit of reading lists. The analysis followed three steps. First, mechanisms described in the literature were collected and grouped into families. Second, each family was examined for its aims, practices, reported outcomes and limitations. Third, the families were compared using the conceptual framework, with special attention to the depth of change each could achieve. The aim was not to measure effects statistically, since the available evidence does not allow that, but to build a clear and honest picture that can guide students and teachers. This approach has limits. The literature is uneven, with much more writing from the United Kingdom and South Africa than from other regions, and with fewer studies from the Arab world, Latin America and East Asia written in English. Studies published in other languages are underrepresented, which is itself an example of the problem this article discusses. These limits are returned to in the conclusion. 5. Analysis: Six Mechanisms for Diversifying Knowledge Sources The literature reviewed above points to six main mechanisms that universities use to diversify knowledge in their curricula. They are shown in Figure 2. Each mechanism is discussed in turn, with attention to what it does well and where it falls short. Figure 2. Six mechanisms for diversifying knowledge sources in humanities and social science curricula. 5.1 Mechanism one: reading list audits and citation review The most common first step is to look closely at the #reading_list. Teachers, librarians and students examine who wrote the sources, where those authors are based, which languages the sources were first written in and which perspectives are missing. Some universities have created toolkits to guide this process. At the University of Leicester, for example, library staff and student volunteers worked together between 2020 and 2022 to build a toolkit with prompts that help academic staff assess and diversify their module reading lists across race, gender and socio-economic background (Karavadra, Kelalech and Nockels, 2023). Audits can also use data. Price et al. (2022) developed a computational method to examine the geographic affiliation of authors on reading lists, applying it to 568 articles representing 3,166 authors on a master's programme in public health. They found only a marginal shift away from authors affiliated with institutions in the Global North between two time periods. Importantly, the authors were careful to explain both the value and the limits of such numbers. Data can show patterns quickly and at scale, but it cannot tell us whether a source is used well or simply listed. A related practice is the #citation_diversity_statement, a short paragraph in which authors report on the balance of the references they cite. Zurn, Bassett and Rust (2020) proposed this practice to make citation imbalance visible and to encourage authors to reflect on whose work they build upon. In teaching, a similar idea can be used by asking students to reflect on the sources they use in their own essays. The strength of this mechanism is that it is concrete and measurable. It gives teachers a clear starting point and makes the problem visible. Its weakness is that it can easily stop at counting. A reading list with more diverse authors may still present those authors as optional extras, placed at the end of the list under a heading such as further perspectives, while the core of the course stays the same. Phiri et al. (2023) warned that adding minority authors without changing the framing of a course is a surface change. There is also a risk that audits reduce people to categories, as if an author's identity guaranteed a particular viewpoint. A good audit therefore asks not only who is on the list but how each source is used. 5.2 Mechanism two: reframing the canon and the history of theory The second mechanism goes deeper by changing the story a discipline tells about itself. Instead of adding new authors to an old narrative, teachers rewrite the narrative. In sociology, this may mean teaching the founding period of the discipline alongside the history of empire, and including thinkers such as W. E. B. Du Bois, whose work on race and modernity was long excluded from the standard #canon (Bhambra and Holmwood, 2021; Meghji, 2021). It may also mean introducing the fourteenth century North African scholar Ibn Khaldun, whose analysis of social cohesion and the rise and fall of dynasties is now widely recognised as an early contribution to social science. In politics, it may mean reading anticolonial political thought as political theory in its own right rather than as a case study. In literature, it may mean studying how the European novel developed in relation to colonial trade and travel, and placing it beside writing from Africa, the Arab world, South Asia and the Caribbean. Gopal (2021) argued that this kind of work is not about deleting Western writers but about understanding them better. When students learn that ideas often travelled across continents and were shaped by encounters, conflicts and exchanges, they gain a more accurate picture of #intellectual_history. In this sense, #canon_reform is also a matter of historical accuracy. A history of political thought that leaves out the Haitian Revolution, the Indian independence movement or the debates of Arab reformers in the nineteenth century is not neutral. It is incomplete. The strength of this mechanism is that it changes the frame within which all sources are read. It can make a course more honest and more interesting. Its main difficulty is that it demands expertise. Teachers trained in one tradition may not feel confident teaching another, and there is a danger of thin or inaccurate teaching if new material is added without proper preparation. Moosavi (2023) also warned that scholars can unintentionally create a new canon of decolonial authors, mostly based in elite Northern institutions, which then becomes as fixed as the old one. Reframing the canon therefore needs to stay open and self-critical. 5.3 Mechanism three: language, translation and multilingual sources The third mechanism concerns language. English now dominates academic publishing, and this dominance shapes which knowledge is visible. Amano et al. (2023) surveyed environmental scientists in several countries and found that researchers whose first language is not English spend considerably more time reading and writing papers, face more rejections linked to their writing and may avoid international conferences because of language barriers. Although their study concerns the natural sciences, the same pressures apply strongly in the humanities and social sciences, where meaning depends closely on language. For curricula, #linguistic_diversity matters in several ways. Students may never meet important work that was written in Arabic, Swahili, Portuguese, Hindi, Mandarin or Spanish if only English sources are listed. Concepts that do not translate easily may be lost or flattened. In South Africa, Msani and Nsele (2025) reviewed research on African languages in curriculum transformation and found that many studies of decolonisation give little attention to language, even though language is one of the strongest pillars of the colonial legacy in universities. They argued that African languages should be placed at the centre of curriculum change, alongside dominant languages. Makokotlela and Gumbo (2025), studying an environmental education module at the University of South Africa, found that Indigenous knowledge was introduced partly through a glossary of terms translated into African languages, though its integration was uneven across the module. Practical steps under this mechanism include listing translated works, encouraging students who read other languages to bring sources into seminars, teaching key concepts in their original language with careful explanation, and supporting translation projects. For students in multilingual regions, such as the Gulf, North Africa or South Asia, this mechanism can be especially powerful, because it allows them to draw on scholarship in their own languages as legitimate academic material. The limits of this mechanism are practical. Translation is expensive and slow, and many teachers cannot read the languages involved. There is also a risk of treating translated texts as windows onto a culture rather than as arguments in their own right. Yet even small changes, such as acknowledging the language of origin of a text and discussing what may be lost in translation, can help students see that English is one language of scholarship among many, not the language of knowledge itself. 5.4 Mechanism four: Indigenous and community knowledge holders The fourth mechanism brings knowledge from outside the university into the curriculum. This includes #Indigenous_knowledge, community histories, oral traditions, religious and philosophical traditions and the practical wisdom of social movements. Smith (2021), in the third edition of her influential work on decolonizing methodologies, showed how research has often treated #Indigenous_peoples as objects of study, and she set out principles for research that respects their knowledge, protocols and self-determination. Thambinathan and Kinsella (2021) translated similar ideas into qualitative research, highlighting practices such as critical reflexivity, reciprocity, respect for self-determination and openness to ways of knowing that have been marginalised. In teaching, this mechanism may involve inviting knowledge holders to co-teach, designing field visits with #community_partners, using oral history projects or including community-produced texts and archives. Makokotlela and Gumbo (2025) showed that integrating Indigenous knowledge into a university module can make learning more relevant to Indigenous students while exposing all students to other forms of knowledge. In medical education, Wong, Gishen and Lokugamage (2021) described how students, staff and members of the public worked together as agents of change at one London medical school, using epistemic pluralism as one of three guiding concepts. Although medicine lies outside the humanities and social sciences, their approach is directly relevant, because it shows how a field that sees itself as universal can open space for other perspectives. This mechanism is often the most transformative, because it challenges the idea that universities are the only places where serious knowledge is made. It also carries the highest ethical risks. Community knowledge can be extracted, simplified or used without proper credit or payment. Knowledge holders may be invited for a single session and then forgotten, which repeats the pattern of using people as resources. For these reasons, the literature stresses relationships, reciprocity and long-term partnership. A course that brings in community knowledge should also give something back, whether through fair payment, shared authorship or support for community goals. 5.5 Mechanism five: student co-creation and partnership The fifth mechanism treats students as partners in curriculum design. Students often notice gaps that teachers miss, especially students from communities whose histories and ideas are absent from the curriculum. #Student_partnership can take many forms: student audits of reading lists, curriculum advisory roles, co-designed modules, student-led events and jointly produced online resources. Takhar (2023) argued that the student voice is an integral part of decolonising, and that critical conversations and co-produced resources can help a university move toward more inclusive teaching. The Leicester reading list toolkit described earlier was also developed with student volunteers (Karavadra, Kelalech and Nockels, 2023). Partnership has clear benefits. It builds a sense of #belonging, it brings new knowledge into courses and it gives students practical experience of shaping their own education. It can also connect curriculum change to wider concerns about fairness, including the awarding gap discussed by Takhar (2023). There are, however, important cautions. Students from minority backgrounds may be asked to carry the burden of fixing a problem they did not create, often without pay or recognition. This form of hidden labour can be exhausting and can reproduce the very inequality it aims to address. Takhar (2023) was careful to note that her project did not ask minority ethnic students to do the work of decolonising for the university, but sought input from students about their experience. Good partnership therefore requires fair recognition, clear roles and a real ability for students to influence decisions. Without these, co-creation can become a form of consultation that changes little. 5.6 Mechanism six: redesigning pedagogy and assessment The sixth mechanism changes how students learn and how their learning is judged. A curriculum may include sources from many traditions, but if every assignment demands the same kind of argument, in the same structure, citing the same type of evidence, then the course still privileges one way of knowing. #Assessment_design is therefore central to epistemological pluralism. Practical examples include assignments that ask students to compare how two traditions approach the same question, reflective tasks in which students examine their own position as knowers, projects that allow oral presentation or community-based work alongside written essays, and tasks that require students to explain a concept from another tradition on its own terms before evaluating it. Wong, Gishen and Lokugamage (2021) linked epistemic pluralism to #critical_consciousness, the ability to recognise how power shapes knowledge and practice. Assessment that builds this capacity helps students carry pluralist habits of mind beyond a single course. Arday, Belluigi and Thomas (2021) placed pedagogy at the heart of inclusive change, arguing that curriculum reform must include the relationships and practices of the classroom. A seminar where only some students feel able to speak, or where certain accents and experiences are quietly treated as less authoritative, can undermine even the most diverse reading list. #Inclusive_pedagogy therefore includes classroom dialogue, the treatment of disagreement and the ways teachers respond to knowledge that students bring from their own communities. The challenge of this mechanism is that assessment is often tightly controlled by regulations, external examiners and accreditation bodies. Changing it can take years. There is also a concern about standards. Some teachers fear that new forms of assessment will be seen as less rigorous. This concern deserves a serious answer. Pluralist assessment is not easier. Asking a student to explain an unfamiliar tradition accurately and then compare it fairly with a familiar one is a demanding intellectual task that tests understanding at a high level. 5.7 Comparing the mechanisms: the question of depth When the six mechanisms are compared, a clear pattern appears. They can all be carried out at different depths. A reading list audit can be a simple count, or it can lead to a complete rethinking of a course. Community knowledge can appear in a single guest lecture, or it can shape the design of a whole programme in partnership with community members. Student partnership can be a survey, or it can give students real decision-making power. To capture this pattern, the article proposes a four-level model of curriculum change, shown in Figure 3. It is offered as a tool for reflection, not as a fixed ladder that every institution must climb in order. Figure 3. A four-level model of curriculum change, from additive inclusion to structural transformation. Level one is additive change. New authors and topics are added, but the questions, framing and assessment of the course remain the same. This level is common and easy to report in institutional documents. It has some value, because it introduces students to names they might otherwise never meet, but it often places new knowledge at the margins. #Tokenism is the main risk at this level. Level two is integrative change. New sources are woven into the core topics of the course and are studied in depth, not only listed. Students read them as sources of theory and evidence, and they are examined in essays and exams in the same way as familiar sources. This level requires more preparation from teachers but produces a more honest curriculum. Level three is transformative change. Here the encounter with different traditions changes the course itself. The central questions are rethought, methods from different traditions are taught and compared, and assessment is redesigned to reward pluralist thinking. Students are not only told that there are many #knowledge_traditions. They learn to work with them. Level four is structural change. At this level, the institution changes the conditions that shape all courses. It funds time for curriculum redesign, hires staff with expertise in a wider range of traditions, supports multilingual scholarship, revises #quality_assurance rules and rewards research published in a wider range of journals and languages. Without this level, changes at the first three levels depend on the energy of individuals and are easily lost when those individuals move on, a pattern that Papen and Atanasova (2025) observed clearly. The model helps to explain why so many initiatives feel disappointing. Many universities report activity at level one and call it decolonisation. Critics are right to say that this is not enough. But the model also shows that level one can be a starting point if it leads somewhere. The key question for any initiative is not only what has been done, but whether it is designed to move deeper. 5.8 Barriers and enablers across levels The literature identifies barriers and enablers at four levels: the individual teacher, the discipline, the institution and the global system. These are summarised in Figure 4. Figure 4. Main barriers and enablers of curriculum diversification at four levels. At the level of the individual teacher, the most common barriers are lack of time, lack of training and a fear of getting things wrong. Teachers may worry about teaching traditions they do not know well, or about being accused of political bias. Papen and Atanasova (2025) found that committed teachers often felt alone. Enablers at this level include reflexive practice, peer support and a habit of making small, steady changes to each module over time rather than attempting a complete redesign at once. At the level of the discipline, the main barrier is the strength of existing canons and paradigms. Choat, Wolf and O'Neill (2024) showed how the neoclassical paradigm in economics shapes what counts as rigorous work and makes decolonising appear irrelevant to many economists. Similar dynamics can operate in philosophy, where certain traditions are treated as the core of the subject, or in history, where national narratives dominate. Enablers at this level include disciplinary histories that show where ideas actually came from, shared reading banks built by networks of teachers, and professional associations that support curriculum change. At the level of the institution, barriers include box-ticking, short-term projects and the use of unpaid student and staff labour. Hall et al. (2021) showed how institutional procedures can absorb the energy of change. Shain et al. (2021) showed how decolonising can be turned into a strategic slogan. Enablers include funded roles, workload allowance for curriculum redesign and long-term strategies with honest review. At the level of the global system, the barriers are the most difficult to change. They include the dominance of English, the rules of journal indexing and #university_rankings, and unequal funding between regions. Heleta and Mzileni (2024) showed that the lists of accredited journals used by South Africa's Department of Higher Education and Training are dominated by journals from Europe and North America, a pattern they called #bibliometric_coloniality. Such rules shape what academics publish, what they cite and, in turn, what they teach. Enablers at this level include multilingual publishing, fairer journal lists and partnerships between universities in the #Global_South. The value of Figure 4 is that it shows why individual effort alone cannot achieve deep change. A committed teacher can move a single course to level two or three. Only coordinated action across the discipline, the institution and the wider system can sustain change at level four. 5.9 Responding to the critics A critical study must take criticism seriously. Three objections are especially important. The first objection is that decolonising is often performative. Universities publish statements and run events, but little changes in teaching or in the experience of students. The evidence reviewed here supports this concern. Shain et al. (2021), Hall et al. (2021) and Choat, Wolf and O'Neill (2024) all document gaps between language and practice. The four-level model offers a way to respond. Rather than abandoning the agenda, students and teachers can ask precise questions about the depth of change and hold institutions to account for moving beyond level one. The second objection, made strongly by Taiwo (2022), is that the language of decolonisation can deny the agency of people in formerly colonised societies by treating their adoption of modern ideas as mere imposition. This is a serious point, and it is one reason why this article prefers the language of epistemological pluralism. Pluralism does not require us to sort ideas into colonial and non-colonial boxes. It asks us to recognise that knowledge traditions have always interacted, that thinkers in Africa, Asia, the Arab world and Latin America have made original contributions to global debates, and that these contributions deserve a full place in the curriculum as part of a shared human conversation. The third objection is that pluralism will weaken academic standards. This concern often rests on a misunderstanding. Pluralism, as defined in this article, is not relativism. It does not claim that all views are equally true. It claims that good judgement requires knowledge of more than one tradition. In fact, a curriculum that presents only one tradition as universal may be less rigorous, because it hides its own assumptions from examination. When students compare different ways of knowing, they become more aware of the strengths and limits of each, including the tradition they started with. This is a gain in #critical_thinking, not a loss. Moosavi (2023) adds a fourth, more reflective caution. Those who promote decolonising should examine their own position and practices. Decolonial reflexivity asks whether the movement itself has created new hierarchies, for example by giving most attention to scholars in elite Northern institutions or by turning a complex set of ideas into a simple brand. This caution applies to the present article as well. A study that relies mainly on English-language sources, as this one does, can only partly escape the patterns it describes. 5.10 Implications for students Although much of the literature addresses teachers and institutions, students have an important role. First, students can read their own reading lists critically, asking whose voices are present and how they are used. Second, students can seek out sources beyond the list, including work in other languages they read. Third, students can practise pluralist habits in their own essays by comparing traditions fairly and by explaining unfamiliar ideas on their own terms before judging them. Fourth, students can take part in partnership schemes, while insisting that their work is recognised and that their input leads to real change. Students can also use the four-level model to evaluate their courses. A simple set of questions can help. Are diverse sources only added, or are they studied in depth? Do they change the questions the course asks? Does assessment reward the ability to work across traditions? Does the department show long-term commitment, for example through staffing and resources? These questions turn a broad debate into practical judgement, and they help students become active participants in shaping #knowledge_diversity in their universities. 5.11 A note on regional contexts The literature reviewed here is strongest on the United Kingdom and South Africa, but the questions it raises apply widely. In many universities in the Middle East, for example, curricula in the humanities and social sciences draw heavily on English-language textbooks produced elsewhere, while rich traditions of #Arabic and Islamic scholarship in history, philosophy, law and social thought are taught mainly in separate departments or not at all. The six mechanisms described in this article can be adapted to such settings. Reading list audits can ask how much Arabic scholarship appears in social science courses. Canon reform can place classical and modern Arab thinkers in dialogue with European theory. Language policies can support bilingual reading. The same caution applies, however. Pluralism means dialogue, not replacement, and it requires the same critical standards for every tradition, including one's own. Similar observations could be made about Latin America, where Santos (2018) and many others have developed #epistemologies_of_the_South, or about South and East Asia, where long scholarly traditions sit alongside imported disciplinary models. The value of epistemological pluralism as a principle is that it can travel across these contexts without pretending that they are all the same. Each context must decide which traditions have been marginalised and how they can be brought into fair conversation. 5.12 An illustrative example: rethinking an introductory social theory module To show how the mechanisms and the four levels fit together, it helps to consider an illustrative example. The example below is not a report of a real course. It is a teaching scenario built from the practices described in the literature, offered so that students can see what each level might look like in a familiar setting. Imagine a first-year module called Introduction to Social Theory. In its original form, the module follows a familiar pattern. The first weeks cover Marx, Weber and Durkheim, followed by later European and North American theorists. The reading list contains almost no authors from outside Europe and North America. Assessment is a single essay asking students to compare two of the classical thinkers. At level one, the teacher adds a week on postcolonial theory near the end of the term and places several non-Western authors under a heading of additional readings. Students who are interested may read them, but the essay question does not change, and most students never engage with the new material. An audit would show a more diverse list, yet the learning experience is almost the same. At level two, the teacher moves further. Following the argument of Bhambra and Holmwood (2021), the week on Weber now asks how his comparisons between civilisations were shaped by the colonial world of his time. Du Bois is studied as a classical theorist of modernity, not as a footnote. Ibn Khaldun appears in the first week as an early thinker on social cohesion, so that students learn from the start that the study of society did not begin in nineteenth century Europe. The essay question is revised so that students may compare any two theorists from the full list. At level three, the module is rebuilt around questions rather than names. Instead of a sequence of founding fathers, each week asks a problem, such as what holds societies together, how inequality is produced or how modernity should be understood. For each question, students read sources from at least two traditions and compare how they define the problem, what evidence they use and what they leave out. A seminar task asks students to explain a concept from an unfamiliar tradition in its own terms before evaluating it. Assessment includes a short piece of #reflective_writing in which students consider how their own background shapes the theories they find convincing. Students who read other languages are invited to bring a source into class, and the teacher discusses with the group what may be lost or gained in #translation. At level four, the department supports this work over time. It gives the teacher workload hours for redesign, funds a library budget for translated works, builds a shared reading bank with colleagues in partner universities in Africa and Asia, and revises its programme review so that external examiners are asked to comment on the range of traditions taught. When the original teacher leaves, the redesigned module survives because it is supported by the department and not only by one person. This example shows that the same subject can be taught in very different ways, and that the move from level one to level four is not only about adding more names. It is about changing the questions, the methods, the assessment and the support that surrounds teaching. It also shows that pluralism does not require removing Marx, Weber or Durkheim. These thinkers remain, but they are read more accurately, as participants in a wider and more connected history of ideas. For students, this offers a richer education and a better preparation for working in diverse societies and global institutions. 5.13 Measuring progress without reducing it to numbers A final practical issue concerns evaluation. Universities like to measure change, and numbers are useful. Reading list audits, such as those described by Price et al. (2022), can show whether the proportion of authors from different regions has shifted over time. Surveys of staff and students can show changes in awareness and attitudes. Data on student outcomes, including the awarding gap, can show whether curriculum change is linked to fairer results. Yet the framework of this article suggests that numbers should be used with care. A curriculum that moves from ten to twenty per cent of non-Western authors may still be additive if those authors are not studied in depth. A curriculum with fewer new authors may be more transformative if each is used to rethink the questions of the course. For this reason, quantitative measures should be combined with qualitative evidence, such as analysis of course aims and assessment tasks, interviews with students and teachers, and examples of student work that shows pluralist thinking. A useful approach is to treat the four levels as a rubric. For each module, reviewers can ask which level best describes current practice, what evidence supports that judgement and what the next step would be. This approach keeps attention on depth rather than on headline figures, and it gives teachers a clear path forward. It also allows students to participate in evaluation, since the questions are simple enough to discuss in a seminar or a student panel. 6. Discussion The analysis suggests several broader lessons. The first is that the debate on decolonising the curriculum is often framed as a choice between doing too little and doing too much. Supporters fear that institutions will offer only symbolic change, while critics fear that the movement will politicise teaching or reject valuable ideas. The framework developed here suggests a different way of thinking. The real question is not whether to change the curriculum but how deep and how honest that change should be. Epistemological pluralism offers a standard that both supporters and many critics can accept, because it rests on the academic values of accuracy, evidence and open debate. The second lesson is that mechanisms work best in combination. A reading list audit is most useful when it leads to canon reform. Canon reform is most effective when it is supported by changes in assessment. Community partnerships are most ethical when backed by institutional structures that ensure fairness. Student partnership is most meaningful when students can influence decisions. In other words, the six mechanisms are not alternatives. They are parts of a system, and their value increases when they are connected. The third lesson concerns sustainability. The research consistently shows that change that relies on committed individuals is fragile (Papen and Atanasova, 2025; Hall et al., 2021). This is perhaps the most important finding for university leaders. If institutions want curriculum diversification to last, they must invest in it as they would in any other core academic activity, with time, money, expertise and evaluation. The fourth lesson concerns the global system of knowledge. Many of the deepest barriers lie beyond any single university, in the rules of publishing, indexing, ranking and funding. Heleta and Mzileni (2024) and Amano et al. (2023) show, in different ways, how these rules shape whose knowledge becomes visible. Curriculum reform is therefore connected to wider reforms in #academic_publishing and research evaluation. Students who understand these links are better prepared to see the curriculum not as a fixed object but as the result of many decisions that can be questioned and changed. The fifth lesson is about humility. Every curriculum is partial. No course can include all traditions, and every selection leaves something out. Epistemological pluralism does not promise a perfect curriculum. It promises a curriculum that is honest about its choices, open about its limits and willing to learn. This is perhaps its most valuable contribution to #higher_education. 7. Conclusion This article set out to examine, in a critical and practical way, the mechanisms that universities use to diversify knowledge sources in the humanities and social sciences. Drawing on recent research, it identified six mechanisms: reading list audits and citation review, reframing the canon, attention to language and translation, engagement with Indigenous and community knowledge holders, student co-creation and the redesign of pedagogy and assessment. It argued that each mechanism has real value but that all can be carried out at different depths, and it proposed a four-level model that distinguishes additive, integrative, transformative and structural change. The main findings can be summarised in four points. First, most current initiatives remain at the additive level, which explains much of the disappointment expressed in the literature. Second, deeper change happens when new knowledge sources reshape the questions, methods and assessment of a course. Third, lasting change depends on institutional and systemic support, without which committed individuals are left to carry the work alone. Fourth, the strongest criticisms of the decolonial agenda, including concerns about tokenism, performative policy and the denial of agency, can be addressed by framing the goal as epistemological pluralism, a principle grounded in accuracy, dialogue and fair judgement rather than in the simple replacement of one canon with another. The study has limits. It is a conceptual review rather than an empirical study, and it relies mainly on English-language sources from a small number of regions. Future research could compare mechanisms across countries, follow curriculum changes over several years, examine student learning outcomes in pluralist courses and pay closer attention to universities in the Arab world, Latin America and Asia, including studies published in languages other than English. Such research would itself be an act of epistemological pluralism. For students, the message is both critical and hopeful. The curriculum is not a natural fact. It is a human creation, shaped by history and open to change. By asking whose knowledge counts, how it is taught and why, students can take part in building a university where scholarship from many cultures meets on fair terms. That is the promise of #curriculum_reform guided by pluralism, and it is a promise worth holding universities to. References Abu Moghli, M., and Kadiwal, L. (2021). Decolonising the curriculum beyond the surge: Conceptualisation, positionality and conduct. London Review of Education, 19(1), Article 23. https://doi.org/10.14324/LRE.19.1.23 Adebisi, F. (2023). Decolonisation and legal knowledge: Reflections on power and possibility. Bristol University Press. Adefila, A., Teixeira, R. V., Morini, L., Garcia, M. L. T., Delboni, T. M. Z. G. F., Spolander, G., and Khalil-Babatunde, M. (2022). Higher education decolonisation: Whose voices and their geographical locations? 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University teachers as agents in curriculum innovation: Experiences of decolonising curricula. Innovations in Education and Teaching International, 63(5), 1281-1294. https://doi.org/10.1080/14703297.2025.2558222 Phiri, P., Sajid, S., and Delanerolle, G. (2023). Decolonising the psychology curriculum: A perspective. Frontiers in Psychology, 14, Article 1193241. https://doi.org/10.3389/fpsyg.2023.1193241 Price, R., Skopec, M., Mackenzie, S., Nijhoff, C., Harrison, R., Seabrook, G., and Harris, M. (2022). A novel data solution to inform curriculum decolonisation: The case of the Imperial College London Masters of Public Health. Scientometrics, 127(2), 1021-1037. https://doi.org/10.1007/s11192-021-04231-3 Radcliffe, S. A. (2022). Decolonizing geography: An introduction. Polity Press. Santos, B. de S. (2018). The end of the cognitive empire: The coming of age of epistemologies of the South. Duke University Press. Shahjahan, R. A., Estera, A. L., Surla, K. L., and Edwards, K. T. (2022). Decolonizing curriculum and pedagogy: A comparative review across disciplines and global higher education contexts. Review of Educational Research, 92(1), 73-113. https://doi.org/10.3102/00346543211042423 Shain, F., Yildiz, U. K., Poku, V., and Gokay, B. (2021). From silence to strategic advancement: Institutional responses to decolonising in higher education in England. Teaching in Higher Education, 26(7-8), 920-936. https://doi.org/10.1080/13562517.2021.1976749 Smith, L. T. (2021). Decolonizing methodologies: Research and Indigenous peoples (3rd ed.). Bloomsbury Academic. Taiwo, O. (2022). Against decolonisation: Taking African agency seriously. Hurst Publishers. Takhar, S. (2023). The student voice: Decolonising the curriculum. Equity in Education and Society, 3(2), 114-129. https://doi.org/10.1177/27526461231192671 Thambinathan, V., and Kinsella, E. A. (2021). Decolonizing methodologies in qualitative research: Creating spaces for transformative praxis. International Journal of Qualitative Methods, 20. https://doi.org/10.1177/16094069211014766 Wong, S. H. M., Gishen, F., and Lokugamage, A. U. (2021). Decolonising the medical curriculum: Humanising medicine through epistemic pluralism, cultural safety and critical consciousness. London Review of Education, 19(1), Article 16. https://doi.org/10.14324/LRE.19.1.16 Zurn, P., Bassett, D. S., and Rust, N. C. (2020). The citation diversity statement: A practice of transparency, a way of life. Trends in Cognitive Sciences, 24(9), 669-672. https://doi.org/10.1016/j.tics.2020.06.009 #EpistemologicalPluralism #decolonising_education #DecoloniseTheCurriculum #epistemic_justice #knowledge_justice #inclusive_curriculum #Global_South_scholarship #diverse_reading_lists #humanities #social_sciences #pluriversal_university #multilingual_scholarship #student_voice #STULIB #university_teaching

Most school systems, and a growing number of universities, give teachers a curriculum that has already been written somewhere else. National standards, approved textbooks, pacing guides, common examinations and learning-outcome templates set out what should be taught and, increasingly, how and when. Yet every classroom holds students who differ in language, prior knowledge, culture, ability, interest and life circumstance. This article asks how much room teachers and faculty members actually have to modify and adapt centralized, standardized curricula so that these different students can learn, and what conditions turn that room into real practice. It is written as an integrative review of recent peer-reviewed research, most of it published between 2021 and 2025, read through two lenses: the ecological model of teacher agency and the idea of curriculum making as activity spread across several sites, from international bodies down to the classroom. The review finds that formal autonomy and achieved agency are not the same thing. Teachers often have more legal freedom than they use, and sometimes use more freedom than policy formally grants. Accountability pressure, scripted materials, weak professional trust and thin preparation narrow adaptation, while collegial design work, supportive leadership and flexible frameworks widen it. The article proposes a five-level continuum of curricular autonomy and a practical co-design cycle, and it argues that the goal is not maximum freedom but supported, shared and accountable adaptation that serves every learner. Keywords: teacher agency, curriculum making, curriculum co-design, standardized curriculum, teacher autonomy, learner diversity, accountability, higher education 1. Introduction Imagine two teachers in the same country, teaching the same grade, using the same national curriculum document. One works in a school where every lesson arrives as a ready-made slide deck with a timed script, and where a district test every six weeks checks whether the class has kept pace. The other works in a school where the department meets each week to look at student work, decide what to reteach, and redesign the next unit around the needs that have appeared. On paper, both teachers follow the same #standardized_curriculum. In practice, the students in these two rooms will experience very different things. This gap between the curriculum on paper and the curriculum that students actually meet is the subject of this article. The question matters because classrooms are more varied than ever. Students arrive with different home languages, different levels of readiness, different disabilities and talents, and different cultural reference points, and #equity depends on how teachers respond to these differences. A curriculum written for an imagined average student rarely fits any real group perfectly. Someone has to make the fit, and in most cases that someone is the teacher. The capacity of teachers to act purposefully in shaping what happens in their classrooms is usually called #teacher_agency, and the freedom that institutions grant them to do so is usually called autonomy. These two ideas are related but not identical, and much of this article is about the difference between them. Centralized curricula exist for good reasons. They aim to guarantee that every student, wherever they live, has access to important knowledge. They give parents and employers a shared sense of what a qualification means. They allow a system to check whether schools are doing their job. Many reform movements have pushed for stronger central prescription because they worried that leaving everything to local choice produced uneven quality and deep #inequality between schools. These arguments are serious, and this article does not dismiss them. The real question is not whether there should be a common curriculum, but how much space a common curriculum should leave for professional judgement, and how that space can be used well. Over the past decade, research on this question has changed in an important way. Earlier work often treated teachers either as faithful implementers of policy or as resisters of it. Newer research treats teachers as #curriculum_makers who are always doing some curriculum work, whether or not policy recognizes it. A teacher who chooses a local example, slows down for a struggling group, translates a key word into a student's home language, or swaps a written test for an oral one is making curriculum. The question is whether that work is recognized, supported and done well, or whether it happens quietly, unevenly and under pressure. The term #co_design adds a further layer. Co-design refers to curriculum work done jointly by several people, such as teachers in a department, teachers and university researchers, faculty members and learning designers, or teachers and their own students. Co-design moves curriculum adaptation from a private act to a shared, visible and improvable practice. It also raises new questions about power, time and ownership. This article sets out to answer three linked research questions. First, what degree of autonomy do teachers and faculty members typically hold over centralized, standardized curricula, and how is that autonomy distributed across different parts of the curriculum? Second, what factors enable or constrain teachers in turning formal autonomy into achieved agency on behalf of diverse learners? Third, what does recent evidence suggest about how curriculum co-design can strengthen responsible adaptation without losing the benefits of a common curriculum? The article makes three contributions. It brings together recent international research from school and university settings that is usually read separately. It offers a simple continuum of curricular autonomy that students and practitioners can use to describe their own situation. And it proposes a co-design cycle and a set of practical recommendations for policymakers, school and university leaders, and teachers themselves. Throughout, the language is kept plain, because the people who most need to think about these questions, including student teachers and new faculty members, deserve research that is easy to read. The rest of the article is organized as follows. Section 2 reviews the relevant literature. Section 3 explains the theoretical framework. Section 4 describes the review approach. Section 5 presents the analysis in six themes. Section 6 discusses the findings and their implications, and Section 7 concludes. 2. Literature Review 2.1 From implementation to curriculum making For much of the twentieth century, curriculum policy was imagined as a chain. Experts and governments designed a curriculum, textbooks and guides translated it, and teachers delivered it. Success was measured by fidelity, that is, by how closely classroom practice matched the intended design. This view still shapes many systems, and the word #curriculum_fidelity remains common in policy documents and research. A strong body of recent work challenges this chain picture. Priestley, Philippou, Alvunger and Soini (2021) argue that curriculum making should not be understood as something that happens at fixed levels connected by a linear chain of command. Instead, they describe curriculum making as types of activity that take place across intertwined sites, which they name supra, macro, meso, micro and nano. Supra refers to transnational influences such as international organizations and global policy trends. Macro refers to national policy, including curriculum frameworks and legislation. Meso refers to the many intermediate actors, such as local authorities, agencies, inspectorates and textbook publishers, that interpret and package policy. Micro refers to schools and departments, and nano refers to the classroom itself, where teachers and students interact. The important point is that curriculum is made, and remade, at every one of these sites, and that the sites shape each other in both directions. This framing has been taken up widely. In Australia, Garrard, Cairns, Weinmann, Hannigan and Phillips (2025) used the same sites to show how teachers, students, school leaders and community organizations are often pushed to the edge of curriculum debates during reform, even though they are essential curriculum actors. Their work highlights that top-down, politicized curriculum reform can silence the very people who know most about local learners. 2.2 Teacher curriculum competence in centralized systems If teachers are curriculum makers, what knowledge and skill does that role require? Tran and Reid O'Connor (2024) propose the idea of #curriculum_competence, which they describe as a combination of formal and practical teacher knowledge and orientations in relation to curriculum. Writing about mathematics in a nationally mandated system, they show that competence includes what teachers notice in the curriculum, how they interpret it, and why they make particular decisions, such as when to introduce a concept, based on the needs of their local students. They also argue that working closely with curriculum develops teacher knowledge in return. This is a useful reminder that adaptation is not a departure from professionalism but one of its central expressions. 2.3 The squeeze of accountability A large literature examines how test-based and #performance_based_accountability affect teachers' room for manoeuvre. Parcerisa, Verger, Pages and Browes (2022) reviewed 101 studies published between 2017 and 2020 and found that the effects of accountability on teachers' work and beliefs vary a great deal. Crucially, they concluded that the impact on teacher autonomy depends not only on how high the stakes are but also on how the teaching profession is regulated in each country. In other words, the same kind of test can feel very different in a system that trusts teachers as professionals and one that manages them as employees. Levatino, Parcerisa and Verger (2024) added a surprising finding. Using a survey experiment with teachers, they found that the type and level of stakes did not make much difference to whether teachers engaged in side-effect practices, such as narrowing teaching towards the test. Even symbolic consequences, such as public comparison, triggered similar reactions to material ones. This suggests that the pressure teachers feel is not only a matter of formal sanctions but also of reputation, identity and comparison. In England, Towers, Gewirtz, Maguire and Neumann (2022) studied secondary teachers' responses to high-stakes reforms. Many participants expressed negative views of the reforms and concerns about staying in teaching, raising questions about #teacher_wellbeing and supporting a narrative of a profession in crisis. However, the authors also found a counter-narrative of job satisfaction and a desire to remain. Proudfoot and Boyd (2024), also in England, introduced the concept of instrumental motivation to describe how high-stakes performance management can damage teachers' motivation to engage in genuine #professional_learning, pushing them to learn only what is needed to meet targets. 2.4 Scripted lessons and the shrinking space of planning One of the most direct ways that standardization reaches the classroom is through #scripted_lessons and centrally produced lesson plans. Narayanan, Shields and Delhagen (2024) surveyed 155 teachers in charter management organizations, independent charter schools and district schools in the United States. Teachers who wrote their own lessons, and those in high schools, reported the greatest autonomy, while elementary teachers and those who received scripted lesson plans reported the least. Interviews with 17 teachers complicated this picture. Similar organizational structures could produce very different feelings of autonomy, and the key difference was trust. Where relationships of trust existed, teachers found spaces of autonomy even inside restrictive structures. Where trust was missing, planning felt controlling or isolating. A related development in England is the provision of a publicly funded online platform of curriculum resources. Swift, Clowes, Gilbert and Lambert (2024), writing as teacher-researchers, argue that treating such a platform as the answer to teachers' curriculum design capability restricts both curriculum and professional development. Drawing on the tradition of the teacher as researcher, they describe a project in which teachers acted as co-enquirers in curriculum design and report benefits both for their own professional learning and for their pupils. 2.5 Does autonomy improve learning? It would be convenient if research showed that more autonomy always produced better results. It does not. Jerrim, Morgan and Sims (2023) linked mathematics test scores to survey data from an international video study of teaching. With the possible exception of inexperienced teachers, they found generally no relationship between teacher autonomy and pupil outcomes, whether measured by test scores, self-efficacy or interest in mathematics. However, teachers with very low autonomy were more likely to report lower #job_satisfaction. The authors suggest that some limits on autonomy may be justified, especially for new teachers, when they involve only partial control and are used to introduce evidence-based methods. This finding is important for balance. It suggests that autonomy is not a magic ingredient, but that removing it almost entirely carries a real cost for the workforce. 2.6 Agency in reform contexts around the world Studies from many countries show teachers negotiating national curricula. In China, Wang (2022) surveyed 353 high school English teachers about an ongoing curriculum reform. Most held positive attitudes towards the reform, but their agency in practice was constrained. Prior experience, beliefs and the perceived school culture, including relations with students, colleagues and administrators, shaped how much agency they could exercise. Wang also notes the tension teachers face between preparing students for high-stakes examinations and pursuing more holistic aims. In Colombia, Valdelamar Gonzalez and Calle-Diaz (2023) followed three high school teachers as they enacted a #national_curriculum for English. Using the ecological model of agency, they showed that teachers coped with policy change in different ways depending on their histories, present conditions and future aims. In Indonesia, Saefudin, Wasino, Susanto and Musadad (2024) studied history teachers in three different types of school during the move to a new national curriculum that promotes independent learning. They found that teachers' autonomy was concentrated in the practical stage of lesson planning rather than in shaping the outcomes the government expected, and they noted a contradiction in a policy that offers freedom in principle while still standardizing schools. In Finland, a system often admired for trusting teachers, Ince and Tikkanen (2025) interviewed nine teachers and proposed a relational view of autonomy. They found that teachers' professional decision-making developed through socialization into school culture, shared responsibility with colleagues, the curriculum framework itself, and planning practices that were often synchronized by coursebooks. Autonomy, in their account, is not freedom from others but something that works through relationships and structures. 2.7 Early career teachers and teacher education Several recent studies focus on the beginning of a teacher's career. Poulton and Mockler (2024) examined the experiences of early career primary teachers in Australia and found that constrained curriculum-making experiences limited their chances to develop identities as knowledge-led curriculum makers, reinforcing an image of teachers as technicians. Poulton (2025) extended this work to preservice teachers, showing that professional placements often gave them very limited curriculum-making opportunities and arguing that the outsourcing of curriculum thinking risks further #deprofessionalization. On a more hopeful note, Banegas, Budzenski and Yang (2024) reported that a curriculum development course for 220 preservice language teachers helped strengthen their projective agency, that is, their sense of how they would act in future, including their willingness to use agency for critical teaching in multilingual classrooms. Leijen, Pedaste and Baucal (2024) developed and validated a questionnaire that measures eight dimensions of teacher agency in planning and in using digital technology, giving researchers a tool to compare agency across contexts. 2.8 Co-design in schools and universities Collaborative curriculum design has a growing evidence base. Ke, Friedrichsen, Rawson and Sadler (2023) studied teacher learning through collaborative curriculum design during the pandemic, using activity theory to trace how design work functioned as professional learning. Kim and Kwon (2025) followed two middle school teachers who co-designed and then co-taught an integrated artificial intelligence unit within science, technology, engineering and mathematics. The less experienced teacher's engagement grew through a structured support system involving shared goals, tailored resources and repeated refinement, and the authors conclude that co-design and #co_teaching can foster teacher ownership. In #higher_education, Zeivots, Hopwood, Wardak and Cram (2025) studied three co-design projects inside a large curriculum transformation at an Australian university. Teams included course coordinators, educational developers, learning designers, tutors, students, industry representatives and media producers. The authors show co-design as a process of learning and of coming to practise differently, shaped by its institutional setting but also reshaping that setting. Cong-Lem (2026) reviewed 42 empirical studies of tertiary educators' agency and identified curriculum change as one of its key practices, alongside pedagogical innovation, research engagement, professional learning and well-being. The review stresses that individual capacity matters, but so do contextual conditions such as leadership and support. 2.9 Gaps in the literature Three gaps stand out. First, research on schools and research on universities rarely speak to each other, even though both sectors are moving towards standardized outcomes and templates. Second, many studies describe either constraints or success stories, but fewer explain systematically how formal autonomy, teacher capacity and support interact. Third, there is still limited practical guidance for teachers on how to adapt a common curriculum responsibly for diverse learners without either drifting from shared standards or carrying the whole burden alone. This article aims to address these gaps. 3. Theoretical Framework This article uses two connected lenses. The first explains what teacher agency is. The second explains where curriculum decisions are made. Together they help us see why two teachers with identical formal freedom may act very differently, and why a teacher with little formal freedom may still find ways to adapt. 3.1 The ecological model of teacher agency The #ecological_model of teacher agency, developed by Priestley, Biesta and Robinson (2015), starts from a simple but powerful claim: agency is not something a person has, like a personality trait. It is something a person achieves, in particular situations, through the interplay of their own capacities and the conditions around them. A teacher may act with strong agency in one school and weak agency in another, not because they have changed as a person but because the situation has changed. The model describes three dimensions that come together in any moment of action. The iterational dimension concerns the past: the teacher's own schooling, training, professional experience, beliefs and habits. A teacher who once struggled as a second-language learner, for example, may bring a deep sensitivity to students in the same position. The practical-evaluative dimension concerns the present: the cultural, structural and material conditions of the job. These include the rules, the timetable, the available resources, the relationships with colleagues and leaders, and the ideas about good teaching that circulate in the school. The projective dimension concerns the future: the short-term and long-term aims the teacher holds for students and for their own practice. This model is widely used in the studies reviewed here. Valdelamar Gonzalez and Calle-Diaz (2023) and Wang (2022) both applied it to national curriculum reforms, and Leijen, Pedaste and Baucal (2024) built their measurement instrument on it. The model is helpful for our purpose because it shifts attention away from asking whether teachers are willing to adapt the curriculum and towards asking whether the ecology of their work makes adaptation possible. 3.2 Sites of curriculum making The second lens, introduced in the literature review, is the framing of curriculum making as activity across five sites: supra, macro, meso, micro and nano (Priestley et al., 2021). This framing helps us locate autonomy. A national ministry may grant teachers wide freedom at the macro site, but a local authority, an examination board or a textbook publisher at the meso site may effectively take that freedom back. A school may adopt a single scheme of work at the micro site, leaving little room at the nano site. Equally, a supportive department at the micro site may create space for adaptation that national policy never explicitly mentions. 3.3 Bringing the two lenses together Figure 1 combines the two lenses. On the left are the sites of curriculum making, with arrows in both directions to show that influence flows up as well as down. On the right are the three dimensions of agency. The conditions created at each site feed directly into the practical-evaluative dimension: they enable or constrain what a teacher can do in the present. Achieved agency, shown at the bottom right, is the point where a teacher's history, present situation and future aims come together into actual adaptation and co-design for diverse learners. Figure 1. Conceptual framework: sites of curriculum making shape the practical-evaluative conditions of teacher agency. Three propositions follow from this framework and guide the analysis. First, formal autonomy granted at the macro site does not automatically produce achieved agency at the nano site. Second, achieved agency depends on the alignment of conditions across several sites, not just one. Third, because agency is achieved in context, it can be strengthened deliberately by changing those conditions, including through collaborative design. 4. Review Approach This article is an integrative narrative review rather than a new empirical study. Its purpose is to bring together recent research in a form that is useful to students and practitioners. The review focused on peer-reviewed journal articles, scholarly books and book chapters, and reports from international organizations, published mostly between 2021 and 2025. A small number of earlier foundational works were included where they provide the theory that recent studies build on. Sources were identified through searches of large scholarly databases using combinations of terms such as teacher agency, curriculum making, curriculum adaptation, teacher autonomy, scripted lessons, accountability, collaborative curriculum design and co-design, in both school and higher education settings. Studies were selected if they addressed how teachers or faculty members respond to, adapt or design curriculum within a centrally defined framework, or if they examined conditions that shape this work. Priority was given to studies with clear methods, to work published in established journals, and to a geographical spread that includes Europe, North and South America, Asia and Australia. Every source was checked against its publication record before inclusion. The selected studies were read and coded for evidence on four questions: which parts of the curriculum teachers could change, what helped them change it, what stopped them, and what forms collaborative design took. The themes in Section 5 emerged from this reading. The approach has clear limits. It is not a systematic review, so it does not claim to capture every relevant study, and no statistical synthesis was attempted. The selection inevitably reflects what is published in English. Findings from one country should not be assumed to transfer directly to another. These limits are discussed again in Section 6. 5. Analysis and Findings 5.1 How much autonomy? A continuum, not a switch Public debate often treats curriculum autonomy as a yes or no question: either teachers are free or they are not. The evidence reviewed here suggests a more useful picture. Autonomy is better described as a continuum with several recognizable positions. Figure 2 sets out five of them. Figure 2. A continuum of curricular autonomy, from scripted delivery to teacher-led curriculum making. At one end is scripted delivery, where lessons, pacing and assessments are fixed centrally and teachers are expected to follow them closely. The charter school settings described by Narayanan et al. (2024) include versions of this model. Next comes bounded adaptation, where the core materials are fixed but teachers can change examples, timing, grouping and explanations. This is probably the most common position in many systems, and it matches the Indonesian finding that teacher autonomy was concentrated in the practical stage of lesson planning (Saefudin et al., 2024). The third position, negotiated adaptation, involves larger changes that are discussed and agreed with department heads, curriculum coordinators or school leaders. The fourth, #curriculum_co_design, describes teams of teachers, sometimes with researchers, designers or students, building or rebuilding units of study together within a common framework (Ke et al., 2023; Kim and Kwon, 2025; Zeivots et al., 2025). At the far end is teacher-led curriculum making, where teachers select content, sequence and assessment themselves within broad national guidance. The history curricula of England and Scotland, which avoid prescribing specific content and expect schools to devise their own, are examples (Smith, Harris and Burn, 2025). Two further points matter. First, a single teacher may sit at different points of the continuum for different subjects or year groups. A secondary teacher may have wide freedom in a non-examined course and almost none in a course that ends with a national test. Second, autonomy is distributed unevenly across the parts of a curriculum. Drawing together the studies reviewed, a general pattern appears. Teachers usually hold most freedom over teaching methods, examples, classroom talk and the order of activities within a lesson. They hold less freedom over the selection of content and the pace of coverage. They hold least freedom over the intended outcomes and over high-stakes assessment. Ince and Tikkanen (2025) show that even in Finland, planning is often synchronized by coursebooks and shaped by testing practices, while Narayanan et al. (2024) show that where lesson plans are supplied, the planning stage itself may be removed from teachers' hands. This uneven distribution matters for #learner_diversity. Many of the most powerful adaptations for diverse learners involve not only how something is taught but also what is emphasized, how fast the class moves and how learning is shown. A student who is new to the language of instruction may need more time on fewer concepts. A student with a strong interest in a local issue may engage far more deeply if a unit is rebuilt around it. A student with a disability may need a different way to demonstrate understanding. When the pace, the content and the assessment are fixed, teachers are left to adapt only the surface of the lesson, which limits how far they can genuinely meet different needs. 5.2 Formal autonomy is not the same as achieved agency One of the clearest findings across the literature is that policy statements about autonomy tell us only part of the story. Smith, Harris and Burn (2025) provide a striking example. In Scotland, policy explicitly describes teachers as curriculum makers. In England, it does not. Yet survey data from history teachers suggested that teachers in English secondary schools were more likely than those in Scotland to have diversified their curricula. The authors argue that demographic diversity, #inspection cultures and knowledge exchange networks had a greater influence on teachers' willingness to diversify their curricula than how policy described teachers' role. This finding fits the ecological model well. Formal autonomy, granted at the macro site, is only one input into the practical-evaluative dimension of agency. Whether teachers actually use it depends on what they believe their students need, what their colleagues are doing, what inspectors are likely to value and what ideas and resources they can draw on. A policy that grants freedom but leaves teachers isolated, unsure or afraid of judgement may produce less adaptation than a policy that grants less freedom but sits inside a lively professional community. The reverse also happens. Teachers with little formal autonomy often find what Narayanan et al. (2024) call spaces of autonomy inside restrictive structures. They adjust a script, add a story, rearrange a group or quietly spend an extra day on a concept the class has not grasped. Valdelamar Gonzalez and Calle-Diaz (2023) show teachers coping with a national curriculum in different ways, shaped by their personal histories and aims. Wang (2022) describes teachers who support a reform in principle but find their agency constrained in practice. Agency, in all these cases, is something achieved in the gap between official rules and daily realities. The practical lesson is that systems cannot simply legislate adaptation into existence. Granting formal autonomy is necessary for the upper end of the continuum, but it is not sufficient. The conditions for using it must also be built. 5.3 Accountability pressure and the narrowing of adaptation If formal autonomy is one side of the equation, #accountability is often the other. Every study of #high_stakes_testing reviewed here points in a similar direction: when results on a narrow set of measures carry consequences for schools or teachers, adaptation tends to be pulled towards the measured content and away from broader needs. The mechanisms are subtle. Parcerisa et al. (2022) show that the impact of accountability on teacher autonomy depends on how the profession is regulated, which suggests that pressure is filtered through local professional cultures. Levatino et al. (2024) found that symbolic consequences, not just material ones, triggered similar responses among teachers. This means that a system does not need harsh sanctions to narrow teaching. Public league tables, comparison between colleagues or the fear of being seen as a weak school may be enough. The effects on the workforce are also important. Proudfoot and Boyd (2024) describe how high-stakes performance management can produce instrumental motivation, where teachers learn only what helps them meet targets rather than what helps them grow as professionals. Towers et al. (2022) document a strong sense of crisis among some teachers, though also a counter-current of satisfaction and commitment. For curriculum adaptation, these findings matter because adaptation requires both energy and curiosity. A teacher who is anxious, overloaded or focused only on targets is less likely to experiment with new ways of reaching students who do not fit the standard path. None of this means that accountability is wrong in itself. Students and families have a right to know whether schools are teaching well. The problem arises when accountability measures are so narrow, and so heavily weighted, that they effectively replace the curriculum. In such cases, the official curriculum may be broad and flexible on paper, while the curriculum that is actually taught is the test. 5.4 Trust, leadership and relational autonomy A recurring finding across very different contexts is the role of #professional_trust. In the study by Narayanan et al. (2024), trust was the factor that explained why similar organizational structures produced very different feelings of autonomy. Where teachers trusted their leaders and colleagues, even shared or supplied lesson plans could feel supportive. Where trust was absent, the same structures felt controlling. Ince and Tikkanen (2025) go further and argue that autonomy should be understood relationally. Their Finnish teachers did not experience autonomy as standing alone against the system. Instead, their decision-making was shaped through socialization into school culture, shared responsibility with colleagues and a curriculum framework that carried a sense of professional responsibility. This view challenges a common assumption that collaboration and autonomy pull in opposite directions. In a relational view, collaboration is one of the main ways autonomy is exercised. #school_leadership sits at the centre of these relationships. Leaders decide whether department time is spent on compliance checks or on joint planning, whether deviations from a scheme of work are treated as errors or as professional judgements to be discussed, and whether teachers are protected from or exposed to external pressure. Wang (2022) found that perceived administrative support and colleague cooperation shaped teachers' agency during curriculum reform. The OECD (2024) report on curriculum flexibility and autonomy similarly emphasizes collaborative policymaking, teacher training, clear goals and societal support as conditions for flexible curricula to work. Leadership, in short, is one of the most important micro-site conditions that turns formal freedom into achieved agency. For students reading this article, a useful question to ask of any school or department is not only how much freedom teachers have, but what happens when a teacher uses it. Is a thoughtful change welcomed, questioned constructively, or punished? The answer often says more about real autonomy than any policy document. 5.5 Capacity, career stage and preparation Autonomy is only valuable if teachers have the knowledge and skill to use it well. Tran and Reid O'Connor (2024) describe curriculum competence as including what teachers attend to, how they interpret curriculum and why they make particular decisions for their students. This competence is not automatic. It grows through experience, study and, crucially, through actually doing curriculum work. The finding by Jerrim et al. (2023) that autonomy was not generally linked to pupil outcomes, with a possible exception for inexperienced teachers, is relevant here. It suggests that for teachers early in their careers, more structure may be helpful, at least for some aspects of practice. At the same time, Poulton and Mockler (2024) and Poulton (2025) warn that if #early_career_teachers and preservice teachers are given almost no chance to make curriculum, they may never develop the identity and competence of curriculum makers. Structure without development produces technicians, not professionals. These two findings can be reconciled. What new teachers need is not simply less freedom or more freedom, but a gradual, supported widening of curricular responsibility. A beginning teacher might start with well-designed shared materials, adapt them in bounded ways with feedback from a mentor, then take part in negotiated adaptation and co-design with more experienced colleagues. The study by Banegas et al. (2024) shows that #teacher_education can deliberately build projective agency for diverse and multilingual classrooms. The measurement work of Leijen et al. (2024) also offers a way to track how agency develops across career stages, although the authors caution that their instrument should not be used to compare preservice and in-service teachers directly. Figure 3 brings together the two dimensions discussed so far: the formal flexibility a system grants and the capacity and support available to teachers. Figure 3. Four conditions of curricular agency produced by combining formal flexibility with teacher capacity and support. When both are low, the result is compliant delivery: teachers follow prescribed lessons with few changes, and diverse needs are often unmet. When capacity is high but flexibility is low, teachers experience frustrated agency, knowing what their students need but lacking room to provide it. This matches many of the accounts of accountability pressure above. When flexibility is high but capacity and support are low, the result is unsupported freedom, which can lead to uneven quality across classrooms and heavy individual workloads. This is the risk that critics of teacher autonomy often point to, and it helps explain why some systems have moved towards more prescription. Only when both are high does supported co-design become possible, with real adaptation for learners within a shared framework. The figure is a simplification, but it shows why debates that focus only on flexibility, or only on teacher quality, tend to miss the point. 5.6 Co-design as a bridge between common standards and local needs The studies of collaborative design reviewed here suggest that co-design can do something that individual adaptation struggles to do. It allows teachers to adapt the curriculum substantially while keeping it connected to shared standards, shared expertise and shared accountability. Several benefits appear across the evidence. First, co-design is a powerful form of #teacher_development. Ke et al. (2023) trace teacher learning through collaborative design, and Kim and Kwon (2025) show a less experienced teacher's engagement growing through shared goals, tailored resources and repeated refinement. Second, co-design spreads the workload. One of the strongest objections to teacher-led adaptation is that it asks every teacher to reinvent every lesson. When a team designs a unit together, the effort is shared and the result can be reused and improved. Third, co-design builds ownership. Teachers who have shaped a unit understand its purpose and are more likely to teach it well and adapt it sensibly in the moment. Fourth, co-design can bring in voices that are usually missing. Swift et al. (2024) describe teachers acting as co-enquirers, and Garrard et al. (2025) show how students and community organizations can contribute to curriculum making when they are invited in. Co-design is not a cure for every problem. It needs time, which is scarce in most schools. It can be dominated by the most confident or senior voices. It can drift into producing materials that look impressive but do not meet the needs of the students in the room. Zeivots et al. (2025) emphasize that co-design is shaped by the institutional site in which it takes place, which means that supportive structures, not just good intentions, are required. These are reasons to design co-design processes carefully, not reasons to avoid them. 5.7 Faculty autonomy in higher education University teachers are often assumed to enjoy far more curricular freedom than school teachers, and in many respects they do. Academic tradition has long linked university teaching to #academic_freedom, and many faculty members still design their own courses. However, the higher education literature reviewed here suggests that this freedom is also being reshaped. Large curriculum transformation projects, institution-wide templates, required learning outcomes, quality assurance processes and professional accreditation can all narrow the space in which individual faculty members design and adapt their teaching. The co-design projects studied by Zeivots et al. (2025) took place inside such a transformation initiative. Rather than a single academic designing a course alone, teams of coordinators, educational developers, learning designers, tutors, students and industry representatives worked together. This model can produce richer and more coherent courses. It also changes what faculty autonomy means. Autonomy shifts from the right to design a course individually towards the capacity to shape a shared design. Cong-Lem (2026) shows that curriculum change is one of the main arenas in which university teachers exercise agency, and that leadership and support are key conditions for it. For #faculty_autonomy, the implications mirror those in schools. A standardized outcome template can protect students by making expectations clear and comparable. But if it is applied too rigidly, it may prevent faculty members from responding to the increasingly diverse student body that universities now serve, including mature students, part-time and working students, first-generation students and international students learning in a second language. The challenge is the same: to keep what is valuable in standardization while leaving, and supporting, room for responsive design. 5.8 What adaptation for diverse learners looks like in practice It is worth being concrete about what responsible adaptation involves, because abstract talk of autonomy can hide the actual work. Drawing on the studies reviewed, adaptation for diverse learners usually takes several forms. The first is adapting access. Teachers keep the same learning goal but change how students reach it, for example by providing key vocabulary in advance, using visual supports, breaking tasks into smaller steps or offering texts at different reading levels. This is the core of #differentiation and of #inclusive_education. The second is adapting relevance. Teachers connect required content to students' lives, cultures, languages and communities. The diversification of history curricula described by Smith, Harris and Burn (2025) is an example at the level of content selection, and Banegas et al. (2024) show how preparation for #multilingual_classrooms can build teachers' readiness to make such connections. The third is adapting pace and depth. Teachers spend longer on foundations for some students and offer extension for others, which requires some freedom over pacing. The fourth is adapting assessment. Teachers allow students to show understanding in different ways, such as orally, visually or through projects, and use #formative_assessment to decide what to do next. Each of these forms of adaptation sits at a different point on the continuum in Figure 2, and each requires a different degree of autonomy. Adapting access is often possible even under bounded adaptation. Adapting relevance may require freedom over content selection. Adapting pace requires freedom from rigid #pacing_guides. Adapting assessment may be impossible where a single high-stakes test determines outcomes. Mapping these requirements onto the actual freedoms in a given system is a useful way for teachers and leaders to see where the real barriers lie. 5.9 Two illustrative scenarios To show how these findings play out, consider two composite scenarios. They are not reports of specific studies but illustrations built from the patterns described above, offered to help readers connect the research to everyday practice. In the first scenario, a primary school in a large urban district receives a new mathematics programme with daily scripted lessons and a fortnightly district check. One class includes several children who have recently arrived and are still learning the language of instruction, two children with diagnosed learning difficulties and a small group who are already well ahead. The teacher can see that the scripted pace leaves the new arrivals confused by the third lesson and the advanced group bored by the first. Formally, she may only add short explanations and change seating. She does what she can within those limits, previewing vocabulary with a picture card and pairing students carefully, but she worries that any larger change will show up as a gap on the district check. Her agency is real but frustrated, close to the top-left cell of Figure 3. Nothing in her own skill or commitment is lacking. What is missing is room and support. In the second scenario, a secondary school in a different district uses the same national standards but organizes its mathematics department differently. Each half term, teachers meet for a protected planning session in which they read the relevant standards together, look at last term's student work, and design the next unit as a team. They agree on the essential ideas every student must reach, then build in pre-teaching of key terms for students new to the language, worked examples in several forms, a challenge track for confident learners and two ways of showing understanding at the end. The head of department treats changes made in the moment as information to bring back to the next meeting rather than as departures to be justified. Over two years, the department builds a shared bank of adapted units that new teachers can use from their first week. This is close to the supported co-design cell of Figure 3, and it reflects the trust, collegial structure and capacity-building that the research associates with achieved #curricular_agency. The difference between these two scenarios does not lie in the national curriculum, which is the same in both. It lies in the meso and micro sites of curriculum making: the district's choice of materials and checks, and the school's choices about time, leadership and collaboration. This is precisely the point made by the ecological model. Agency is achieved, or blocked, in the ecology of everyday work. 6. Discussion 6.1 Answering the research questions The first research question asked what degree of autonomy teachers and faculty members typically hold over centralized, standardized curricula. The answer is that autonomy is real but partial and uneven. Across the systems reviewed, teachers commonly hold meaningful freedom over methods, examples and classroom interaction, less freedom over content selection and pacing, and least freedom over intended outcomes and high-stakes assessment. Where scripted lessons or centrally supplied plans are used, even the planning stage may be taken out of teachers' hands. In universities, faculty members traditionally hold wider freedom, but institutional templates, quality processes and large-scale curriculum transformation projects are reshaping that freedom into a more collective and more regulated form. The second question asked what enables or constrains teachers in turning formal autonomy into achieved agency. The evidence points to five clusters of factors. The first is the design of accountability, especially how narrow and how heavily weighted the measures are and how the teaching profession is regulated. The second is professional trust, particularly between teachers and leaders. The third is teacher capacity, including curriculum competence, experience and preparation. The fourth is collegial structure, meaning whether there are regular opportunities to plan, reflect and design together. The fifth is the wider culture of ideas and networks, which shapes what teachers believe is possible and desirable. These factors work together, which is why the same policy can produce very different results in different places. The third question asked how co-design can strengthen responsible adaptation without losing the benefits of a common curriculum. The evidence suggests that co-design, when properly supported, can combine three things that are often seen as being in tension: shared standards, professional judgement and responsiveness to learners. It does this by moving adaptation from a private act to a shared one, which makes it more visible, more open to improvement and more sustainable in terms of workload. 6.2 A practical co-design cycle To make these findings usable, Figure 4 proposes a simple co-design cycle for teams working within a centralized curriculum. It is not a fixed procedure but a way of organizing collaborative work so that adaptation stays connected both to the standard and to the students. Figure 4. A six-step curriculum co-design cycle for adapting a common standard to diverse learners. The cycle begins with reading the standard closely. Teams ask what students must actually learn, separating the essential knowledge and skills from the particular examples and activities that a textbook or guide happens to use. This step is important because it shows where flexibility already exists. Many standards are less prescriptive than the materials built around them. The second step is mapping the learners. Teams gather what they know about their students' readiness, languages, interests, needs and circumstances, using evidence from previous work and, where possible, from students themselves. The aim is not to label students but to anticipate where the standard path is likely to work and where it is not. The third step is co-designing the unit. Teams decide which content to emphasize, which tasks and examples to use, what supports to build in for students who need them, what extensions to offer for those ready to go further, and what choices students might have. This is where most adaptation happens, and where shared expertise is most valuable. The fourth step is teaching and observing. Each teacher teaches the unit, adapting further in the moment, and pays deliberate attention to how different students respond. The fifth step is formative assessment and #student_voice. Teams gather evidence of learning and ask students what helped and what did not. This step keeps the process honest by checking whether adaptations actually served learners. The sixth step is reflection, revision and sharing. Teams meet to compare evidence, revise the unit and share it with colleagues. Over time, a department builds a library of tested, adapted units that new teachers can use and further adapt, which supports the gradual widening of responsibility discussed in Section 5.5. At the centre of the cycle is a simple principle: the same standard, different routes. The goal of adaptation is not to lower expectations for some students but to give every student a realistic path to meaningful learning. 6.3 Implications for policymakers For those who design national and regional curricula, the evidence suggests several priorities. First, write curriculum frameworks that are clear about essential outcomes but deliberately open about examples, sequence and methods, and say explicitly where local adaptation is expected. Second, review accountability systems to check whether they narrow the taught curriculum in practice, paying attention not only to formal sanctions but also to symbolic pressures such as public comparison. Third, recognize that publishing high-quality shared materials can support teachers, but that materials should be offered as starting points for professional adaptation rather than as scripts to be followed. Fourth, invest in the conditions for #teacher_collaboration, especially protected time for joint planning, since formal autonomy without these conditions tends to produce either compliance or uneven quality. The OECD (2024) analysis of #curriculum_flexibility makes a similar case for combining flexibility with teacher training, collaborative policymaking, clear goals and accountability. 6.4 Implications for school and university leaders Leaders shape the micro site where formal autonomy becomes real or remains on paper. They can protect regular time for departments and course teams to design and review curriculum together. They can treat thoughtful deviations from a common scheme as professional judgements to be discussed, not as errors to be corrected. They can build trust by being transparent about external pressures and by shielding teachers from the most narrowing effects of accountability where possible. In universities, leaders of curriculum transformation can design co-design processes that give faculty members, students and support staff real influence, and they can apply outcome templates in ways that set clear expectations while leaving room for responsive teaching. 6.5 Implications for teachers and student teachers For teachers, the main implication is that curriculum agency is a professional capacity to be developed, not simply a right to be claimed. Reading standards closely, understanding learners deeply, designing with colleagues and checking adaptations against evidence are all skills that grow with practice. Student teachers in particular can seek out placements, mentors and courses that give them real opportunities to take part in curriculum work. Even in highly prescribed settings, teachers can look for the spaces of autonomy that the research describes, and they can make their adaptations visible and shared, so that good ideas spread and weak ones are improved. 6.6 Common misconceptions about curriculum autonomy The research reviewed here also helps correct several common misconceptions that often appear in public debate and in student discussions. The first misconception is that autonomy and standards are opposites. In fact, most of the strongest examples of adaptation in the literature took place within shared standards. Clear standards can make adaptation easier, because they tell teachers what must be preserved and therefore what can safely be changed. The second misconception is that teacher autonomy means every teacher working alone. The relational view of autonomy (Ince and Tikkanen, 2025) and the evidence on co-design suggest the opposite. Much of the most effective curricular agency is exercised together, in departments, teams and partnerships. The third misconception is that more prescription always protects weaker students. Scripted materials can provide a useful floor of quality, especially for new teachers, and the evidence does not suggest that autonomy automatically raises results (Jerrim et al., 2023). But a curriculum delivered at a fixed pace with no room to adjust can leave behind exactly the students who most need adaptation, such as #English_language_learners, students with disabilities and students whose prior schooling was interrupted. Protection for these students depends on responsive teaching, not only on uniform delivery. The fourth misconception is that giving teachers freedom is enough. As Figure 3 shows, freedom without capacity and support can produce uneven quality and heavy workloads. Autonomy is best understood as a shared professional responsibility that systems must deliberately build, not simply a permission they grant. 6.7 Limitations and directions for future research This review has several limitations. It is integrative rather than systematic, so it may have missed relevant studies. It draws mainly on English-language research. Many of the studies it reviews are small, qualitative and context-specific, which makes them rich in insight but limited in their ability to support general claims. The relationship between curricular autonomy and student outcomes, in particular, remains under-researched, and the one large quantitative study reviewed here found little direct link (Jerrim et al., 2023). Future research could move in several directions. Studies that follow teachers over time could show how curriculum agency develops across a career, using tools such as the questionnaire developed by Leijen et al. (2024). Comparative studies could examine how different combinations of formal flexibility and support, as shown in Figure 3, affect both teachers and students. Research on co-design could pay more attention to student participation and to whether co-designed units actually improve learning for the most marginalized students. Finally, research in higher education and schools could be brought closer together, since both sectors face similar tensions between standardization and responsiveness. 7. Conclusion This article set out to assess the degree of autonomy that teachers and faculty members hold to modify and adapt centralized, standardized curricula for the diverse students in their classrooms. The review of recent research suggests that the most useful answer is not a single number or a simple yes or no. Autonomy varies along a continuum, from scripted delivery to teacher-led curriculum making, and it is distributed unevenly across the parts of a curriculum. Teachers usually have most freedom over how they teach and least over what outcomes are measured and how. More importantly, formal autonomy is only the starting point. Whether teachers actually use their freedom on behalf of diverse learners depends on the ecology of their work: their histories and beliefs, the trust and support around them, the pressure of accountability, their preparation and competence, and the presence or absence of opportunities to design together. Formal freedom without support can produce uneven quality and exhaustion. Support without freedom can produce frustration. Neither serves students well. Curriculum co-design offers a promising way forward. 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Higher Education Research and Development, 44(3), 769-783. https://doi.org/10.1080/07294360.2024.2410269 #TeacherAgency #co_designed_curriculum #teacher_autonomy #CurriculumMaking #standardized_curricula #diverse_learners #inclusive_classrooms #differentiated_instruction #teacher_professionalism #education_policy #faculty_agency #curriculum_reform #teachers_as_curriculum_makers #equity_in_education #STULIB

Universities have spent the last decade promising to support the United Nations Sustainable Development Goals (SDGs), yet in many institutions sustainability and global citizenship still live at the edges of student life, in clubs, awareness weeks, and optional courses. This article asks whether moving these themes from extracurricular and elective spaces into the core of every degree makes a real difference to what students learn and can do. Using a structured narrative review of recent peer-reviewed studies published mainly between 2021 and 2025, it compares four models of provision: extracurricular activity, elective courses, compulsory stand-alone courses, and embedded cross-cutting themes, and it places these within a whole-institution perspective. The article proposes a six-level integration ladder as a conceptual framework and uses it to read the evidence on reach, depth, continuity, assessment, and staff capacity. The review finds that optional and one-off provision raises awareness but rarely builds lasting competence, that repeated and discipline-specific exposure is linked with deeper learning and stronger student agency, and that embedding succeeds only when institutions invest in curriculum mapping, staff development, assessment design, and leadership support. The main weakness of current evidence is that most studies measure awareness or attitudes rather than demonstrated competence. The article closes with practical guidance for students, teachers, and university leaders, and with a research agenda for evaluating structural integration more rigorously. Keywords: Sustainable Development Goals, education for sustainable development, global citizenship education, curriculum integration, higher education, whole-institution approach, sustainability competencies 1. Introduction Every university student today will spend most of their working life in a world shaped by #climate_change, unequal access to resources, rapid technological change, and pressure on public institutions. The 17 #Sustainable_Development_Goals, adopted by the United Nations in 2015 as part of the #2030_Agenda, give a shared language for these problems. They range from ending poverty and hunger to clean energy, decent work, reduced #inequality, #climate_action, and peaceful and just institutions. Because the goals touch almost every area of human activity, they also touch almost every area of university study. An engineer, a nurse, an accountant, a lawyer, a teacher, and a software developer will all make decisions that help or harm progress on the goals. Universities have responded to this agenda in many ways. Some have signed declarations and joined international networks. Many report their activity through sustainability rankings. Most now have #green_campus projects, student sustainability societies, and events linked to the goals. However, a closer look at what students actually study often tells a different story. In many institutions, the #SDGs appear in a welcome week talk, a student club, or an optional module chosen by a small number of already interested students. The core courses that every student must pass, and that carry most of the credit and most of the assessment weight, often say little or nothing about sustainability or about the student's place in a connected world. This gap between institutional promise and everyday curriculum is the problem this article addresses. Researchers increasingly argue that sustainability and #global_citizenship should not sit beside the curriculum as optional extras but should run through it as cross-cutting themes, present in every discipline and revisited across the whole degree (Kohl et al., 2022; Zaleniene and Pereira, 2021). This idea is often called embedding, mainstreaming, or #curriculum_integration. It sounds sensible, but it is also demanding. It asks every department to rethink #learning_outcomes, every teacher to connect their subject to global problems, and every programme to find room in a crowded timetable. It is fair to ask whether the effort is worth it. The central question of this article is therefore simple to state: does transitioning sustainability and global citizenship concepts from extracurricular or elective activities into core, cross-cutting themes across all disciplines lead to better learning outcomes for students, and under what conditions does it work? To answer it, the article reviews recent research evidence, organises that evidence through a clear conceptual framework, and draws out lessons that students and staff can use. 1.1 Why this question matters for students It is easy to think of curriculum design as a matter for professors and managers. In fact it shapes the daily experience of students in very direct ways. If sustainability is only offered as an elective, a student who chooses a different elective may graduate without ever having been asked how their future profession affects people and the planet. If it appears only in a voluntary club, it competes with paid work, family duties, and exam preparation, and many students simply cannot attend. If it is taught once in the first year and never mentioned again, students may conclude that it is a side topic rather than part of being a competent professional. Employers, professional bodies, and governments are also starting to expect graduates who can work with sustainability problems. Accreditation bodies in fields such as engineering, business, and health are paying growing attention to environmental and social responsibility. Students therefore have a practical interest in whether their degree prepares them for this. Understanding the difference between token inclusion and genuine integration helps students judge their own programmes, ask better questions of their teachers, and take ownership of their learning. 1.2 Aim, approach, and contribution The aim of this article is to evaluate the effectiveness of structural integration of the SDGs and global citizenship into academic curricula, compared with extracurricular and elective provision. The approach is a structured narrative review of recent peer-reviewed studies and authoritative books and reports, with a strong preference for work published within the last five years. Sources were located through scholarly databases and were checked against publisher records before inclusion. The review is not a formal meta-analysis, because the available studies use very different designs, measures, and settings. Instead it reads the evidence thematically, asking what each type of provision achieves for reach, depth, continuity, assessment, and institutional sustainability. The article makes three contributions. First, it brings together evidence on sustainability education and on global citizenship education, two fields that are often discussed separately even though they share many goals. Second, it offers a six-level integration ladder as a practical framework for describing where a programme or institution sits and what the next step might be. Third, it identifies a clear weakness in current research, namely the heavy reliance on measures of awareness and attitude rather than demonstrated competence, and suggests how future evaluations could do better. The rest of the article is organised as follows. Section 2 reviews the literature on the role of universities in the SDGs, on the forms that integration takes, and on global citizenship education. Section 3 sets out the conceptual framework. Section 4 explains the review method. Section 5, the core of the article, analyses the evidence on effectiveness and on the conditions for success. Section 6 discusses implications for practice, and Section 7 concludes with limitations and directions for future research. 2. Literature Review 2.1 Universities and the 2030 Agenda There is broad agreement in recent scholarship that universities hold a special position in relation to the SDGs. They educate future professionals and leaders, they produce research that informs policy, and they are large organisations with their own environmental and social footprint. Zaleniene and Pereira (2021) argue that #higher_education makes a decisive contribution to several goals, including those on poverty, health, gender equality, decent work, responsible consumption, climate action, and strong institutions. They also stress that this contribution depends on sustainability principles being placed at the heart of institutional strategy, including the curriculum and the way the university operates, rather than being left to isolated projects. Kohl et al. (2022) trace the long history of university engagement with sustainability through international declarations and networks. They conclude that higher education has not yet used its full potential and that calls for engagement are often limited to training and supplying research on demand. They argue for a #whole_institution_approach, in which teaching, research, campus operations, governance, and community engagement all move in the same direction. This idea has become central to the debate about curriculum integration, because it suggests that changing courses alone may not be enough if the rest of the institution sends different messages. At the level of strategy, there is evidence that commitment is uneven. In an international study of 128 members of higher education institutions in 28 countries, Leal Filho, Simaens, et al. (2023) found that many organisations recognised the importance of sustainable development in general, but placed comparatively limited emphasis on the SDGs specifically. They also reported a shortage of training opportunities to help university staff handle the SDGs in their work. This finding matters because it suggests that institutional statements of support do not automatically translate into curriculum change. 2.2 How the SDGs currently enter the curriculum Several recent reviews describe how the SDGs are being brought into university teaching around the world. In a scoping review of peer-reviewed articles and grey literature published between 2015 and 2021, Amoros Molina et al. (2023) identified 20 articles and 38 grey literature sources. They found that the SDGs were most often included at bachelor level and in a limited set of disciplines, notably engineering and technology, humanities and social sciences, and business and economics. The most common methods were workshops and courses. Progress was also skewed towards high-income countries. The authors observed that institutions in high-income countries tended to treat the SDGs more academically, while those in low- and middle-income countries more often linked them to solving real local problems. Avelar, da Silva Oliveira, and Farina (2023) examined how universities integrate the SDGs across three areas: curricula, research, and partnerships. Their work reinforces the view that curriculum integration does not happen alone but interacts with research priorities and external relationships. Abo-Khalil (2024), drawing on international and United Arab Emirates cases, likewise highlights #interdisciplinary approaches and active faculty involvement as essential ingredients, and notes the influence of global initiatives such as impact rankings in encouraging SDG-aligned change. Case studies show that the depth of integration varies widely even inside institutions that describe themselves as committed. A review of the curricula of a Saudi university found no elective or mandatory courses designed specifically for sustainability education and a low degree of integration across programmes, with the existing coverage focused mainly on environmental topics (Bataeineh and Aga, 2023). A study of business students in the United Arab Emirates found high awareness of the goal on responsible consumption and production but limited competence in applying its principles to real business situations (Elmassri et al., 2025). The authors describe this as a paradox: students know that sustainability matters, but they have not been taught how to act on it within their field. These examples point to a recurring pattern in the literature. The SDGs are often visible in institutional communication and in selected activities, but they are much less visible in the graded learning outcomes of core courses. Where integration does occur, it tends to cluster in disciplines that already have an obvious link to environmental or social questions, leaving other disciplines largely untouched. 2.3 Extracurricular and elective provision: strengths and limits Extracurricular activities and elective courses have real value. They allow motivated students to go deeper, they can be launched quickly without rewriting whole programmes, and they often involve creative teaching, community projects, and partnerships with external organisations. Many students first meet sustainability ideas through such activities. Leal Filho, Trevisan, et al. (2024), in a survey of 602 students from 53 countries combined with bibliometric analysis and case studies, found that student participation in SDG activities was associated with the level of commitment of their institution. Importantly, having taken a course or discipline related to the SDGs played a significant role in students' involvement in implementing the goals. This suggests that formal curriculum contact is not just one option among many but a key driver of engagement. The limits of optional provision are also clear in recent work. Sidiropoulos (2022), in a mixed-methods study of tertiary students, concluded that sustainability education often remains on the fringes of mainstream curricula and is delivered on an ad hoc basis. Across five connected studies, the research found resistance to sustainability education, limited empowerment and sometimes even disempowerment, and a focus on personal behaviour change rather than professional action. Crucially, cumulative and deeper learning occurred when sustainability education was repeated and connected to the student's own world. The author judged the current ad hoc approach to be ineffective in creating widespread agents for change. Domingues et al. (2025) studied #project_based_learning with business partners at a United Kingdom university. They found that such projects significantly improved students' sustainability knowledge and competencies. They also reported that design choices shaped the outcomes, including whether activities were integrated into the curriculum or run as extracurricular activities, whether projects were bespoke or ad hoc, and how long students were exposed to sustainability topics. This is one of the clearest recent signals that the structural position of an activity, inside or outside the curriculum, affects what students gain from it. 2.4 Global citizenship education in higher education #Global_citizenship_education (GCE) shares much with #education_for_sustainable_development. Both aim to help learners understand global interdependence, think critically about power and inequality, and act responsibly. Massaro (2022), in a systematic review of 57 empirical studies, found that global citizenship enters higher education through measurement scales, #study_abroad, coursework, university programmes, and research on staff and student perceptions. The review showed how varied, and sometimes vague, the concept can be in practice. Student understanding of the term is limited. Pownall, Birtill, and Harris (2024) surveyed 202 undergraduate students in the United Kingdom and found that only 12.87 percent had come across the term global citizenship education, although many gave reasonable definitions when asked. Students saw it as important but raised concerns about how it would be put into practice at subject level. In a systematic review of sustainability and global citizenship education in the Gulf Cooperation Council countries, Amin, Zaman, and Tok (2023) found that most studies focused on students, faculty, and curricula inside institutions, and that the challenges reported in the Gulf were strikingly similar to those reported in Europe. They read this as a sign of a deeper structural problem within modern education systems. These findings matter for the present article because global citizenship, like sustainability, is frequently pushed into optional or co-curricular spaces such as exchange programmes and volunteering. If only a minority of students take part in those activities, then a majority may graduate without structured opportunities to develop global awareness and responsibility. 2.5 Competence frameworks as a bridge to the core curriculum A major development of recent years is the effort to define what sustainability learning should produce in terms of competencies. Brundiers et al. (2021) proposed an agreed-upon reference framework of key competencies in sustainability for higher education, including systems thinking, #futures_thinking, values thinking, strategic thinking, and interpersonal competence, together with an integrated problem-solving competence that combines them. Redman and Wiek (2021) refined this work and showed how competencies for advancing transformations towards sustainability can be described and taught. In Europe, the GreenComp framework sets out twelve sustainability competences grouped into four areas: embodying sustainability values, embracing complexity in sustainability, envisioning sustainable futures, and acting for sustainability (Bianchi, Pisiotis, and Cabrera Giraldez, 2022). UNESCO (2024) has also published guidance on greening the curriculum, which encourages climate and sustainability content to be built into subjects across the whole curriculum rather than treated as a separate topic. These frameworks are important for the integration debate because they make it possible to write sustainability into the learning outcomes of ordinary courses. A statistics course can develop systems thinking by analysing environmental data. A law course can develop values thinking by examining the rights of future generations. A design course can develop futures thinking by imagining products for a low-carbon economy. Competencies offer a common language that lets different disciplines contribute to shared goals without losing their own identity. 2.6 Research gap The literature makes a strong case that integration is desirable, and it describes many examples and barriers. What it does less well is directly compare the effectiveness of different structural models. Many studies are single case studies, many rely on student self-reports, and few follow students over time. There is therefore a need to bring the scattered evidence together and to ask, as directly as possible, what changes when sustainability and global citizenship move from the margins to the core. This article addresses that gap through a framework that allows different studies to be read against a common set of criteria. 3. Conceptual Framework: The Integration Ladder To compare very different kinds of provision, this article uses a simple conceptual framework called the integration ladder. It is built from the patterns described in recent studies of curriculum change (Weiss, Barth, and von Wehrden, 2021), from the whole-institution argument (Kohl et al., 2022; Price et al., 2024), and from competence-based approaches to #sustainability_education (Brundiers et al., 2021; Bianchi, Pisiotis, and Cabrera Giraldez, 2022). The ladder does not claim that every institution must climb in strict order. It is a thinking tool that helps students, teachers, and managers describe where a programme sits today and what would change if it moved up a step. Figure 1 sets out the six levels. Figure 1. The integration ladder: six levels of curriculum integration for the SDGs and global citizenship. 3.1 The six levels Level 0, absent, describes a programme in which students have no planned contact with the SDGs or with global citizenship. Some students may meet these ideas by chance, for example through a guest lecture or the news, but nothing in the design of the degree ensures it. Level 1, extracurricular, describes provision that sits outside the credit-bearing curriculum: student societies, awareness weeks, volunteering, competitions, and campus events. These activities are usually optional and unassessed. They can be inspiring and they often build community, but they depend on student free time and interest. Level 2, elective, describes a stand-alone optional course on sustainability, the SDGs, or global issues. Such a course carries credit and assessment, which gives it more weight than a club, but it reaches only those who choose it. In practice these are often students who were already interested, which means the students who most need new perspectives may be the least likely to take it. Level 3, compulsory stand-alone, describes a single required course, often in the first year, that all students in a programme or university must take. This guarantees reach, but the course can feel isolated from the rest of the degree. Students may treat it as a box to tick, and teachers of other courses may assume that sustainability has been covered and need not be mentioned again. Level 4, embedded and cross-cutting, describes a curriculum in which SDG-related and global citizenship learning outcomes appear in the core courses of every discipline and are revisited at different stages of the degree. Here sustainability is not a separate subject but a lens through which the discipline itself is studied. A finance student meets it in investment appraisal, a civil engineering student in materials choice and life-cycle thinking, a nursing student in the environmental footprint of care and in health inequality, and a computing student in the energy demands and social effects of digital systems. Level 5, whole-institution, describes a situation in which embedded curriculum is supported and reinforced by research priorities, campus operations, governance, staff development, and partnerships with the community. Students see the university practising what it teaches. This level corresponds to the whole-institution approach advocated by Kohl et al. (2022) and evaluated in practice by Price et al. (2024). 3.2 Five criteria for judging effectiveness The framework uses five criteria to judge how effective each level is likely to be. Each criterion is drawn from findings that recur in the literature reviewed above. Reach asks what proportion of students actually receive the learning. Optional provision reaches only some students, while compulsory and embedded provision can reach all. Depth asks whether students move beyond awareness to understanding and competence. Elmassri et al. (2025) show that awareness can be high while competence remains low, so depth must be considered separately from reach. Continuity asks whether learning is repeated and built up over time. Sidiropoulos (2022) found that cumulative learning occurred with repetition, which suggests that one-off exposure is weaker than revisited exposure. Assessment asks whether the learning is formally assessed against clear outcomes. What is assessed tends to be taken seriously by students, and assessment also creates evidence that learning has occurred (Sanchez-Carracedo et al., 2021). Institutional sustainability asks whether the provision survives changes in staff, leadership, and funding. Weiss, Barth, and von Wehrden (2021) found that isolated initiatives and limited institutional change were among the patterns of curriculum change, and these are the patterns most vulnerable to collapse when a champion leaves. 3.3 How the framework links sustainability and global citizenship The ladder is used here for both sustainability and global citizenship because the two share structural problems. Both are interdisciplinary, both deal with values as well as facts, and both are easy to label as soft or optional. Both also benefit from #experiential_learning, from real-world problems, and from contact with people whose lives and perspectives differ from the student's own. Treating them together allows the article to ask a broader question about how universities handle cross-cutting themes that do not belong to any single department. 4. Method This article is a structured narrative review. It was designed to answer an evaluative question across a field where studies differ greatly in design, so a narrative synthesis organised by a framework was judged more suitable than a statistical meta-analysis. 4.1 Search and selection Searches were carried out in large scholarly indexes covering peer-reviewed literature across disciplines. Search terms combined phrases such as Sustainable Development Goals, education for sustainable development, global citizenship education, curriculum integration, embedding sustainability, extracurricular, elective, compulsory, whole-institution approach, and higher education. The search focused on work published from 2021 onwards, in line with the aim of using recent evidence. A small number of slightly older foundational sources were considered only where they remain the main reference for a concept. Studies were included if they reported on the integration of sustainability, the SDGs, or global citizenship into higher education curricula, or on barriers and conditions for such integration, and if they offered empirical evidence, systematic review findings, or an established framework. Studies focused only on campus operations without a teaching dimension were excluded. Every source used was checked against publisher or registry records to confirm authors, year, journal, and identifiers. 4.2 Analysis Each study was read for its setting, its design, the type of provision it described, the outcomes it measured, and its main conclusions. Studies were then placed on the integration ladder according to the type of provision they examined and assessed against the five criteria of reach, depth, continuity, assessment, and institutional sustainability. Findings were grouped into themes, which form the subsections of the analysis below. 4.3 Limits of the method A narrative review depends on the judgement of the reviewer and cannot give precise effect sizes. The included studies come from many countries and disciplines, but the evidence base is still weighted towards Europe and towards a small number of fields such as engineering, business, and health. Many studies rely on self-reported outcomes. These limits are discussed further in the conclusion, and they are part of the reason the article ends with a research agenda. 5. Analysis and Discussion 5.1 Reach: who actually learns about the SDGs The first and most basic difference between models is reach. Extracurricular and elective provision is, by definition, chosen. This choice creates a self-selection effect. Students who join a sustainability society or choose a sustainability elective tend to be those who already care about the issues. Their gains may be real, but they are gains for a minority. The scale of the reach problem is visible in recent data. Pownall, Birtill, and Harris (2024) found that only 12.87 percent of surveyed undergraduates had met the term global citizenship education, even in a national system where the idea features in university strategies. Leal, Azeiteiro, and Aleixo (2024), surveying 444 teachers in Portuguese public higher education, found that only 16 percent believed sustainable development was holistically integrated across their institutions' activities, while about 30 percent reported integrating it largely or extensively in their own courses. If roughly a third of teachers integrate sustainability deeply, then a student's exposure depends heavily on which teachers they happen to meet. By contrast, structured approaches can change reach quickly. Price et al. (2024) analysed a whole-institution strategy at a United Kingdom university. Within the first year of the strategy, education for sustainable development was embedded in 42 percent of courses and in progress in a further 7 percent, and #Carbon_Literacy training was embedded in almost a fifth of courses. More than 80 percent of students agreed that their course gave them opportunities to gain knowledge and skills related to sustainable development. Figure 3, later in this section, places some of these figures side by side. The contrast is clear: optional provision leaves reach to chance, while structural integration makes reach a design decision. Reach also has an equity dimension. Students who work long hours, care for family members, or commute long distances are less able to join voluntary activities. Students in demanding professional programmes may have little room for electives. When sustainability and #global_awareness are left in optional spaces, they become, in effect, privileges for those with time and flexibility. Embedding them in core courses is one way of making sure that all students, not just the most advantaged, receive this preparation. This is an argument from #educational_equity as much as from effectiveness. 5.2 Depth: from awareness to competence Reach alone is not enough. A curriculum could mention the SDGs in every course and still leave students with nothing more than a list of slogans. The second criterion, depth, asks whether students develop real understanding and the ability to act. The evidence suggests that one-off and optional exposure mostly raises awareness. Angelaki et al. (2024) studied an intervention in information and computer technology education at a Greek university, using two questionnaires two weeks apart around lectures on sustainability. Students began with a weak understanding of the SDGs, and the intervention significantly increased their knowledge and their intention to engage. This is encouraging, but it measured short-term change in knowledge and intention, not lasting professional competence. Elmassri et al. (2025) offer a sharper warning. Their business students showed high awareness of responsible consumption and production yet limited competence in applying those principles to business contexts. The students divided into idealists and pragmatists, suggesting that awareness without structured practice can leave students unsure how to reconcile sustainability with the demands of their field. The authors call for pedagogical innovation and interdisciplinary integration to close this gap. Depth appears to grow when sustainability is connected to the student's discipline and to real problems. Domingues et al. (2025) found that project-based learning with businesses improved not only knowledge and competencies but also skills, attitudes, and behaviours. Dixon et al. (2024), working in dentistry and oral health education, found that staff and students saw environmental sustainability as relevant to all areas of their discipline and wanted baseline knowledge combined with practical application. They developed 44 evidence-based content statements and mapped them to 19 subjects in the curriculum. This kind of mapping illustrates what depth looks like in practice: sustainability is not a separate lecture but part of how each topic in the discipline is taught. The competence frameworks discussed earlier help to explain why integration supports depth. Competencies such as #systems_thinking, futures thinking, and strategic thinking are not learned by hearing about them once. They are built through repeated practice on problems of increasing difficulty, which is exactly what a well-designed core curriculum provides (Brundiers et al., 2021; Redman and Wiek, 2021). A stand-alone elective can introduce these competencies, but only the core curriculum can give students the many opportunities they need to practise them in the context of their future work. 5.3 Continuity: why repetition matters The third criterion, continuity, is closely linked to depth but deserves separate attention. One of the most consistent findings in recent research is that learning for sustainability deepens when it is revisited. Sidiropoulos (2022) reported that cumulative and deeper learning occurred with repetition of sustainability education and a greater connection to the student's own life. Domingues et al. (2025) identified the duration of exposure to sustainability topics as one of the factors that shaped outcomes. Continuity is the weak point of both extracurricular activity and the compulsory stand-alone course. A club may run for a semester and fade when its leaders graduate. A first-year compulsory course can give everyone a common starting point, but if later courses never return to the theme, students may conclude that it was a general education requirement with no bearing on their real subject. In both cases, learning is likely to decay. Embedded provision, by contrast, can be designed as a spiral. A first-year course introduces basic concepts and the goals themselves. A second-year course applies them to discipline-specific methods. A third-year project or #capstone asks students to address a complex, real problem in which environmental, social, and economic factors interact. Postgraduate study can then move to leadership and change. This spiral structure is familiar to curriculum designers in other areas, and it fits well with the developmental nature of sustainability competencies. 5.4 Assessment: what gets measured gets learned Students are strategic learners. They allocate time and effort according to what is assessed and how much it counts. This simple fact has large consequences for sustainability and global citizenship. If these themes appear only in unassessed activities, many students will rationally give them low priority, however much they value them in principle. Integration into core courses allows sustainability to be written into assessed learning outcomes. Sanchez-Carracedo et al. (2021) developed three tools for this purpose in engineering education. The first, an engineering sustainability map, lists sustainability-related learning outcomes that every engineering graduate should achieve, grouped under four competencies: critical contextualisation of knowledge, sustainable use of resources, participation in community processes, and application of ethical principles. The second, a sustainability presence map, shows how much each competency is actually present in a given degree. The third is a questionnaire that measures how much students feel they have learned. Comparing the presence map with student perceptions offers a first step towards evaluating whether embedding is working. Tools of this kind are important because they turn integration from a vague intention into something that can be checked. They also help teachers see where their own course fits in a larger picture. Ahmad et al. (2023) describe a similar effort in the CoDesignS framework, which supports curriculum designers in making sustainability explicit in course design and in developing cognitive, socio-emotional, and behavioural competencies. Their evaluation found that stakeholders saw the framework as easy to use and capable of supporting integration across disciplines. However, the assessment question also exposes a weakness in the current evidence. Many studies evaluate integration through student perceptions and self-reports rather than through assessed performance. Students may report that they have learned a great deal while being unable to apply their learning, as the findings of Elmassri et al. (2025) suggest. Effective integration therefore requires #authentic_assessment tasks, such as projects, case analyses, and design briefs, in which students must use sustainability competencies to produce real work. Figure 2 summarises the differences between optional and embedded models across the main features discussed so far. Figure 2. Comparison of extracurricular or elective provision with embedded core provision across seven features. 5.5 Institutional sustainability: will it last? The fifth criterion asks whether provision survives over time. Weiss, Barth, and von Wehrden (2021) analysed 131 international case studies of curriculum change and identified six patterns: collaborative paradigm change, bottom-up evolving institutional change, top-down mandated institutional change, externally driven initiatives, isolated initiatives, and limited institutional change. Their analysis showed that more comprehensive implementation was linked to open communication among stakeholders, a shared vision, active participation, careful planning, and balanced responsibilities. Strong informal collaboration could partly make up for weak formal leadership support. Isolated initiatives, which closely resemble many extracurricular and elective offerings, are vulnerable. They depend on one or two enthusiastic individuals. When those individuals move on, burn out, or lose funding, the initiative often ends. Embedded provision written into programme specifications, learning outcomes, and quality assurance documents is much harder to remove. It becomes part of what the degree is. Price et al. (2024) drew two broad principles from their experience that they argued apply regardless of context: demonstrating that sustainability adds value to academic activity, and using consultation and co-creation to build a shared vision and support for change. These principles echo the findings of Weiss and colleagues and suggest that durable integration is as much a social and political process inside the university as a technical exercise in curriculum writing. Figure 3. Selected reported indicators of reach and integration from recent studies (Pownall, Birtill and Harris, 2024; Leal, Azeiteiro and Aleixo, 2024; Price et al., 2024). Values come from different samples and are shown for illustration, not direct comparison. Figure 3 should be read with care, because each bar comes from a different study, country, and population. Its value lies in the pattern it suggests. Where integration is left to individual initiative, low figures appear: few students have met the idea of global citizenship education, few teachers see holistic integration, and few receive regular training. Where an institution adopts a deliberate whole-institution strategy, a much larger share of courses can be changed within a short time. 5.6 Staff capacity: the hidden engine of integration If integration means that every teacher in every discipline contributes to sustainability and global citizenship learning, then the capacity of teaching staff becomes the decisive factor. Here the evidence points to a serious and widely shared problem. Leal, Azeiteiro, and Aleixo (2024) found that only 20 percent of the Portuguese teachers they surveyed said their institutions provided regular or systematic training in sustainable development. Almost 90 percent were concerned about climate change or the environment, but only 40 percent or fewer took part in related activities. Concern, in other words, was not matched by preparation or practice. Leal Filho, Simaens, et al. (2023) similarly identified a shortage of training opportunities on the SDGs for university staff across many countries. This matters because embedding asks teachers to do something many were never trained to do. A chemist or an economist may be an expert in their field and still feel unsure how to connect it to the SDGs, how to handle value-laden classroom discussions, or how to assess competencies such as #critical_thinking about global problems. Without support, teachers may add a single slide about the goals to a lecture and consider the job done. That is integration in name only. Barriers are not only about skills. Turner et al. (2024) studied an attempt to embed interdisciplinary learning in the first year of undergraduate study across institutions. Although there were many drivers, including professional bodies and staff advocates, these were overwhelmed by administrative and ideological barriers. Timetabling, credit structures, departmental boundaries, and beliefs about what a discipline should contain all worked against change. Because sustainability and global citizenship are inherently interdisciplinary, the same barriers are likely to apply to them. Evidence from specific disciplines tells the same story. Dixon et al. (2024) noted that staff and students in oral health education wanted sustainability normalised as part of everyday teaching, but this required looking at existing teaching and assessment through a new lens rather than simply adding content. Abo-Khalil (2024) also highlighted active faculty involvement as essential. The common lesson is that structural integration succeeds only when universities invest in #faculty_development, give staff time to redesign courses, and reward teaching innovation alongside research. 5.7 Disciplinary differences and the risk of shallow integration Not all disciplines start from the same place. The scoping review by Amoros Molina et al. (2023) found that SDG integration was concentrated in engineering and technology, humanities and social sciences, and business and economics. Some fields, such as environmental science, already have sustainability at their centre. Others, such as pure mathematics or classical languages, may struggle to see the link. This unevenness creates two risks. The first is that integration remains concentrated in a few faculties while others do nothing, which reproduces the reach problem at a different scale. The second is shallow integration, where institutions label existing content with SDG icons without changing what students learn. #Curriculum_mapping exercises can encourage this if they reward the number of goals mentioned rather than the quality of learning. Bataeineh and Aga (2023) found that where sustainability did appear in a university's courses, it was mainly environmental, with less attention to social and economic dimensions. A curriculum that mentions climate but ignores inequality, governance, and #social_justice does not reflect the integrated nature of the SDGs. Avoiding these risks requires each discipline to find its own authentic entry points. Mathematics can contribute through modelling, data, and uncertainty. Literature and history can contribute through narratives of injustice, colonialism, resilience, and change. Law can examine environmental regulation, human rights, and corporate accountability. Medicine and nursing can examine #planetary_health, access to care, and the carbon footprint of health systems. Computing can examine the energy use of data centres, algorithmic bias, and digital inclusion. When integration grows from the questions a discipline already asks, it is more likely to be deep and lasting than when it is imposed as a list of goals to tick. 5.8 Global citizenship inside the disciplines Global citizenship faces a particular challenge. It is often associated with mobility programmes, international events, and language learning, all of which are optional and, in many cases, costly. Massaro (2022) found that study abroad was one of the main ways in which global citizenship development has been studied in higher education. Yet only a minority of students worldwide can study abroad. Embedding global citizenship in core courses offers a fairer route. It can take the form of comparative case studies, international collaborative online projects, readings from authors in different regions, and discussion of how a discipline's knowledge has been shaped by history and power. Pownall, Birtill, and Harris (2024) found that students valued global citizenship education but worried about how it would be applied at subject level. This concern is exactly what discipline-based integration addresses: it shows students what global citizenship means for a psychologist, an engineer, or an accountant, rather than leaving it as an abstract ideal. There is also a critical point to make. Global citizenship can be taught in ways that are uncritical, presenting the world as a level playing field and the student as a benevolent helper. Amin, Zaman, and Tok (2023) suggest that the similar challenges seen across regions point to deeper structural and epistemological issues in modern education. Integration that is serious about global citizenship should therefore include #critical_reflection on inequality, on whose knowledge counts, and on the responsibilities that come with privilege. This is harder to do in a one-hour event than in a course that returns to these questions over time. 5.9 The continuing role of extracurricular and elective activities None of this means that extracurricular and elective activities should disappear. The evidence suggests a different conclusion: they work best as a complement to embedded provision, not as a substitute for it. Leal Filho, Trevisan, et al. (2024) found that student participation in SDG activities was linked both to institutional commitment and to having taken a related course. This suggests a reinforcing cycle in which formal learning prompts engagement and engagement deepens learning. Optional activities provide spaces for #student_agency, leadership, and experimentation that are hard to create inside assessed courses. Student societies can organise campaigns, run campus projects, and connect with local communities. Electives can offer advanced specialisation for students who want to make sustainability a career focus. Araneo (2024), studying the content of stand-alone courses on education for sustainable development across several universities and the views of 737 students, identified seven categories of curricular themes that students valued, including science-based content, contextually relevant material, and hope for the future. Such courses can act as laboratories for teaching approaches that are later spread across the wider curriculum. The key shift is in the role these activities play. In a marginal model, they carry the whole responsibility for sustainability learning, and most students never meet them. In an integrated model, every student builds a foundation in core courses, and optional activities offer further depth and practical opportunities for those who want them. 5.10 Evaluating structural integration in practice If structural integration is to be judged fairly, universities need better ways of evaluating it. The studies reviewed suggest a cycle of six steps, shown in Figure 4. First, institutions should map their current curriculum to see where the SDGs and global citizenship already appear and where they are missing. Second, they should write clear, discipline-specific learning outcomes for core courses. Third, they should train and support academic staff. Fourth, they should teach and assess through real problems in each discipline. Fifth, they should evaluate student competence and not only awareness. Sixth, they should revise outcomes, courses, and support in light of the evidence. At the centre of the cycle sit leadership, policy, and resources, without which the cycle stalls. Figure 4. A six-step cycle for implementing and evaluating structural integration, supported by leadership, policy and resources. The framework for implementing the SDGs in university programmes proposed by Leal Filho, Frankenberger, et al. (2021) offers detailed guidance that fits this cycle. The tools developed by Sanchez-Carracedo et al. (2021) support the mapping and evaluation steps. The CoDesignS framework (Ahmad et al., 2023) supports the design step. What is often missing in practice is the fifth step. Most evaluations stop at student satisfaction or self-reported learning. Stronger designs would compare cohorts before and after integration, use performance-based assessments of #sustainability_competencies, and follow graduates into employment to see whether their learning affects professional practice. 5.11 A regional lens: lessons from the Gulf and the wider Middle East Much of the published evidence comes from Europe, but recent studies from the Gulf and the wider Middle East add useful perspective. Countries in this region are investing heavily in higher education as part of national plans for economic diversification and knowledge-based growth, and sustainability features strongly in these plans. Amin, Zaman, and Tok (2023) note that education for sustainable development, and to a lesser extent global citizenship education, have become part of this shift in the Gulf Cooperation Council states. Yet their review also found that the barriers reported in the region closely resembled those reported elsewhere, including limited teacher preparation and gaps between policy ambition and classroom practice. Case evidence from the region illustrates the integration ladder in action. The Saudi university studied by Bataeineh and Aga (2023) appears to sit near the lower levels, with no dedicated courses and low integration across programmes despite national commitments under Vision 2030. The United Arab Emirates study by Elmassri et al. (2025) shows students with high awareness but limited applied competence, a pattern typical of provision that informs without fully embedding. Abo-Khalil (2024), discussing cases from the United Arab Emirates alongside international examples, points to interdisciplinary approaches and faculty involvement as the levers that move institutions upwards. The regional lens is useful for students everywhere because it shows that structural integration is not only a concern of wealthy Western universities. Rapidly growing systems have an unusual opportunity: new programmes and new campuses can build sustainability and global citizenship into their design from the start, rather than retrofitting them later. Where curricula are being written for the first time, it is far easier to place the SDGs in core learning outcomes than to persuade established departments to change long-standing courses. 5.12 A worked illustration: one degree, four years To make the idea of structural integration concrete, consider how it might look in a single undergraduate business degree. This is an illustration of the principles discussed above, not a report of a particular programme. In the first year, a compulsory introduction to management includes a unit on the SDGs and the idea of stakeholders, assessed through a short case analysis of a local firm. An introductory economics course uses examples of externalities such as pollution and congestion. An academic skills course asks students to evaluate sources on a contested sustainability claim, building information literacy and critical judgement at the same time. In the second year, accounting students learn the basics of sustainability reporting alongside financial reporting. Marketing students examine misleading environmental claims and the ethics of persuasion. Operations students map a supply chain and identify social and environmental risks. Each of these courses carries an assessed learning outcome connected to a named sustainability competence, such as systems thinking or values thinking. In the third year, a finance course examines how climate risk affects investment decisions, and a human resources course examines fair work, diversity, and inclusion across countries. Students take part in an online collaborative project with peers from a university in another region, which builds global citizenship through real interaction rather than abstract discussion. In the final year, a capstone project asks student teams to work with a real organisation on a problem that combines financial, social, and environmental dimensions. The project is assessed on the quality of analysis, the realism of recommendations, and the students' ability to justify trade-offs. Alongside this core, students can still choose an advanced elective in sustainable finance or join a student consultancy club that works with local charities. In this model, no single course carries the whole burden. Every student meets the SDGs repeatedly, in the language and methods of their own discipline, and each encounter builds on the last. That is the practical meaning of a cross-cutting theme. 6. Implications for Practice 6.1 For students Students are not passive recipients of curriculum decisions. They can look at their programme specifications and course outlines to see whether the SDGs and global citizenship appear in learning outcomes or only in marketing. They can ask teachers how a topic connects to sustainability and global issues, which often encourages teachers to make the link explicit. They can use assignments, dissertations, and projects to explore sustainability questions in their own discipline. They can join student representative structures and push for integration as a matter of educational quality and fairness. And they can use extracurricular activities to build leadership and practical skills on top of the foundation their degree provides. In short, students can practise #active_citizenship within their own university. 6.2 For teachers For teachers, the evidence suggests starting small but thinking structurally. A single course can be redesigned to include one authentic sustainability problem, with an assessment task that requires students to apply discipline knowledge to it. Teachers can work with colleagues to agree on how the theme develops across years, so that students meet it repeatedly at increasing levels of complexity. Competence frameworks such as GreenComp (Bianchi, Pisiotis, and Cabrera Giraldez, 2022) and the key competencies proposed by Brundiers et al. (2021) provide ready-made language for writing learning outcomes. UNESCO (2024) guidance on greening the curriculum offers further examples. Teachers should also feel entitled to ask their institutions for training, time, and recognition. 6.3 For university leaders and policy makers For leaders, the clearest message from the evidence is that integration cannot be achieved by encouragement alone. It needs strategy, structures, and resources. That means writing sustainability and global citizenship into #graduate_attributes and programme approval rules, funding staff development, giving curriculum teams time to redesign courses, and building evaluation into quality assurance. The experience reported by Price et al. (2024) shows that rapid progress is possible when a strategy is clear and co-created with staff and students. The patterns identified by Weiss, Barth, and von Wehrden (2021) show that sustainable change combines top-down support with bottom-up ownership. Leaders should also resist the temptation to measure success by the number of SDG labels attached to courses, and should instead ask what students can now do that they could not do before. National policy makers and accreditation bodies can support this by including sustainability competencies in #quality_assurance standards and professional requirements. 7. Conclusion This article set out to evaluate whether moving sustainability and global citizenship from extracurricular and elective spaces into the core curriculum of all disciplines makes education more effective. Reading recent evidence through the integration ladder and five criteria of reach, depth, continuity, assessment, and institutional sustainability, it reaches a clear overall judgement. Extracurricular and elective provision has value, but on its own it reaches too few students, rarely builds lasting competence, and depends on fragile individual effort. Structural integration, in which sustainability and global citizenship become cross-cutting themes revisited and assessed across every degree, offers much better prospects for reach, depth, and durability. The evidence also shows that integration is not automatic. It fails when it is reduced to labelling, when teachers are not trained or given time, when administrative structures block interdisciplinary work, and when evaluation stops at awareness. It succeeds when institutions combine curriculum mapping, discipline-specific learning outcomes, staff development, authentic assessment, and leadership support within a whole-institution approach. Optional activities remain important as complements that give motivated students further depth and opportunities for leadership. 7.1 Limitations This review has several limitations. It is a narrative review, so its conclusions rely on interpretation rather than statistical pooling. The evidence base is dominated by studies from Europe and from a small number of disciplines, and many studies are single-institution case studies. Most measure student awareness, attitudes, or self-reported learning rather than demonstrated competence, and very few follow students beyond graduation. The figures shown in this article come from different samples and cannot be compared directly. These limitations do not undermine the overall direction of the findings, which is consistent across very different settings, but they do mean that the size of the benefit from integration is still uncertain. 7.2 Directions for future research Future research should compare students in programmes at different levels of the integration ladder using the same outcome measures. It should use performance-based assessments of sustainability competencies rather than relying only on surveys. It should follow graduates into work to see how their education shapes professional decisions. It should give more attention to disciplines and regions that are under-represented, including many universities in Africa, Asia, Latin America, and the Middle East. And it should examine how students from different backgrounds experience integration, so that efforts to embed sustainability also advance fairness. The SDGs set a deadline of 2030, which is now very close. Whatever happens to the goals after that date, the problems they describe will remain. The students now in university classrooms will spend their careers dealing with them. Giving every one of those students, in every discipline, the knowledge, skills, and values to act responsibly is not an optional extra for universities. It is part of what a good education now means. References Abo-Khalil, A. G. (2024). Integrating sustainability into higher education challenges and opportunities for universities worldwide. Heliyon, 10(9), e29946. https://doi.org/10.1016/j.heliyon.2024.e29946 Ahmad, N., Toro-Troconis, M., Ibahrine, M., Armour, R., Tait, V., Reedy, K., Malevicius, R., Dale, V., Tasler, N., & Inzolia, Y. (2023). CoDesignS education for sustainable development: A framework for embedding education for sustainable development in curriculum design. 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Frontiers in Education, 6, 785163. https://doi.org/10.3389/feduc.2021.785163 Sanchez-Carracedo, F., Segalas, J., Bueno, G., Busquets, P., Climent, J., Galofre, V. G., Lazzarini, B., Lopez, D., Martin, C., Minano, R., Saez de Camara, E., Sureda, B., Tejedor, G., & Vidal, E. (2021). Tools for embedding and assessing sustainable development goals in engineering education. Sustainability, 13(21), 12154. https://doi.org/10.3390/su132112154 Sidiropoulos, E. (2022). The influence of higher education on student learning and agency for sustainability transition. Sustainability, 14(5), 3098. https://doi.org/10.3390/su14053098 Turner, R., Cotton, D., Morrison, D., & Kneale, P. (2024). Embedding interdisciplinary learning into the first-year undergraduate curriculum: Drivers and barriers in a cross-institutional enhancement project. Teaching in Higher Education, 29(4), 1092-1108. https://doi.org/10.1080/13562517.2022.2056834 UNESCO. (2024). Greening curriculum guidance: Teaching and learning for climate action. UNESCO. https://doi.org/10.54675/AOOZ1758 Weiss, M., Barth, M., & von Wehrden, H. (2021). The patterns of curriculum change processes that embed sustainability in higher education institutions. Sustainability Science, 16(5), 1579-1593. https://doi.org/10.1007/s11625-021-00984-1 Zaleniene, I., & Pereira, P. (2021). Higher education for sustainability: A global perspective. Geography and Sustainability, 2(2), 99-106. https://doi.org/10.1016/j.geosus.2021.05.001 #SDGs_in_higher_education #sustainable_curriculum #embedding_sustainability #ESD #GCED #UN_SDGs #sustainability_in_education #future_ready_graduates #global_learning #education_reform #university_curriculum #transformative_learning

Universities are under growing pressure to offer learning in smaller, more flexible units that adults can take while working and later combine into larger qualifications. Micro-credentials and stackable degrees are the most visible answer to this pressure, yet the practical question of how an existing degree can be taken apart and rebuilt without losing its coherence has received less attention than the policy debate around it. This article examines that question. Drawing on recent systematic reviews, empirical studies of employer views, labor market evaluations of credential stacking in the United States, and critical work from the sociology of curriculum, it develops a seven-layer framework for curriculum deconstruction that moves from demand signals through outcome decomposition, sizing, assessment, credit and recognition, and stacking rules to lifelong learning outcomes. The analysis identifies four broad unbundling strategies, ranging from stand-alone badges to fully integrated stacking, and argues that the value of a micro-credential depends less on its size than on how firmly it is tied to recognized awards. Evidence suggests modest but real earnings gains from stacking, strong but conditional employer interest, and persistent problems of definition, quality assurance, and equity. The article concludes that unbundling works best when it is treated as curriculum design rather than as marketing, and it offers practical guidance for students, academic staff, and institutional leaders. Keywords: micro-credentials, stackable credentials, curriculum design, unbundling, lifelong learning, higher education, labor market, recognition of learning 1. Introduction For most of the last century, a university degree worked like a sealed package. A student enrolled at around eighteen, followed a fixed sequence of courses for three or four years, and left with a single award that was meant to last a working life. That model still dominates, and it still has real strengths. But the conditions around it have changed. People now change jobs and even occupations several times, #technological_change reshapes tasks within a few years, and many #adult_learners need new knowledge long after graduation without being able to stop working for years at a time. In this setting, the idea of #lifelong_learning has moved from a slogan in policy documents to a practical problem that universities must solve. One response has been to break study into smaller pieces. Short, focused, assessed units of learning, usually called #micro_credentials, certify a specific set of #skills or knowledge. When these units are designed so that they can be combined over time into a certificate, a diploma, or a full degree, they become #stackable_credentials. In theory, this gives learners many doors into higher education and many doors out of it, each with something useful in hand. Recent reviews describe a rapid growth of interest in micro-credentials across Europe, Australasia, North America, and Asia, and they note that institutions, employers, and governments each expect different things from them (Varadarajan et al., 2023; Ahsan et al., 2023). Much of the writing on micro-credentials focuses on definitions, policy frameworks, and #digital_badges. These questions matter, but they leave a gap. A university that wants to offer stackable learning usually does not start from an empty page. It starts from existing degree programs, with their own learning outcomes, course structures, assessment habits, and accreditation rules. The real work is to take that existing structure apart and put it back together in a new form. This process is often called #unbundling, and it is the main subject of this article. The article asks three questions. First, what does it actually mean to deconstruct a curriculum into stackable modules, and what decisions does the process involve? Second, what does recent evidence say about the value of such modules to learners, employers, and institutions? Third, what risks come with unbundling, and how might careful design reduce them? The argument is that unbundling succeeds when it is treated as a serious act of #curriculum_design, in which coherence, assessment, and recognition are rebuilt deliberately, rather than as a way of cutting existing courses into sellable pieces. The article is written for #university_students as well as for staff. Students are increasingly the people who must navigate a world of badges, certificates, and nested awards, and they benefit from understanding how these offerings are built and what makes some of them more valuable than others. The discussion therefore keeps its language plain while following the structure of a research article: a review of recent literature, a conceptual framework, a thematic analysis, and a conclusion that names implications and limits. 1.1 Approach and scope This is an integrative, conceptual study rather than an empirical one. It does not report new data. Instead, it brings together findings from recent peer-reviewed work on micro-credentials and stackable credentials, most of it published between 2021 and 2026, and uses them to build and test a design framework. Sources were located through scholarly databases and checked against their publisher records before use. Priority was given to three kinds of work: systematic and integrative reviews, which summarize a wide field; empirical studies of employers, learners, and labor market outcomes, which show what happens in practice; and critical studies, which question the assumptions behind the trend. Combining these three kinds of work is deliberate. Reviews alone tend to describe potential rather than results. Labor market studies give precise numbers but usually concern short vocational credentials in one country. Critical studies raise important questions about knowledge and fairness but often say less about practical design. Reading them together allows a more balanced picture, in which enthusiasm is tested against evidence and critique is translated into design principles. The scope is limited in two ways. First, the focus is on higher education institutions that already offer degrees and want to rebuild part of their provision into stackable form. Commercial platforms and professional bodies are discussed only where they shed light on university practice. Second, the article treats curriculum as its main object. Technology, finance, and national regulation are discussed where they shape curriculum decisions, but they are not examined in their own right. Readers interested in the technical standards behind digital records, or in national funding rules, will need to look further. A note on language is also useful. Different countries use different words for similar things. A module in one system is a course or unit in another; a diploma may sit below or above a degree depending on the country. This article uses module and unit to mean a defined block of teaching with its own outcomes and assessment, certificate and diploma to mean intermediate awards below a full degree, and degree to mean a bachelor or master award. Where a cited study uses different terms, its meaning is explained in context. 2. Background and Literature Review 2.1 What counts as a micro-credential There is still no single agreed definition of a micro-credential. Brown et al. (2021) describe a crowded and confusing landscape in which badges, certificates, nanodegrees, short courses, and professional certifications all claim the label. They argue that the field is best understood as a new #credential_ecology for lifelong learning, in which different kinds of awards sit alongside each other rather than one replacing another. Most definitions share a few elements: the learning is short compared with a degree, it is focused on a defined set of outcomes, it is assessed, and it leads to a record that can be shared and checked. In Europe, the Council of the #European_Union adopted a recommendation on a European approach to micro-credentials in 2022, which encouraged member states to describe micro-credentials with common elements such as learning outcomes, workload, level, form of assessment, and quality assurance. This kind of standard description is central to the stacking problem, because two units can only be combined meaningfully if they are described in comparable terms. Several of the reviews discussed below note that the lack of a shared definition still makes recognition and accumulation harder, but the broad direction of policy is toward common descriptors rather than away from them. Size is one of the most contested features. Some micro-credentials represent a few hours of study, while others carry the credit of a full semester course. Berkling et al. (2023), writing from a German university, describe typical micro-credentials of 1 to 3 credits under the #ECTS system, where one credit corresponds to roughly thirty hours of student work. In their model, a group or stack of micro-credentials corresponds to one standard module of 3 to 12 credits. This is a useful anchor for students: a micro-credential is usually a fraction of a normal course, not a replacement for a degree. 2.2 Why institutions are interested Several reviews explain why universities have become interested in this area. Varadarajan et al. (2023) found that learners want short, practical, and current courses linked to a career path; institutions stress accreditation as a way to build trust; employers want clarity about what competencies a credential represents; and governments hope for higher #graduate_employability at lower cost. These expectations overlap but do not match, and the review concludes that closer cooperation among the groups is needed to manage the tensions. Olcott (2022) describes the situation in the United States as a strategic reset. Graduates who struggle to find suitable work, employers who cannot find people with the skills they need, rising student debt, and competition from new providers have together pushed colleges to rethink what credentials they offer. McGreal and Olcott (2022) add an important caution for university leaders: micro-credentials are not a cure for every institutional problem and are unlikely to become a large source of revenue. Their value, in this view, lies in how they connect to an institution's wider mission and existing programs. Tamoliune et al. (2023), reviewing research published up to early 2022, place the potential of micro-credentials in three contexts. In the economic context, they help people #upskill, #reskill, and enter the labor market. In the social context, they support lifelong learning through flexible personal pathways. In the higher education context, they extend the assessment and #recognition_of_prior_learning and introduce stackable credits. That last point is directly relevant here, because it shows that stackability is not a side feature but one of the main reasons micro-credentials are taken seriously by universities. 2.3 Implementation studies Ahsan et al. (2023) reviewed 56 peer-reviewed articles on micro-credentials and digital badges in higher education published between 2015 and 2021. They found growing research attention but noted that many studies were small, that technology was often studied separately from teaching, and that the views of different stakeholders were rarely compared. They proposed a conceptual framework for implementation and called for more research on how institutions actually put micro-credentials into practice. Selvaratnam and Sankey (2021), in an integrative review focused on Australasia, similarly emphasized that implementation raises practical questions about how units are designed, how they are recognized, and how they relate to existing qualifications. Pirkkalainen et al. (2023) used a Delphi study with education experts to look five to ten years ahead. The experts identified several possible futures for micro-credentials and agreed that their strongest potential lay in institutions and networks of institutions innovating both inside and beyond traditional programs. However, they judged that wide adoption was hard to predict because it depended on national and international policy and technology choices that individual universities cannot control. A key finding was that a #one_size_fits_all approach is neither necessary nor optimal; institutions and learners do not need to gain the same benefits for adoption to succeed. More recent work has turned to the idea of unbundling directly. Dang and Tang (2026), using Hong Kong as a case, describe how micro-credentials unbundle higher education and argue for an ecosystem in which learners curate their own journeys, providers design modular and stackable offerings, regulators build quality assurance and recognition frameworks, and facilitators make the system accessible at scale. Gamage and Dehideniya (2025), in a narrative review, highlight collaboration between universities and industry, including joint design of programs, as one of the main ways micro-credentials can bridge study and work. 2.4 Critical perspectives Not all scholars welcome these developments. Wheelahan and Moodie (2021), drawing on the sociology of Basil Bernstein, argue that micro-credentials extend #human_capital_theory into the curriculum. In their reading, the learner imagined by micro-credentials is a market self who buys this or that skill set in anticipation of what employers will want, and the curriculum is reshaped to fit that picture. In a second paper, Wheelahan and Moodie (2022) describe micro-credentials as gig qualifications for the #gig_economy. They argue that unbundling the curriculum can blur the line between public and private provision, shift the cost of job preparation from governments and employers onto individuals, and tie universities more tightly to the needs of particular jobs rather than whole occupations. Desmarchelier and Cary (2022), writing from Australia, focus on justice and equity. They ask who actually benefits from micro-credentials and warn that, without deliberate design, short and fee-paying units may favor people who already have degrees, time, and money. These critiques are important for this article because they show that unbundling is not a neutral technical act. How a degree is taken apart shapes what kind of knowledge survives, who can afford to learn, and what kind of graduate is produced. 2.5 Evidence from stackable credential research A separate body of research, mainly from the United States, studies stackable credentials in #community_colleges, where short certificates have long been combined into associate degrees. This work is valuable because it uses large administrative datasets and follows learners over time. Bozick et al. (2021), using fifteen years of records from public institutions in Ohio, found that most certificate holders re-enrolled to continue toward further credentials. Re-enrollment became less likely the longer people waited, and it was faster among those who earned their first certificate at a community college, those on low wages, and those facing rising local unemployment. Anderson and Daugherty (2023) studied what happened when Ohio community colleges introduced new certificate or associate programs within an existing technical field. Students whose college offered an additional program in their field were more likely to re-enroll and earn another credential within two years, and this extra study did not reduce their employment or their transfer to universities. Meyer et al. (2025) and Lenard and Wright-Kim (2026) estimated the earnings effects of stacking for working adults in Virginia and New Jersey and found positive returns, discussed in detail in Section 4. Soliz (2023), reviewing the wider literature on short technical credentials, concluded that returns are significant on average but vary by field and location, and that long-term evidence is still limited. Taken together, the literature shows strong interest, some encouraging evidence, and many open questions. What is missing is a clear account of the design work that sits between an existing degree and a working stackable system. The next section offers a framework for that work. 2.6 Key terms for students Because the vocabulary in this area can be confusing, it helps to set out the main terms in plain words before moving on. A micro-credential is a record that a person has completed a short, focused, and assessed piece of learning. A digital badge is one common way of displaying that record online, usually with information about what was learned and who issued it attached to the image. A stackable credential is any credential designed so that it can be combined with others to build toward a larger award. Credit is the currency that makes combination possible. In many systems, one credit stands for a set amount of student work, and a degree requires a fixed total. A credential that is credit-bearing can therefore be counted toward a degree, while one that is not credit-bearing usually cannot. Level describes how demanding the learning is, for example introductory, intermediate, or advanced, and is often linked to a national or regional #qualifications_framework. Recognition of prior learning means giving formal credit for knowledge and skills a person already has, whether gained through work, volunteering, or earlier study. Finally, unbundling describes the process of separating a degree into its parts so that they can be offered and recognized separately, and rebundling describes putting parts together again, sometimes in new combinations. The rest of this article is mainly about how to do both well. 3. Conceptual Framework: A Seven-Layer Model of Curriculum Deconstruction The framework developed here brings together ideas from the implementation reviews, the European descriptor approach, the community college research on pathways, and the critical literature. It treats curriculum deconstruction as a sequence of seven connected layers, each of which requires a deliberate decision. Figure 1 first shows the basic contrast between a bundled degree and an unbundled, stackable architecture, so that the purpose of the framework is clear. Figure 1. A bundled degree compared with an unbundled, stackable architecture in which small units build toward larger awards. In the bundled model on the left of Figure 1, value is released only at the end. A student who leaves after two years may have learned a great deal but holds no award that the labor market recognizes. In the stackable model on the right, value is released in stages. Each micro-credential is recognized on its own, groups of micro-credentials form a certificate or diploma, and these in turn count toward a degree. The design problem is to make sure that the parts are meaningful alone and also add up to a coherent whole. Figure 2 sets out the seven layers through which this can be done. Figure 2. A seven-layer framework for deconstructing a degree curriculum into stackable modules, with a review loop linking outcomes back to demand. 3.1 Layer one: demand signals Deconstruction starts with a question about purpose. Who will take these units, and why? Demand comes from two directions: the labor market, which signals what skills are scarce or changing, and learners, who bring their own career goals, constraints, and prior learning. Varadarajan et al. (2023) show that these signals are not the same as institutional priorities, which is why the first layer must be explicit. A university that skips this step risks cutting its curriculum along lines that suit its timetable rather than any real need. 3.2 Layer two: outcome decomposition The second layer breaks program-level #learning_outcomes into smaller competencies that can be taught and assessed separately. This is harder than it sounds. Some outcomes, such as applying a statistical test, divide naturally. Others, such as judging evidence critically or working ethically in a profession, develop slowly across many courses and resist being reduced to a single unit. Berkling et al. (2023) addressed this by first rewriting skill descriptions in a standard form, using the Dublin descriptors to structure levels and the European ESCO vocabulary for skills, before splitting modules. This step of translating outcomes into a common language is what later makes stacking and recognition possible. 3.3 Layer three: granularity The third layer decides how big each unit should be. Smaller units are more flexible but risk fragmentation; larger units preserve depth but reduce flexibility. Granularity also affects cost, because every unit needs its own assessment and record. Raj et al. (2024) found that a mismatch between learning hours and learning outcomes, and an inadequate volume of learning, were among the most significant barriers to accepting micro-credentials as credible qualifications. Sizing is therefore not only a practical matter but a question of trust. 3.4 Layer four: assessment and evidence A micro-credential is only as good as the evidence behind it. The fourth layer designs assessment that shows clearly what a learner can do, ideally through authentic tasks, and records that evidence in a way others can check. Maina et al. (2022) offer a useful example. Their Employability Skills Micro-credentialing methodology, tested at three East African universities with 13 lecturers, 169 students, and 24 employers, linked a competency-based ePortfolio to a digital credentialing system. The study found that the approach helped students move from study to work, but also that it required rethinking teaching practice and curricula to embed employability skills. 3.5 Layer five: credit and recognition The fifth layer gives each unit a credit value, a level, and a place in the #quality_assurance system. Without this, a unit may be useful but cannot count toward anything else. Narayanaswamy et al. (2024) found that employers placed more value on micro-credentials that were transcripted, meaning they carried formal course credit, than on non-transcripted ones. Recognition is therefore a design decision with direct consequences for learners. 3.6 Layer six: stacking rules The sixth layer writes the rules for combination: which units can be stacked, in what order, with what prerequisites, and how credit from other providers or from prior learning can be counted. This is where the architecture of #learning_pathways is built. Poor stacking rules create dead ends; good ones create clear routes with several possible destinations. 3.7 Layer seven: lifelong learning outcomes and the review loop The final layer looks at what actually happens to learners: whether they progress to further awards, whether their employment or earnings change, and whether they return to study later. The evidence gathered here should feed back into the first layer, which is why Figure 2 includes a review loop. A stackable system is not built once. It must be revised as labor markets change and as data show which pathways learners actually use. The framework is not a rigid procedure. Institutions will move between layers and revisit earlier decisions. Its value is to make visible the decisions that are often made implicitly, or not made at all, when a degree is cut into smaller units. 4. Analysis and Discussion This section uses the framework to examine five themes that emerge from the literature: strategies of unbundling, the architecture of stacking, labor market evidence, employer recognition, and the risks of fragmentation and inequality. A final subsection considers the student perspective. 4.1 Four strategies of unbundling Institutions do not all unbundle in the same way. Two dimensions help to sort the main approaches. The first is granularity: how finely the curriculum is divided. The second is integration: how firmly the resulting units are tied to recognized full awards. Combining these gives four broad strategies, shown in Figure 3. Figure 3. Four unbundling strategies defined by granularity and by integration with full awards. The first strategy, stand-alone badges, divides learning finely but links it weakly to awards. A university might issue digital badges for short workshops or skill units that are recognized on a CV but do not count toward any qualification. This is quick to set up and can be useful for visibility, but it does little for learners who want to build toward a degree. The second strategy, repackaged courses, takes existing semester-length courses and offers them separately to external learners with little thought to how they connect. This is common because it is cheap, but it rarely changes the curriculum and may confuse learners about what they are buying. The third strategy, nested certificates, keeps units fairly large but builds them into the structure of a degree, so that the first year of a program might also be awarded as a certificate. This preserves coherence and supports exit points but offers less flexibility. The fourth strategy, integrated stacking, divides learning finely and ties every unit to full awards through clear credit and stacking rules. This is the most demanding approach and the one closest to the ideal described by Dang and Tang (2026) and by Tamoliune et al. (2023). It requires work at every layer of the framework. The main point of Figure 3 is that size alone does not determine value. A very small unit can be valuable if it is firmly integrated, and a large unit can be of limited use if it leads nowhere. The literature on employer views, discussed below, supports this conclusion: employers tend to trust micro-credentials more when they carry formal credit and come from recognized institutions (Narayanaswamy et al., 2024). Berkling et al. (2023) provide a concrete example of the integrated approach in computer science. Their process involved two main steps. First, they rewrote the skill descriptions of existing modules in a standard form, using European reference vocabularies. Second, they broke modules of 3 to 12 credits into smaller units of 1 to 3 credits, with the rule that a defined stack of units corresponds to one original module. This design has a clear advantage: a learner who completes all units in a stack has, in effect, completed the original module and can receive credit for it toward the degree. The original structure is preserved, but it now has more entry and exit points. Ward et al. (2024) go further in computing education with what they call a universal micro-credential framework. Their approach uses skills profiling, badge catalogues, and design patterns to build personalized learning and assessment paths, including content that crosses disciplinary boundaries. They describe a persistent gap between the capabilities learners develop in education and the competencies employers look for, and they present flexible credentialing as a way to narrow that gap. Their work shows that integrated stacking is not only about splitting what already exists but can also enable new combinations that a fixed degree structure would not allow. Which strategy should an institution choose? The answer depends on the field and on the learners it wants to serve. In fields with clear professional roles and licensing, such as health, nested certificates and integrated stacking fit well, because the labor market already recognizes defined competencies and the evidence on returns is strongest there. In fields where the value of study lies mainly in broad intellectual development, such as philosophy or history, fine-grained unbundling may damage what makes the program worthwhile, and nested certificates or carefully chosen stand-alone units may be more appropriate. Many institutions will sensibly use more than one strategy at the same time, matching the approach to the character of each program rather than applying a single model across the whole university. 4.2 The architecture of stacking: pathways, ramps, and prior learning A stackable system is defined less by its units than by the routes between them. Figure 4 shows a typical vertical pathway, from micro-credentials through a short certificate and a diploma or associate award to a bachelor or master degree. The important features are the ramps. #Exit_ramps allow learners to leave with a usable award at each stage. #Re_entry_ramps allow them to return later and keep the credit they have already earned. At each entry point, learning gained at work or elsewhere can be recognized. Figure 4. A vertical stacking pathway with exit ramps to work and re-entry ramps back into study. The research from Ohio helps explain why these ramps matter. Bozick et al. (2021) found that most certificate holders did return to study, but the chance of returning fell the longer they stayed away. This suggests that pathways should make the next step visible and easy soon after a learner earns a first credential. Re-enrollment was also faster among people on low wages and during periods of rising local unemployment, which suggests that learners use stacking partly as a response to labor market pressure. A well-designed system should expect this and make it simple to return when circumstances change. Anderson and Daugherty (2023) add a design lesson. When colleges introduced additional programs within an established technical field, students who had just completed a credential were more likely to continue and earn another. In other words, the presence of a clear next step within the same field encouraged progression. This supports the idea, built into Layer six of the framework, that stacking rules should create visible routes rather than leave learners to assemble pathways alone. Pathways need not lead only upward within one institution. Barselai-Shaham and Yaish (2024) studied a pathway in Israel in which short-cycle vocational tertiary programs serve as a first step toward an academic degree. They found the route was especially popular among high-achieving students from lower social backgrounds, who appeared to use it to reduce the risk of failing in a full academic program. This finding is significant for curriculum designers. It shows that stackable routes can widen access for some groups, not because the content is easier, but because the risk of each step is smaller and each stage leaves the learner with something of value. Recognition of prior learning is the third element of the architecture. Many adults returning to study already have skills from work. If a stackable system cannot recognize these, it forces learners to repeat what they know. Tamoliune et al. (2023) identify the assessment and recognition of non-formal and informal learning as one of the main contributions micro-credentials can make in higher education. In practice, this means that the outcome decomposition in Layer two must be precise enough that a learner's existing evidence can be matched against specific competencies. 4.3 Labor market evidence Does stacking actually help people in the labor market? The most rigorous evidence currently comes from studies of sub-baccalaureate credentials in the United States. These studies cannot be transferred directly to university micro-credentials in other countries, but they are the best available evidence on what happens when people combine credentials over time. Figure 5 summarizes the earnings effects reported by two recent studies. Figure 5. Estimated quarterly earnings gains from credential stacking among adult learners. Data from Meyer et al. (2025) for Virginia and Lenard and Wright-Kim (2026) for New Jersey. Meyer et al. (2025) used administrative data on enrollment and employment in Virginia and compared the same individuals before and after they stacked credentials. They found that stacking increased #employment by four percentage points and raised quarterly wages by about 375 dollars, or around four percent. Returns were larger for people studying in health fields and for those who returned to college after first completing a short certificate. Lenard and Wright-Kim (2026), studying adult workers in New Jersey across cohorts around the Great Recession, estimated that stacking raised earnings by about 700 dollars per quarter, or about six percent, and increased hours of work by about three percent. Benefits were larger for women, for Black learners, for those whose second credential was an associate degree, and for those in healthcare. Several points follow from this evidence. First, the gains are positive but modest. Stacking does not transform careers overnight, but it appears to improve them in measurable ways. Second, the gains depend on the field. #Healthcare, where credentials are tied to clear occupational roles and licensing, shows the strongest effects. This suggests that unbundling works best where the labor market already recognizes specific competencies. Third, the finding that returns are larger when the second credential is a more substantial award supports the argument made in Section 4.1: value comes from integration with recognized qualifications, not from small units alone. Soliz (2023) reaches a similar conclusion from a broader review of short technical credentials. Returns are significant on average but vary considerably by location and field, and most studies follow learners for only a short period. For university micro-credentials, the evidence base is thinner still. Recent reviews repeatedly note that most research on micro-credentials in higher education is descriptive and short-term, with few studies that follow learners over years (Ahsan et al., 2023; Varadarajan et al., 2023). Students should therefore be cautious about strong claims that any particular micro-credential will raise their income. There is also a critical reading of the same evidence. Wheelahan and Moodie (2022) argue that the growth of short credentials can shift the cost and risk of training onto individuals. Even if stacking raises earnings on average, a system in which workers must repeatedly pay for small units to stay employable may be less fair than one in which employers and governments share those costs. The labor market evidence does not settle this debate, but it does show that design choices, such as which fields are unbundled and how units connect to larger awards, affect who gains. 4.4 Employer recognition and trust A micro-credential is a #labor_market_signal. Its value depends on whether employers understand it and believe it. Recent empirical studies show a consistent pattern: employers are interested, but their trust is conditional. Miller and Jorre de St Jorre (2024) interviewed 22 environmental professionals in Australia about recruiting graduates. They found that employers use many sources of evidence when hiring and value alternative forms of information about candidates. There was strong enthusiasm for a micro-credential case study presented to them, but employers wanted more context about how such credentials work and confidence in the standards and rigor behind them. Bruguera et al. (2025), drawing on 85 interviews with labor market stakeholders, found that employers saw online micro-credentials as a feasible and flexible way for professionals to train, including in #soft_skills, but stressed that such units must be practical and closely resemble real-life situations. Alasmari (2024) surveyed 124 human resource professionals and found that many saw micro-credentials as a way to strengthen a #CV and support personal skill-building, while some recruiters questioned their legitimacy and saw them as informal. Narayanaswamy et al. (2024) found that employers actually valued micro-credentials more than students did, but that they used the source of the credential, meaning the organization that issued and branded it, to judge its worth. Employers also valued university micro-credentials more when they carried transcripted course credit. Awareness is a further problem. Zhang and Alasmari (2025), studying 178 students and 18 employers in Saudi Arabia, found low awareness of micro-credentials in both groups, with a moderate positive link between awareness and perceived value. Their study is especially relevant to the #Gulf_region, where national strategies place heavy emphasis on workforce development. It suggests that even well-designed micro-credentials may be undervalued if employers and students do not understand them, and it recommends closer partnerships between universities and industry and the creation of a national framework. Raj et al. (2024) ranked the barriers to accepting micro-credentials as credible qualifications. The top-ranked barriers included the lack of clear definitions, ambiguous course descriptions, lack of accreditation and quality assurance, unclear policies on how micro-credentials affect pay, poor alignment between learning hours and outcomes, too little volume of learning, and a general lack of acceptance by individuals and organizations. Almost every item on this list maps onto a layer of the framework in Figure 2. Clear definitions and descriptions belong to Layers two and five, alignment of hours and outcomes to Layer three, and accreditation to Layer five. This suggests that many of the trust problems reported by employers are, at root, design problems that institutions can address. For curriculum designers, the lesson is that a micro-credential must carry its meaning with it. A short description that states learning outcomes, level, workload, assessment method, and credit value allows an employer to understand what the credential represents without knowing the institution well. Maina et al. (2022) showed that involving employers in the recognition process itself increased the visibility, transparency, and reliability of credentials. #Co_design with industry, emphasized by Gamage and Dehideniya (2025), can strengthen this further, as long as the academic integrity of the unit is protected. 4.5 Risks: fragmentation, narrowing, and inequality Unbundling carries real risks, and a responsible account must take them seriously. The first is #fragmentation. A degree is more than the sum of its courses. It includes sequencing, in which later courses build on earlier ones, and integration, in which students learn to connect ideas across subjects. When a curriculum is cut into small units that can be taken in any order, these qualities can disappear. Wheelahan and Moodie (2021), drawing on Bernstein, describe how disciplinary knowledge depends on systematic relations between concepts. Units designed only around discrete workplace tasks may give learners pieces of knowledge without the structure that makes those pieces meaningful and transferable. The framework in this article addresses fragmentation in two ways. First, outcome decomposition in Layer two should identify outcomes that cannot be divided, such as critical judgment or research skills, and protect them. One practical method is to build a #capstone or integrative unit into each stack, which requires learners to combine what they have learned. Second, stacking rules in Layer six can require prerequisites or a particular order where the knowledge structure demands it. Flexibility should be offered where it does not damage coherence, not everywhere by default. The second risk is #narrowing. If units are designed only to meet immediate employer requests, the curriculum may lose its broader educational purpose. Wheelahan and Moodie (2022) argue that education should prepare people to live lives they have reason to value, not just to perform specific jobs. Specific skills also date quickly. A unit built around one software tool may lose its value within a few years. Designers can reduce this risk by pairing applied skills with underlying principles in each unit, and by keeping some units focused on broad capabilities rather than narrow tasks. The third risk is #inequality. Micro-credentials are often presented as a way to widen access, and the Israeli evidence from Barselai-Shaham and Yaish (2024) shows that stackable routes can serve learners from lower social backgrounds. But Desmarchelier and Cary (2022) caution that without deliberate attention to justice, micro-credentials may mainly serve people who already hold degrees and can pay for extra study. Fees, digital access, time, and confidence all shape who takes part. The finding by Lenard and Wright-Kim (2026) that benefits were larger for women and Black learners is encouraging, but it reflects a particular US context and particular programs. Equity outcomes cannot be assumed; they must be designed for and measured. A fourth risk is institutional. McGreal and Olcott (2022) warn that micro-credentials are unlikely to generate large revenue, and Pirkkalainen et al. (2023) note that wider adoption depends on national and international policy that universities cannot control alone. Institutions that unbundle quickly in the hope of financial gain may find that the costs of assessment, record-keeping, and quality assurance for many small units exceed the income. This is another reason to treat unbundling as a strategic and educational decision rather than a commercial one. 4.6 Quality assurance, governance, and digital records Behind every trusted credential lies a system of rules that most learners never see. For full degrees, these rules are well established: programs are approved, reviewed, and accredited, and external examiners or similar mechanisms check standards. Micro-credentials disturb this system because they are smaller, more numerous, and often faster to create and change. A process designed to approve a four-year program every five years is poorly suited to a unit of two credits that may need updating every year. The literature suggests that this mismatch is one of the main obstacles to wider adoption. Raj et al. (2024) ranked the lack of accreditation and quality assurance among the most significant barriers to accepting micro-credentials. Varadarajan et al. (2023) found that institutions emphasize accreditation precisely because it builds trust. The challenge is to design quality processes that are proportionate: rigorous enough to protect standards, but light enough that small units can be approved and revised in reasonable time. One practical approach is to approve the stack rather than each unit alone. If a program team approves the overall architecture, including the outcomes of the full stack, the rules for combination, and the assessment strategy, then individual units can be updated within that approved frame without a full review each time. This mirrors the approach of Berkling et al. (2023), in which a stack of micro-credentials corresponds to an already approved module. The integrity of the larger award protects the smaller parts. #Governance also involves deciding who owns the units. In a traditional faculty, a department owns its courses. In a stackable system, units may be shared across programs, offered by continuing education divisions, or co-designed with employers. Clear rules about who can create, change, and retire units are needed to avoid duplication and confusion. Pirkkalainen et al. (2023) found that the strongest potential of micro-credentials lay in networks of institutions innovating together, which makes questions of shared ownership and mutual recognition even more important. Digital records are the final part of this infrastructure. A micro-credential is only portable if it can be shared and checked easily. Most current systems use digital badges or certificates that carry information about the issuer, the learning outcomes, and the assessment, and that can be verified online. The design point for curriculum teams is that the information placed in these records comes directly from the work done in Layers two to five of the framework. A badge cannot describe outcomes that were never clearly defined, and it cannot show credit that was never assigned. Good digital records are therefore a product of good curriculum design, not a substitute for it. For students, the practical advice is to keep records in a form they control and can share, to check that each record states its outcomes and credit clearly, and to be cautious of credentials that cannot be verified by a third party. As stackable systems grow, a well-organized personal record of learning may become as important as a traditional transcript. 4.7 What unbundling means for students For students, the move toward stackable learning brings both opportunity and responsibility. The opportunity is clear: more ways to enter study, more ways to leave with something useful, and more ways to return later. A working adult can take a single unit to test an interest, add further units over time, and eventually reach a full award without stopping work. A current degree student may also be able to earn recognized micro-credentials along the way, which can strengthen a CV before graduation. The responsibility lies in choosing well. Because the market is crowded, students need to ask practical questions before enrolling. Does this unit carry formal credit, and at what level? Does it count toward a larger award, and if so, which one? Who issues it, and is that organization recognized in the field I want to work in? How is it assessed, and will I have evidence I can show an employer? The research reviewed here suggests that units with clear credit, recognized issuers, authentic assessment, and defined stacking routes are more likely to be valued than units that lack these features (Narayanaswamy et al., 2024; Raj et al., 2024). Students should also think about the whole shape of their learning, not just its parts. A collection of unrelated badges may be less useful than a coherent stack that tells a clear story about what a person can do. In this sense, students in a stackable system take on part of the curriculum designer's role. They need to plan pathways, combine units sensibly, and look for opportunities to integrate what they learn. Institutions can help by offering #academic_advising and pathway maps, so that flexibility does not become confusion. Finally, students should remember the limits of the evidence. Studies of stacking show positive but modest returns, mostly in particular fields and countries. A micro-credential is not a guarantee of a job or a pay rise. It is one tool among others, and its value depends on how it fits with experience, other qualifications, and the needs of a particular labor market. 4.8 Practical guidance for institutions Drawing the analysis together, several practical principles emerge for institutions that want to unbundle their curricula responsibly. These are offered as guidance, not as a fixed recipe, since contexts differ widely. First, begin with purpose and demand. Before cutting any course, identify the learners the stackable offer is meant to serve and the labor market signals that justify it. Not every program needs to be unbundled, and some fields, especially those with clear occupational links such as health, appear to benefit more than others. Second, translate outcomes into a common language before dividing them. Using shared descriptors for level, outcomes, and workload, as Berkling et al. (2023) did, makes later stacking and recognition far easier. It also allows units to be compared with those of other institutions, which supports credit transfer. Third, protect coherence deliberately. Identify outcomes that cannot be split, keep prerequisites where knowledge structure requires them, and include integrative or capstone units in each stack. Flexibility is a design choice, not a default. Fourth, invest in assessment and evidence. #Authentic_assessment tasks, portfolios, and verifiable digital records give micro-credentials meaning outside the institution. Involving employers in defining and checking competencies can increase trust, as Maina et al. (2022) showed. Fifth, attach credit and build stacking rules from the start. The evidence suggests that units integrated with recognized awards are trusted more by employers and generate stronger returns when combined into larger qualifications. Stand-alone badges may have a place, but they should not be the whole strategy. Sixth, design for equity and measure it. Consider fees, scheduling, digital access, and advising support, and collect data on who enrolls, who progresses, and who benefits. Without such data, claims about widening access remain untested. Seventh, close the loop. Track what happens to learners and use that evidence to revise units, pathways, and the choice of fields. A stackable system is a living structure that must change as the labor market changes. 4.9 An illustrative walkthrough To show how the framework works in practice, this subsection walks through a simplified and hypothetical example. It is not a case study of a real institution, and the numbers are chosen only to make the reasoning easy to follow. Imagine a university that offers a two-year master degree in business analytics worth 120 credits, mainly to full-time students, and that wants to make part of the program available to working professionals in stackable form. At Layer one, the program team gathers demand signals. Conversations with regional employers, job advertisement analysis, and enquiries from alumni suggest strong interest in three areas: data visualization and reporting, applied statistics for decision making, and the management of analytics projects. Working professionals say they cannot attend full time and want to see results within a few months. This tells the team which parts of the curriculum to unbundle first and what kind of schedule to offer. At Layer two, the team examines the outcomes of the existing courses that cover these areas. A 10-credit course in data visualization, for example, has outcomes about choosing appropriate chart types, building dashboards with standard software, communicating findings to non-specialist audiences, and evaluating visualizations for honesty and accuracy. The team rewrites these outcomes in a common format with clear levels. It notices that the last outcome, about honesty and accuracy, depends on statistical understanding taught elsewhere, so it marks that outcome as one that should not be separated from its foundation. At Layer three, the team decides on size. It splits the 10-credit course into three units: a 3-credit unit on chart selection and design principles, a 4-credit unit on dashboard building, and a 3-credit unit on communicating and critically evaluating data stories. Each unit has its own outcomes and can be completed in about six to eight weeks of part-time study. Together they equal the original course, which follows the logic used by Berkling et al. (2023). At Layer four, the team designs assessment. Instead of a single exam at the end of the original course, each unit now has an authentic task. Learners in the dashboard unit, for example, build a working dashboard from a realistic dataset and write a short explanation of their design choices. The work is stored in a portfolio that the learner keeps. This change improves the evidence available to employers and also, the team discovers, improves the full-time course, which adopts the same tasks. At Layer five, each unit receives its credit value and level, and the approval committee reviews the stack as a whole, as described in Section 4.6. Learners who complete a unit receive a verifiable digital record listing the outcomes, credit, level, and assessment method. At Layer six, the team writes the stacking rules. The three visualization units together count as the original 10-credit course. Completing the visualization stack plus parallel stacks in applied statistics and analytics project management, totaling 30 credits, earns a postgraduate certificate. The certificate counts fully toward the master degree if the learner later enrolls, and learners with relevant work experience can apply to have up to one unit recognized through prior learning. The critical evaluation unit has a prerequisite in introductory statistics, which protects the outcome identified at Layer two. At Layer seven, the team plans to track enrollment, completion, progression to the certificate and the degree, and, where possible, changes in learners' roles. After two years, it reviews the data. Suppose it finds that many learners complete the visualization stack but few continue to the certificate. The team might then ask whether the next step is visible enough, whether scheduling is a barrier, or whether learners are satisfied with a single stack. These questions feed back into Layer one, completing the loop. This simplified example shows two things. First, every layer involves a real decision with consequences; skipping one creates problems later. Second, unbundling done carefully can improve the original degree as well as create new options. Better-defined outcomes, more authentic assessment, and clearer structure benefit full-time students too. In this sense, deconstructing a curriculum can be a way of understanding it better. 5. Conclusion This article has examined how traditional degree curricula can be deconstructed into flexible, stackable modules that serve lifelong learning and a changing labor market. Its central argument is that unbundling is fundamentally an act of curriculum design. Taking a degree apart is easy; putting it back together in a form that is coherent, trusted, and fair is difficult. The seven-layer framework proposed here, moving from demand signals through outcome decomposition, granularity, assessment, credit and recognition, and stacking rules to lifelong outcomes, is intended to make that difficulty visible and manageable. Three main findings stand out. First, the value of a micro-credential depends more on its integration with recognized awards than on its size. Units that carry credit, come from recognized issuers, and count toward larger qualifications are trusted more by employers and appear to deliver stronger returns. Second, the available labor market evidence, mainly from the United States, shows that stacking credentials produces modest but real gains in earnings and employment, especially in fields such as health with clear occupational structures. Third, unbundling carries real risks of fragmentation, narrowing, and inequality, which critical scholars have rightly highlighted and which careful design can reduce but not eliminate. The implications differ for each group. For students, the message is to choose units that are clearly described, credit-bearing, and part of a defined pathway, and to think about the overall shape of their learning. For academic staff, it is to treat unbundling as a chance to rethink outcomes and assessment rather than simply dividing existing courses. For institutional leaders and policymakers, it is to invest in shared descriptors, quality assurance, recognition of prior learning, and equity monitoring, and to resist the expectation that micro-credentials will solve financial problems. This article has limits. It is a conceptual and integrative study based on published research rather than new empirical data. Much of the strongest evidence on stacking comes from community colleges in the United States and may not transfer directly to universities elsewhere, including in the Gulf region and other contexts where micro-credential systems are still young. The literature on university micro-credentials remains largely descriptive and short-term. Future research should follow learners over many years, compare different unbundling strategies within the same institutions, and examine who benefits and who is left out. Until such evidence exists, the best course is to design stackable systems carefully, measure what they do, and keep the broader purposes of higher education in view. 5.1 A research agenda Several questions deserve priority in future work. The first concerns long-term outcomes. Most studies of university micro-credentials observe learners for months rather than years. Longitudinal studies that follow people from a first unit through later study and work would show whether stacking leads to lasting progression or mainly to short-term gains. The administrative data approaches used by Meyer et al. (2025) and Lenard and Wright-Kim (2026) offer a model that could be adapted for universities in other countries, where suitable data exist. The second question concerns comparison of strategies. Institutions often run different unbundling approaches side by side without evaluating them. Studies that compare stand-alone badges, repackaged courses, nested certificates, and integrated stacking within similar programs would help identify which designs serve which learners best. The framework in this article could provide a common structure for such comparisons, since each layer names a decision whose effects can be observed. The third question concerns knowledge and coherence. The critique offered by Wheelahan and Moodie (2021) raises the possibility that unbundled learning produces a different and possibly thinner kind of knowledge than a full degree. This claim can be studied empirically, for example by comparing how learners who reach a degree through stacked units and those who follow a traditional path perform on integrative tasks. Such research would move the debate beyond argument toward evidence. The fourth question concerns equity in different regions. Much of the current evidence comes from North America, Europe, and Australasia. Studies in the Gulf, Africa, South Asia, and Latin America, such as the work of Zhang and Alasmari (2025) and Maina et al. (2022), are growing but remain few. Since labor markets, funding systems, and cultural attitudes toward degrees differ widely, findings from one region cannot simply be assumed to hold in another. References Ahsan, K., Akbar, S., Kam, B., and Abdulrahman, M. D.-A. (2023). Implementation of micro-credentials in higher education: A systematic literature review. Education and Information Technologies, 28(10), 13505-13540. https://doi.org/10.1007/s10639-023-11739-z Alasmari, T. (2024). Reshaping vocational training: A study on the recognition of micro-credentials in job markets. Education and Training, 66(2/3), 233-251. https://doi.org/10.1108/ET-07-2023-0282 Anderson, D. M., and Daugherty, L. (2023). Community colleges can increase credential stacking by introducing new programs within established technical pathways. 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Achieving professional qualifications using micro-credentials: A case of small packages and big challenges in higher education. International Journal of Educational Management, 38(4), 916-947. https://doi.org/10.1108/IJEM-01-2023-0028 Selvaratnam, R. M., and Sankey, M. (2021). An integrative literature review of the implementation of micro-credentials in higher education: Implications for practice in Australasia. Journal of Teaching and Learning for Graduate Employability, 12(1), 1-17. https://doi.org/10.21153/jtlge2021vol12no1art942 Soliz, A. (2023). Career and technical education at community colleges: A review of the literature. AERA Open, 9. https://doi.org/10.1177/23328584231186618 Tamoliune, G., Greenspon, R., Tereseviciene, M., Volungeviciene, A., Trepule, E., and Dauksiene, E. (2023). Exploring the potential of micro-credentials: A systematic literature review. Frontiers in Education, 7, 1006811. https://doi.org/10.3389/feduc.2022.1006811 Varadarajan, S., Koh, J. H. L., and Daniel, B. K. (2023). A systematic review of the opportunities and challenges of micro-credentials for multiple stakeholders: Learners, employers, higher education institutions and government. International Journal of Educational Technology in Higher Education, 20, Article 13. https://doi.org/10.1186/s41239-023-00381-x Ward, R., Grant, S., Workmon Larsen, M., and Giovacchini, K. (2024). The universal micro-credential framework: The role of badges, micro-credentials, skills profiling, and design patterns in developing interdisciplinary learning and assessment paths for computing education. IEEE Transactions on Education, 67(6), 897-906. https://doi.org/10.1109/TE.2024.3486016 Wheelahan, L., and Moodie, G. (2021). Analysing micro-credentials in higher education: A Bernsteinian analysis. Journal of Curriculum Studies, 53(2), 212-228. https://doi.org/10.1080/00220272.2021.1887358 Wheelahan, L., and Moodie, G. (2022). Gig qualifications for the gig economy: Micro-credentials and the hungry mile. Higher Education, 83(6), 1279-1295. https://doi.org/10.1007/s10734-021-00742-3 Zhang, K., and Alasmari, T. (2025). Micro-credentials in Saudi higher education: Stakeholder perceptions and policy implications for economic transformation and bridging skills gaps. Journal of Professional Capital and Community, 10(4), 481-502. https://doi.org/10.1108/JPCC-10-2024-0171 #MicroCredentials #stackable_degrees #unbundling_higher_education #modular_curriculum #lifelong_learning_pathways #future_of_work #upskilling #reskilling #flexible_learning #higher_education_reform #credential_stacking #skills_based_learning

Learning Management Systems have become the everyday infrastructure of university study. Students log in to find readings, submit assignments, check grades, take quizzes, and receive reminders, often several times a day. Most discussion of these platforms focuses on what they make possible: access, flexibility, and efficiency. This article looks at something less visible. Drawing on the long tradition of hidden curriculum research, it asks what students learn from the platform itself, beyond the content their teachers place inside it. The article uses a critical integrative review of recent scholarship on digital education platforms, learning analytics, algorithmic bias, interface design, and student experience, published mainly between 2021 and 2026. It proposes a three-layer framework that links interface design, interaction models, and algorithmic processes to the implicit lessons they carry. The analysis identifies six hidden lessons: that learning is the completion of visible tasks, that time is measured in deadlines and countdowns, that being seen equals being engaged, that comparison with peers is a natural measure of worth, that surveillance is a normal condition of study, and that the default user is a fully able, well-connected, culturally prepared student. The article argues that these lessons are neither inevitable nor entirely negative, but that they should be made visible so that students, teachers, and institutions can question them. It closes with practical recommendations for each group and suggests directions for future empirical research. Keywords: hidden curriculum, learning management systems, learning analytics, algorithmic bias, interface design, platform pedagogy, datafication, higher education 1. Introduction Ask a university student what they learned in a course and they will usually name the topics in the syllabus. Ask them what they learned from the course website and the question sounds odd. A website, after all, is just where the course lives. It holds the readings, the assignment links, the grade book, and the announcements. It seems to be a container, not a teacher. This article argues that the container teaches too. Every time a student opens a #Learning_Management_System, they meet a set of design choices that someone else made. A green tick appears when a module is complete. A red banner warns that an assignment is overdue. A progress bar fills up as pages are viewed. A dashboard shows how often the student has logged in compared with the class average. An automated email arrives when the system decides the student has been inactive for too long. None of these features appears in the course outline, and few teachers would describe them as part of their teaching. Yet each one carries a message about what counts as good study, what kind of student is valued, and how learning should feel. Education researchers have a name for lessons of this kind. The #hidden_curriculum refers to the values, norms, habits, and expectations that students absorb from the way schooling is organized, rather than from what is formally taught. The idea first developed in studies of physical classrooms, where researchers noticed that children learned punctuality, obedience, waiting in line, and deference to authority alongside reading and arithmetic. Recent work has shown that the concept remains useful in university settings, in professional programs, and in digital contexts (Sebele-Mpofu, 2024; Ferjan and Bernik, 2024). What has changed is the setting. For many students today, the most consistent and most frequently visited part of their university is not a lecture theatre or library but a platform. The growth of these platforms has been rapid. Systems such as Moodle, Canvas, Blackboard, Brightspace, and Google Classroom are now standard across higher education in most parts of the world, and the shift to #emergency_remote_teaching during the COVID-19 pandemic pushed many institutions to rely on them even more heavily. At the same time, these systems have changed in character. Early learning platforms were mostly repositories for files. Contemporary systems collect detailed records of student activity, feed that data into #learning_analytics tools, connect to third-party applications through software interfaces, and increasingly include automated or artificial intelligence features that sort, predict, and recommend (Perrotta et al., 2021; Holmes and Tuomi, 2022). Scholars now speak of the #platformization of education to describe this shift, in which commercial digital infrastructures come to shape how teaching and learning are organized (Decuypere et al., 2021; Kerssens and van Dijck, 2021). Much of the critical research on these developments has focused on institutions, policy, markets, and teachers. Researchers have examined how edtech companies extract value from data (Komljenovic, 2021), how investors imagine the future of higher education (Williamson and Komljenovic, 2023), how platforms challenge the professional autonomy of teachers (Kerssens and van Dijck, 2022), and how algorithmic systems are entering education policy (Gulson et al., 2022). This work is essential. However, it often speaks over the heads of students, who are the people who spend the most time inside these systems. Less attention has been given to the question that matters most for a student audience: what does daily life on the platform teach me about myself, about learning, and about what my university expects? This article addresses that gap. Its aim is to explain, in clear terms and with reference to recent research, how the design of Learning Management Systems carries an implicit curriculum, and what values, behaviors, and biases students may pick up from it without noticing. The article makes three contributions. First, it brings together scholarship from several fields that rarely speak to each other: critical platform studies, learning analytics, human-computer interaction, disability studies, and curriculum theory. Second, it offers a simple three-layer framework that students and teachers can use to examine any digital learning environment. Third, it identifies six specific hidden lessons and suggests practical ways to make them visible and open to discussion. The article does not argue that Learning Management Systems are harmful or that students should avoid them. Many of their features genuinely help, especially for students who work, care for family members, study at a distance, or need flexible access to materials. The point is rather that tools always shape the people who use them, and that this shaping is easier to manage when it is noticed. As Selwyn (2022) puts it in his broader study of education and technology, the important questions are not only whether a technology works, but what it does, for whom, and with what consequences. The rest of the article is organized as follows. Section 2 reviews the literature on the hidden curriculum and on digital learning platforms. Section 3 sets out the conceptual framework. Section 4 explains the review approach. Section 5 presents the analysis of six hidden lessons. Section 6 discusses implications for students, teachers, institutions, and designers. Section 7 concludes with limitations and directions for future research. 2. Literature Review 2.1 The hidden curriculum: from classrooms to campuses The idea of a hidden curriculum rests on a simple observation: schools teach through their structures as well as their lessons. The timetable teaches that knowledge comes in fixed periods. The grading system teaches that learning can be ranked. The layout of the room teaches who speaks and who listens. Early researchers saw these structures as a way in which schooling prepared young people for the routines and hierarchies of adult working life. Later critical scholars went further, arguing that the hidden curriculum often reproduces social inequalities by rewarding the habits and cultural knowledge that some students bring from home and others do not. In higher education, the concept has been applied to the #unwritten_rules of academic life. Students are expected to know how to approach a lecturer, what a seminar is for, how to read an assessment brief, and how much independent work is normal. Those who arrive with family experience of university often know these things already. #first_generation_students, international students, and students from marginalized groups may have to discover them through trial and error. Dorrian (2026) provides a clear recent example. In a case study of distance learning, she found that students are rarely told what labels such as lecture, seminar, or tutorial actually mean or what kind of participation is expected, because institutions assume that everyone already knows. She calls the missing knowledge #learning_event_literacy and shows that making these expectations explicit can improve the match between what students expect and what they experience. Professional fields have also taken up the concept. In a review of accounting education in the digital era, Sebele-Mpofu (2024) argues that the design, delivery, and assessment methods of a program shape students' professional attitudes and values well beyond the formal content, and that new technologies both create fresh hidden lessons and offer ways to address old ones. Ferjan and Bernik (2024) make a related point about #digital_competence, examining how such competences are formed not only through formal instruction but also through the informal and implicit experiences that surround it. 2.2 The hidden curriculum in online and distance learning As study has moved online, researchers have begun to ask whether the hidden curriculum moves with it, and in what form. Some of the most important recent work comes from open and distance education, where the platform is often the main or only point of contact between student and institution. Kpum and Gasa (2026) introduce the concept of #digital_academic_orphanhood to describe African distance learners who are formally enrolled and technically connected, yet remain relationally and culturally excluded from the informal networks that support academic success. Their argument is important for this article because it shows that being inside the platform is not the same as belonging. A student can complete every task the system displays and still miss the tacit knowledge that circulates elsewhere. This work suggests that digital learning environments do not remove the hidden curriculum. They change where it lives. In a physical university, unwritten rules are passed on through corridors, office hours, and conversations after class. In a digital environment, many of these informal channels are thinner or absent, and the platform itself takes on a larger share of the work of signaling expectations. The interface becomes, in effect, the corridor. 2.3 Platforms are not neutral A second body of literature comes from critical studies of digital education platforms. Its central claim is that platforms are not neutral tools that simply carry content from teachers to students. Decuypere et al. (2021), introducing a special issue on the topic, argue that platforms actively reshape educational practices, relationships, and ways of knowing. They do this by deciding what can be done, what is recorded, what is displayed, and what is connected to what. Perrotta et al. (2021) give a detailed example in their study of Google Classroom. They show how the platform's automated functions and its connections to other software through application programming interfaces distribute the work of teaching across teachers, students, and software in new ways. They use the term #platform_pedagogy to describe the teaching logic that is built into the system. On this view, a platform comes with its own assumptions about how learning should be organized, and these assumptions travel into every classroom that uses it. Kerssens and van Dijck (2021, 2022) extend this argument in their research on Dutch education. They show how a small number of commercial platforms came to occupy a central position in schools, and they raise concerns about #pedagogical_autonomy, the ability of teachers and schools to make their own professional judgments about teaching when key decisions are already built into the software. Komljenovic (2021, 2022) adds an economic dimension. She argues that edtech companies increasingly operate as rentiers, earning ongoing income from controlling access to platforms and the data they generate, and that the more pressing concern in digitalized higher education may be how value is created and captured through these systems, rather than privacy alone. Williamson and Komljenovic (2023) show how investors' imagined futures for higher education, built around data and automation, help steer which products are developed and sold. For the purposes of this article, the key point is that the features students meet on a Learning Management System are the result of design decisions shaped by commercial, institutional, and technical pressures. These pressures are rarely visible to students, but their effects are built into the interface. 2.4 Datafication, analytics, and the student as data A third body of literature concerns #datafication, the process by which more and more aspects of education are turned into data that can be stored, counted, and analyzed. Learning Management Systems are among the richest sources of such data in universities. They can record when a student logs in, which pages they open, how long they stay, how many times they attempt a quiz, and when they submit work relative to the deadline. This data feeds learning analytics, a field that aims to use information about learners to understand and improve learning. Supporters of learning analytics point to its potential to identify students who are struggling and to give timely support. Critics and ethicists have raised concerns about consent, privacy, transparency, and fairness. A systematic review by Cerratto Pargman and McGrath (2021) found that empirical studies of learning analytics ethics have focused mostly on privacy, transparency, and consent, with less attention to other ethical issues. Prinsloo et al. (2022) argue that protecting #student_data_privacy cannot be solved by technical measures alone and requires attention to institutional power, culture, and the relationship between students and universities. Mutimukwe et al. (2022) developed a model of students' privacy concerns in learning analytics, showing that such concerns are linked to how students perceive risk, control, and trust in their institution. Khalil et al. (2023), introducing a special section on the topic, call for learning analytics to be judged against values of fairness, trust, transparency, equity, and responsibility. Gulson et al. (2022) place these developments in a wider frame. In their book on the algorithms of education, they show how datafication and artificial intelligence are reshaping not only classroom practice but also how education systems are governed. Selwyn et al. (2023a) raise a set of questions about the #automation_of_education, asking what exactly is being automated, who benefits, and what forms of human judgment are being displaced. 2.5 Dashboards, nudges, and the design of attention A fourth body of research looks at specific design features that students encounter. Student-facing #learning_analytics_dashboards are one of the most studied. These dashboards show students visual summaries of their activity and performance, sometimes with comparisons to their peers. Research on how students experience them is mixed. Rets et al. (2021), interviewing distance learners, found that students valued features that offered study recommendations, but that comparison with peers was among the least favored elements unless accompanied by qualitative information. Divjak et al. (2023), surveying students in two cohorts, found that students appreciated planning and organization features but were cautious about comparison and competition, which some found potentially demotivating. A systematic review by Paulsen and Lindsay (2024) notes a gradual move toward dashboards that are better informed by learning theory, but this also confirms that many earlier designs were driven more by what data was available than by what would help students learn. Aguilar (2023) argues that dashboards can send discouraging messages about performance, and that students from under-served groups may be especially sensitive to such messages, so designers need to consider their motivational effects carefully. Closely related is the use of #nudging, small changes in how choices are presented that aim to steer behavior without restricting options. Weijers et al. (2021) review the use of nudges in education and suggest guidelines for using them responsibly. Reminders, default settings, progress indicators, and social comparison messages are all forms of nudging that commonly appear in learning platforms. Research in human-computer interaction warns that the same techniques can slide into what are known as #dark_patterns, interface designs that steer users in ways that may not serve their interests. Mathur et al. (2021) analyze the attributes of such designs, including whether they are asymmetric, covert, deceptive, hide information, or restrict choice, and they connect these attributes to concerns about individual welfare, collective welfare, and autonomy. While their work focuses on commercial websites rather than education, the analytical tools they offer are useful for thinking about how learning platforms guide student behavior. 2.6 Algorithmic bias and exclusion A fifth body of work concerns bias. Baker and Hawn (2022) provide a comprehensive review of #algorithmic_bias in education. They show that predictive models used in education can perform differently for different groups of students, including groups defined by race, ethnicity, gender, nationality, and other characteristics, and they explain how bias can enter at many stages, from the data collected to the way a model is built and used. Holmes and Tuomi (2022) review the state of artificial intelligence in education and warn that many systems rest on narrow assumptions about what learning is. The edited volume by Holmes and Porayska-Pomsta (2022) collects a range of perspectives on the ethics of artificial intelligence in education, including fairness, accountability, and the rights of learners. Bond et al. (2024), in a meta systematic review of research on artificial intelligence in higher education, call for more attention to ethics, collaboration, and research rigor. Bias in digital learning environments is not only algorithmic. It can also be built into interface and content design. Cain and Fanshawe (2021) show that as Learning Management Systems became the main channel for distributing course content, students with print disabilities, such as vision impairment, blindness, and dyslexia, faced barriers to full engagement. These barriers are a clear case of a hidden lesson: a platform designed around an assumed default user quietly tells other users that they are an exception. 2.7 Gap in the literature Taken together, these studies offer rich material, but they are scattered across fields and are often written for specialist audiences. Research on the hidden curriculum rarely looks closely at software design. Research on platforms and analytics rarely uses the language of curriculum. Research on interface design rarely asks what students learn from it over years of use. This article brings these strands together and asks a single question: what is the hidden curriculum of the Learning Management System, and how is it delivered? 3. Conceptual Framework To answer this question, the article proposes a framework with three layers through which a Learning Management System can carry implicit lessons. The layers are interface design, interaction models, and algorithmic processes. Each layer works through particular mechanisms, and together they produce implicit lessons that may, over time, shape students' dispositions toward learning. Figure 1 sets out the framework. Figure 1. A three-layer framework of the hidden curriculum in Learning Management Systems. 3.1 Layer one: interface design The first layer is what students see. It includes the layout of the course page, the order of menu items, the colors and icons used to show status, the prominence given to grades, and the visual language of progress and completion. #Interface_design works mainly through visibility and emphasis. What appears at the top of the screen, in a large font, or in a bright color is implicitly marked as important. What sits three clicks deep is implicitly marked as optional. If the grade book is one click away and the discussion forum is buried in a submenu, students learn something about which activity matters more, even if no one has said so. 3.2 Layer two: interaction models The second layer is how students are expected to act. An #interaction_model is the pattern of behavior that a system invites or requires. It includes what kinds of actions are possible, what sequence they follow, and what counts as finishing. For example, a module may be locked until the student has viewed every page of the previous one. A forum may require a student to post before they can read others' posts. A quiz may allow unlimited attempts or only one. These rules work through permission and sequence. They make certain ways of learning easy and others difficult or impossible. 3.3 Layer three: algorithmic processes The third layer is what the system does with student data. It includes analytics that calculate engagement scores, #early_warning_systems that flag students at risk, automated reminders, recommendation features, #plagiarism_detection, remote proctoring, and, increasingly, generative artificial intelligence tools. #Algorithmic_processes work through classification and prediction. They sort students into categories, assign scores, and trigger responses, often without students knowing how or why. 3.4 From mechanisms to dispositions The framework assumes that each layer carries implicit lessons, and that repeated exposure to these lessons can shape students' #dispositions, their habitual ways of thinking and acting as learners. This does not mean that students are passive. People interpret, resist, and work around the systems they use. The framework includes a feedback path, shown at the bottom of Figure 1, to signal that critical awareness can interrupt the process. When students and teachers notice what a platform is teaching, they gain the ability to accept, adapt, or reject its lessons. This framework is deliberately simple. It is meant as a tool for thinking rather than a complete theory. It draws on the platform studies view that software actively shapes practice (Decuypere et al., 2021; Perrotta et al., 2021), on the learning analytics ethics literature that highlights classification and prediction (Baker and Hawn, 2022; Prinsloo et al., 2022), and on the human-computer interaction literature on persuasive and manipulative design (Mathur et al., 2021; Weijers et al., 2021). 4. Approach and Method This article is a critical integrative review combined with conceptual analysis. It does not report new empirical data. Instead, it brings together findings and arguments from existing research and reads them through the lens of hidden curriculum theory. The review drew on peer-reviewed journal articles, conference papers, and scholarly books. Sources were identified through searches of major academic databases using combinations of terms including hidden curriculum, learning management system, digital learning platform, learning analytics, dashboard, nudging, algorithmic bias, proctoring, accessibility, and platformization. Priority was given to work published between 2021 and 2026, so that the analysis reflects the current generation of platforms, including the period after the large-scale shift to online teaching during the pandemic and the arrival of #generative_AI. All sources were checked to confirm publication details. The analysis proceeded in three steps. First, the main features of contemporary Learning Management Systems were grouped according to the three layers of the framework. Second, for each feature, the review identified what the literature says about its intended purpose and its observed or likely effects on students. Third, these effects were interpreted as implicit lessons and grouped into themes. Figure 2 shows examples of how specific features were mapped to their explicit purpose and the hidden lesson they may carry. Figure 2. Examples of common LMS features, their stated purpose, and the implicit lesson they may carry. Two limitations of this approach should be stated at the outset. First, because the article interprets existing work rather than collecting new data, its claims about hidden lessons are arguments supported by evidence, not measured outcomes. Second, platforms differ, and institutions configure the same platform in very different ways. The lessons described below are tendencies, not universal effects. Section 7 returns to these limitations. 5. Analysis: Six Hidden Lessons of the Learning Management System 5.1 Lesson one: learning is the completion of visible tasks The most basic unit of a Learning Management System is the task. Readings, videos, quizzes, forum posts, and assignments are each presented as items to be opened, done, and marked off. Many systems display a checkbox or a tick next to each item, and a #progress_bar that fills as items are completed. Some lock later content until earlier items are finished. These features have real benefits. They help students organize their work, break large courses into manageable parts, and see what remains to be done. For students juggling study with paid work or family responsibilities, a clear list can reduce stress. Research on student-facing dashboards confirms that students value features that support planning and organization (Divjak et al., 2023). The hidden lesson, however, is that learning is the same thing as completing tasks. A tick appears when a video has been played or a page has been opened, not when the student has understood it. The system cannot easily register reflection, confusion, curiosity, or a change of mind. It records actions that leave a digital trace. Over time, students may come to treat the visible completion of tasks as the goal, rather than the understanding the tasks were meant to support. This is sometimes described as a #checklist_mentality. This lesson connects to what Perrotta et al. (2021) describe as the distribution of pedagogical labor across people and software. When the platform tracks completion automatically, it takes over part of the work of judging whether a student is keeping up. But it judges using only the signals it can capture. The result is a quiet narrowing of what counts as progress. A student who spends an hour thinking carefully about one difficult reading may appear less active than one who clicks through ten pages in ten minutes. There is also a lesson about the structure of knowledge. When content is divided into sequential modules that must be completed in order, the platform implies that knowledge is linear and cumulative, that each piece is a step toward the next. Some subjects work this way. Many do not. In the humanities, social sciences, and creative fields, understanding often develops through returning to earlier ideas, making connections across topics, and living with unresolved questions. A rigid sequence of locked modules may teach students that this kind of wandering is not part of proper study. 5.2 Lesson two: time is measured in deadlines and countdowns The second hidden lesson concerns time. Learning Management Systems are full of temporal signals: due dates, countdown timers on quizzes, late submission flags, calendar feeds, and automated reminders. Assignments that are submitted after the deadline are often labeled in red. Many systems send notifications to phones and email, so that the course follows the student out of the platform and into the rest of their life. Again, these features are useful. Clear deadlines help students plan, and reminders can prevent missed submissions. Weijers et al. (2021) note that reminders are among the simpler and more widely accepted forms of nudging in education. The hidden lesson is that learning time is the time of the deadline. The platform makes the due date the most prominent temporal fact about a piece of work, more prominent than the time needed to think, draft, and revise. Students learn to organize their study around the next approaching red flag. This can encourage a pattern of #last_minute_work, in which effort rises sharply just before a deadline and falls away after it. It can also create a sense of constant #low_level_urgency, as notifications arrive at all hours. Countdown timers on quizzes teach a more specific lesson: that knowledge should be produced quickly under pressure. Timed assessment has a place, but when it becomes the default format because the platform makes it easy to set up, students may come to equate being good at a subject with being fast at it. This lesson has an equity dimension. Students who work, care for others, live in different time zones, or have disabilities that affect processing speed experience deadline-driven and timed systems differently from students with fewer demands on their time. A platform that treats the clock as neutral may quietly reward those whose lives fit its rhythm. 5.3 Lesson three: being seen equals being engaged The third hidden lesson concerns #engagement. Most Learning Management Systems record student activity in detail. Teachers can often see when each student last logged in, how many times they viewed a resource, and how long they spent on a page. Many institutions use these records, sometimes combined into engagement scores, to identify students who may need support. The intention is usually caring. If a student has not logged in for two weeks, something may be wrong, and early contact can help. However, the hidden lesson is that engagement means generating data. A student who downloads all the readings at the start of term and studies them offline may appear disengaged. A student who leaves a browser tab open for hours while doing something else may appear highly engaged. The platform can only see what happens inside it, and it treats what it sees as a proxy for what matters. Once students realize that their activity is visible, some adjust their behavior to be seen. They may log in regularly not because they need to, but because they know logins are counted. They may post in forums to meet a participation requirement rather than to contribute to a discussion. This is a form of #performative_engagement, in which the goal shifts from learning to appearing to learn. Figure 3 illustrates this cycle. Figure 3. The visibility loop: how activity tracking can shift student behavior from learning toward being seen to learn. The loop shown in Figure 3 matters because it can change the meaning of the data it relies on. If students begin to act for the record, the record becomes a less reliable guide to their actual learning. This is a familiar problem in measurement: when a measure becomes a target, it tends to lose its value as a measure. It also raises questions about trust. Mutimukwe et al. (2022) show that students' concerns about how their data is used are tied to their sense of control and their trust in the institution. If students feel watched rather than supported, the relationship between student and university may shift from one of care to one of monitoring. Cerratto Pargman and McGrath (2021) observe that much ethical discussion of learning analytics focuses on privacy and consent. The visibility loop suggests a further issue: even when consent is given and data is protected, the act of tracking can reshape the behavior it tracks. This is a hidden curriculum effect in the full sense. Students learn not only what the institution wants them to do, but how to perform for an audience they cannot see. 5.4 Lesson four: comparison with peers is a natural measure of worth The fourth hidden lesson comes from features that compare students with each other. Some Learning Management Systems and add-on dashboards show students how their grades, activity levels, or progress compare with the class average. Gamified features such as #leaderboards, badges, and points may rank students explicitly. Even where no explicit comparison is shown, grade distributions, class averages, and percentile scores invite students to locate themselves relative to others. The rationale for such features is often motivational: seeing that others are ahead may encourage a student to work harder. Some studies report positive effects for some students. Yet the research on how students actually experience #peer_comparison is cautious. Rets et al. (2021) found that comparison with peers was among the least favored dashboard features among the distance learners they interviewed, unless it came with richer qualitative information. Divjak et al. (2023) found that students were wary of comparison and competition, with some seeing them as potentially demotivating. Aguilar (2023) warns that under-served students may be especially affected by discouraging messages about their performance, and argues that dashboards should be designed with motivation theory in mind. The hidden lesson here is that a student's standing is relative. The platform implies that the right question to ask is not "have I understood this?" but "how am I doing compared with everyone else?" This can encourage a #competitive_orientation to learning, in which other students become benchmarks or rivals rather than collaborators. It can also damage confidence, particularly for students who are already uncertain whether they belong at university. A student who sees that they are below average every week may learn that they are a below-average person, a lesson that has little to do with their actual potential. Comparison features also teach a lesson about what is worth comparing. Dashboards can only compare what they measure, which is usually activity counts and grades. They cannot show that one student asked a brilliant question, helped a classmate, or connected the course to their own community. By displaying some forms of achievement and not others, the platform shapes students' sense of what achievement is. 5.5 Lesson five: surveillance is a normal condition of study The fifth hidden lesson concerns #surveillance. During the pandemic, many universities adopted #remote_proctoring tools for online examinations. These tools may use webcams, microphones, screen recording, and automated analysis to detect behavior that the system classifies as suspicious. Selwyn et al. (2023b) studied the rise of online exam proctoring in Australian universities and found that it was often presented by institutions as a necessary evil, justified by the need to protect #academic_integrity during a crisis. Coghlan et al. (2021) examined the ethics of these technologies, weighing their potential benefits against concerns about privacy, fairness, trust, and student autonomy. Proctoring is the most visible form of monitoring, but it is not the only one. Plagiarism detection software checks every submission. Activity logs record every click. Some systems flag unusual patterns of behavior. Each of these may be defensible on its own. Together, they create an environment in which being watched is simply part of being a student. The hidden lesson is that suspicion is the default. When every submission is scanned for copying and every exam is monitored for cheating, the platform communicates that students are presumed to be potential wrongdoers who must prove their honesty. This can undermine the relationship of #trust that education depends on. It may also teach a broader civic lesson. Students who spend years in environments where constant monitoring is normal may come to accept it as normal in other parts of life, including work and public space. In this sense, the hidden curriculum of the platform may prepare students for a wider culture of data collection. Surveillance also interacts with bias. Automated proctoring systems that rely on face detection or behavioral analysis may perform differently for students with different skin tones, disabilities, or home environments. A student sharing a small room with family members, or a student with a condition that causes involuntary movements, may be flagged more often than a student with a quiet private space. Coghlan et al. (2021) note fairness among the core ethical concerns raised by these systems. Here the hidden lesson becomes a lesson about who is normal and who is suspect. Prinsloo et al. (2022) argue that student data privacy cannot be secured by technical fixes alone. The same is true of the hidden curriculum of surveillance. Even a perfectly secure system still teaches something about the relationship between students and institutions. Komljenovic (2022) suggests that the deeper issue in digitalized higher education is how value is generated from student activity. Seen in this light, surveillance is not only about catching misconduct. It is also part of a system in which student behavior becomes a resource. 5.6 Lesson six: the default user is able, connected, and culturally prepared The sixth hidden lesson concerns who the platform is designed for. Every system is built with an imagined user in mind. In the case of most Learning Management Systems, this default user appears to be a student with a reliable internet connection, a modern laptop, good eyesight, fluent reading in the language of instruction, familiarity with the conventions of university study, and the time and space to work in long, uninterrupted periods. Students who do not match this profile meet friction. Cain and Fanshawe (2021) show how students with print disabilities faced barriers in online learning environments where course materials were not consistently accessible to screen readers or other #assistive_technologies. Each barrier carries a message: that the student's needs are an add-on, to be handled through special requests, rather than part of the normal design. This is a hidden lesson about #inclusion and exclusion. Connectivity is another dimension. Platforms that assume constant high-speed access, stream video by default, or time out during slow uploads disadvantage students in rural areas, contributing to a #digital_divide, in lower-income households, or in regions with unreliable infrastructure. Kpum and Gasa (2026) show that distance learners in African contexts may be connected yet still excluded from the informal academic networks that matter for success. Their concept of digital academic orphanhood captures the experience of being present on the platform but absent from the conversations that happen around it. Cultural assumptions are also built in. Interface conventions, examples, and default settings often reflect the norms of the countries where the software was designed. The expectation that students will post confidently in public forums, challenge each other's views, or ask for help through a ticketing system may feel natural to some students and uncomfortable to others. The platform does not explain these expectations. It simply presents them as how things are done. Dorrian's (2026) point about learning event literacy applies here too: when institutions assume that students already understand the purpose and norms of an activity, students without that understanding are quietly disadvantaged. Algorithmic systems can deepen these patterns. Baker and Hawn (2022) show that predictive models in education may be less accurate for some groups of students than for others. An early warning system trained mainly on data from one population may misjudge students from another. If such a system flags a student as at risk, the label itself may affect how the student is treated and how they see themselves. If it fails to flag a student who genuinely needs help, the student may slip through unnoticed. Either way, the algorithm teaches a lesson about who the system understands. 5.7 The new layer: generative artificial intelligence The six lessons above describe platforms as they have developed over the past decade. A new layer is now being added. Generative #artificial_intelligence tools, which can produce text, summaries, feedback, and answers, are being integrated into Learning Management Systems and used alongside them. Kasneci et al. (2023) outline both the opportunities and the challenges that large language models present for education, including the potential for personalized support and the risks of #over_reliance, bias, and inaccuracy. Bond et al. (2024) note that research on artificial intelligence in higher education still needs stronger attention to ethics and rigor. From a hidden curriculum perspective, generative tools may introduce new implicit lessons. If a platform offers an automated summary of every reading, it may teach that #deep_reading of the full text is optional. If it generates instant feedback on drafts, it may teach that good writing is writing that satisfies the tool. If it answers questions instantly, it may teach that struggling with a problem is a sign of inefficiency rather than a normal part of learning. None of these effects is certain, and well-designed tools may support deeper learning. But the history of earlier platform features suggests that it is wise to ask what new tools teach, not only what they do. Holmes and Tuomi (2022) caution that many artificial intelligence systems in education are built on narrow models of learning, and these models may become part of what students absorb. 5.8 How the lessons combine The six lessons have been described separately, but in practice they reinforce each other. A student who learns that progress means ticking boxes, that time means deadlines, that engagement means being visible, that worth is relative, that monitoring is normal, and that the system was not designed for people like them, may develop a particular orientation toward study. That orientation may be efficient, compliant, and anxious. It may be focused on outputs rather than understanding, on appearing rather than being, and on fitting the system rather than shaping it. This picture should not be overstated. Students are not blank slates, and they bring their own values, experiences, and critical capacities. Many use platforms selectively and skeptically. Teachers also shape how platforms are experienced, and a thoughtful teacher can soften or counter many of these lessons. Still, the consistency of the platform across courses, years, and institutions gives its lessons a particular force. A student may meet dozens of teachers with different approaches, but the same platform design, day after day. 6. Discussion and Implications 6.1 Is the hidden curriculum of the platform necessarily harmful? It would be easy to read the analysis above as a straightforward critique. That would be too simple. Hidden curricula have always included useful lessons alongside harmful ones. Learning to manage time, to organize tasks, and to meet obligations are valuable skills, and Learning Management Systems can help students develop them. Clear #course_structure can be especially helpful for students who did not receive guidance about study habits before university. In this sense, some of the platform's hidden lessons may actually reduce inequality by making expectations more explicit. The problem is not that platforms teach. It is that they teach without being noticed, and that their lessons are shaped by design decisions made far from the classroom, often under commercial pressures (Komljenovic, 2021; Williamson and Komljenovic, 2023). When lessons are hidden, they cannot be discussed, questioned, or adapted to local needs. The goal, therefore, is not to remove the hidden curriculum, which is impossible, but to make it visible enough that students and educators can engage with it critically. 6.2 Implications for students For students, the most important step is awareness. The framework in Figure 1 can be used as a personal checklist. When using a platform, a student can ask three questions. What does the interface make most visible, and why? What does the system allow, require, or prevent me from doing? What happens to the data I generate, and how might it be used to describe me? Students can also develop habits that resist the narrower lessons of the platform. They can treat #completion_ticks as reminders, not as evidence of learning. They can plan their work around the time a task actually needs, not only around the deadline. They can recognize that their activity log is a partial picture, and that offline reading and thinking count as study. They can turn off peer comparison features where possible, or at least remember that averages say little about individual growth. Building #critical_digital_literacy of this kind is a transferable skill, useful far beyond university. Students also have a #student_voice. Many institutions consult students on technology decisions, and student representatives can ask how analytics are used, whether proctoring is necessary, and whether platforms meet accessibility standards. Mutimukwe et al. (2022) show that trust in the institution shapes how students feel about data use, which suggests that institutions have an interest in listening. 6.3 Implications for teachers Teachers configure platforms, and their choices matter. A teacher can decide whether to lock modules in sequence, whether to use timed quizzes, whether to require forum posts, and whether to display class averages. Each choice carries an implicit lesson, and teachers can make these choices deliberately rather than accepting the defaults. Teachers can also talk about the platform openly with students. Explaining why a particular feature is being used, or why it is not, helps turn hidden lessons into explicit ones. Dorrian (2026) shows that clarifying the purpose of learning activities can improve students' engagement. The same principle applies to platform features. A short discussion at the start of a course about what the system tracks, what the teacher looks at, and what counts as good participation can reduce performative behavior and build trust. Finally, teachers can be careful about how they interpret platform data. An activity log is a trace, not a diagnosis. Using it as a prompt for a caring conversation is different from using it as a judgment. Kerssens and van Dijck (2022) emphasize the importance of protecting teachers' professional autonomy in a platform society, and part of that autonomy is the ability to interpret data with human judgment. 6.4 Implications for institutions Institutions choose platforms, sign contracts, set policies on data use, and decide whether to adopt tools such as proctoring or predictive analytics. These are curriculum decisions, even when they are made by information technology departments or procurement teams. Institutions should therefore involve educators and students in technology decisions and evaluate tools not only for efficiency but also for their implicit pedagogical effects. #Data_ethics policies should go beyond privacy compliance. Following Prinsloo et al. (2022) and Khalil et al. (2023), institutions can ask whether their use of analytics is fair, transparent, and accountable, and whether students understand and have some control over how their data is used. Predictive models should be checked for differential accuracy across student groups, as Baker and Hawn (2022) recommend. Accessibility should be treated as a #baseline_requirement, not an optional extra, in line with the barriers documented by Cain and Fanshawe (2021). Institutions should also be cautious about adopting surveillance tools as a default response to integrity concerns. Selwyn et al. (2023b) show how proctoring was framed as a necessary evil during the pandemic. Once the emergency has passed, it is reasonable to ask whether the evil is still necessary, and whether #assessment_design could reduce the need for monitoring. 6.5 Implications for designers and vendors Designers of learning platforms have the most direct influence over the hidden curriculum, even if they rarely think of their work in these terms. #Value_sensitive_design approaches encourage designers to identify the values embedded in their products and to consider the interests of all stakeholders. For learning platforms, this could mean designing completion indicators that distinguish between opening and understanding, offering reflective rather than comparative dashboards by default, giving students clear explanations of what is tracked and why, and testing features with diverse groups of students before release. Mathur et al. (2021) provide a vocabulary that designers can use to audit their own features. Is a reminder asymmetric, making one choice much easier than another? Is a tracking feature covert, operating without the student's awareness? Does a default setting restrict choices in ways the student would not choose? Asking such questions can help prevent nudges from becoming manipulative. Paulsen and Lindsay (2024) note a promising trend toward dashboards that are more grounded in learning theory. Extending this trend to the whole platform, so that every feature is designed with an explicit theory of learning in mind, would make the hidden curriculum less hidden and more open to debate. Figure 4 summarizes how each group can contribute to making the hidden curriculum visible. Figure 4. Making the hidden curriculum visible: roles for students, teachers, institutions, and designers. 6.6 Toward a pedagogy of the platform The broader implication of this analysis is that universities need what might be called a #pedagogy_of_the_platform. This means treating the digital environment as a site of teaching in its own right, with its own aims, values, and effects, rather than as a neutral background. It means asking of every platform feature the questions that educators ask of any curriculum: What is it for? What does it assume about learners? What does it reward? Who does it serve well, and who does it serve poorly? Such a pedagogy would not reject technology. It would place technology inside the professional conversation about teaching, where it belongs. Selwyn (2022) has argued for a more critical and grounded discussion of educational technology, one that pays attention to actual practices and consequences rather than promises. Williamson et al. (2023) similarly call for a re-examination of the assumptions behind artificial intelligence, automation, and datafication in education. A pedagogy of the platform is one way of carrying these calls into everyday university life. 7. Conclusion This article set out to explore the hidden curriculum of Learning Management Systems: the values, behaviors, and biases that students may acquire from the design of the platforms on which so much of their study now takes place. Drawing on recent research from platform studies, learning analytics, human-computer interaction, accessibility research, and curriculum theory, it proposed a three-layer framework linking interface design, interaction models, and algorithmic processes to the implicit lessons they carry. The analysis identified six hidden lessons. Platforms may teach that learning is the completion of visible tasks; that time is measured in deadlines and countdowns; that being seen equals being engaged; that comparison with peers is a natural measure of worth; that surveillance is a normal condition of study; and that the default student is able, connected, and culturally prepared. The arrival of generative artificial intelligence adds a further layer whose lessons are only beginning to emerge. These lessons are not inevitable, and some of them have real benefits. But because they are rarely discussed, they shape students without students having the chance to question them. The main argument of the article is that the hidden curriculum of the platform should be made visible. Students can develop critical awareness of what their platforms reward and record. Teachers can configure platforms deliberately and talk about their choices openly. Institutions can treat technology decisions as curriculum decisions and evaluate them accordingly. Designers can build values into their products consciously rather than by default. 7.1 Limitations This article has several limitations. It is a conceptual analysis based on a review of existing literature, not an empirical study. The hidden lessons it describes are well-supported interpretations, but their strength and prevalence across different student populations have not been measured directly. Much of the research reviewed comes from English-speaking and European contexts, and the experience of platforms in other regions may differ. Platforms also change quickly, and institutions configure them in many different ways, so the lessons described here should be understood as tendencies rather than fixed effects. 7.2 Directions for future research Several lines of research would strengthen understanding of this topic. Qualitative studies could explore how students themselves describe what they have learned from their platforms, using interviews, diaries, or walkthrough methods. Longitudinal research could follow students across several years to see whether platform-related habits persist and how they change. Comparative studies could examine how the same platform, configured differently, produces different hidden lessons. Research with students from under-represented groups, including students with disabilities, first-generation students, and students in low-connectivity regions, is especially needed. Finally, as generative artificial intelligence becomes part of standard platforms, research should examine early what these tools are teaching, before their lessons become as invisible as those of the features that came before them. Students have always learned more than the syllabus. In the classroom, they learned from the bell, the desk, and the red pen. On the platform, they learn from the tick, the countdown, and the dashboard. Recognizing this is the first step toward ensuring that what they learn serves them well. References Aguilar, S. J. (2023). Using motivation theory to design equity-focused learning analytics dashboards. 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International branch campuses promise students a foreign degree that is equal in value to the one awarded at the home university, yet they teach that degree in a social, cultural, linguistic and legal setting that may be very different from the one in which the curriculum was first written. This article examines how branch campuses try to adapt imported curricula to the lives and expectations of local students while protecting the academic standards on which the home institution's accreditation depends. Using a critical integrative review of recent peer-reviewed research published mainly between 2022 and 2026, the article builds a three-layer framework that separates a protected core of standards, an adaptive layer of content, pedagogy and support, and a contextual layer of national rules, culture and labour market needs. It then analyses six mechanisms that institutions use to manage alignment: outcome anchoring, assessment equivalence, co-design and shared governance, mandated national content, pedagogical mediation by teaching staff, and student agency. The analysis finds that most adaptation remains narrow and compliance-driven, that decision power over the curriculum usually stays with the home campus, and that the gap between equivalence and relevance is often closed informally by teachers and students rather than by design. The article argues that equivalence should be understood as equal standards of learning rather than identical content, and it proposes practical steps that allow branch campuses to become more culturally responsive without weakening the quality of their awards. Keywords: transnational education, international branch campuses, curriculum localisation, cultural alignment, quality assurance, accreditation, glocalisation, student experience 1. Introduction Imagine two students who will one day receive exactly the same degree certificate. One studies business at a university campus in the United Kingdom. The other studies the same programme, with the same course codes and the same learning outcomes, at that university's campus in Dubai, Kuala Lumpur or Suzhou. Both will graduate with a certificate that carries the same name, the same crest and the same accreditation. Yet the second student lives in a different society, often studies in a second language, follows different religious and family customs, will look for work in a different #labour_market, and studies under the laws of a different state. The question this article asks is simple to state but difficult to answer: how far can, and should, the #curriculum taught to the second student be changed to fit his or her world, without the degree losing the value that made it attractive in the first place? This question sits at the heart of #transnational_education, usually shortened to TNE. #TNE refers to programmes in which students are based in one country while the degree they study for is awarded by an institution based in another. It takes many forms, including franchised and validated programmes, joint and #dual_degrees, distance learning and, the focus of this article, the #international_branch_campus. A branch campus is a physical campus operated in a host country in the name of a foreign university, usually awarding that university's own degree. Recent reviews describe the steady growth of research on this model and show that branch campuses have become an established, though still contested, part of global #higher_education (Zhang and Dai, 2025; Qureshi, 2026). Branch campuses depend on a promise of equivalence. Their main selling point is that students can earn a degree from a well-known foreign university without leaving their own region. Students choose these campuses in part because of the reputation of the home country and the home institution (Wilkins et al., 2024). Research on country-branded universities shows that the things that most clearly cross borders from the home system are the curriculum, the style of #pedagogy and foreign #accreditation (Wilkins and Huisman, 2025), and that students' sense that the institution is authentically connected to its home country is positively related to their judgements of service quality and their satisfaction (Wilkins et al., 2026). In other words, part of what students buy is sameness. However, sameness has limits. A curriculum is never culturally neutral. It carries assumptions about what knowledge matters, which examples are familiar, how students should speak in class, how they should argue with teachers, what counts as original work and what kind of society graduates will enter. When a curriculum written for one context is moved, unchanged, into another, these hidden assumptions travel with it. Studies of branch campuses in China and the Gulf have described the tensions this creates, from questions about Western-centred knowledge to the everyday problems of teaching in English to #multilingual_students (Xu, 2023; Hillman, 2025). A recent large study of TNE partnerships identified rigid curriculum transfer as one of the systemic barriers that weaken provision and argued for contextual adaptation instead (Wang, 2026). There is therefore a real dilemma. If the branch campus changes too little, the curriculum may feel distant, irrelevant or even alienating to local students, and their learning may suffer. If it changes too much, the home university may no longer be able to show its #regulators, #professional_bodies and accreditors that the degree taught abroad meets the same standard as the degree taught at home. The concept of #cultural_alignment is used in this article to describe the work of reducing this dilemma: shaping the curriculum so that it fits the socio-cultural context of local students while keeping the academic standard of the award intact. The article has three aims. The first is to explain, in clear terms suitable for students, why adapting curricula at branch campuses is both necessary and risky. The second is to identify and critically analyse the main mechanisms that institutions use to adapt curricula without weakening standards. The third is to propose a framework and a set of practical principles that can help students, teachers and managers think about this problem more clearly. The central argument is that the debate is often framed in the wrong way. Equivalence is commonly treated as if it meant identical content, identical reading lists and identical assessment tasks. This article argues that what accreditation actually needs to protect is #equivalence of standards: the level, the intended learning outcomes and the rigour with which achievement is judged. Once equivalence is understood in this way, a large space opens up for legitimate local adaptation of examples, cases, language support, teaching methods and even some forms of assessment. The evidence reviewed here suggests that branch campuses rarely use this space fully. Adaptation tends to be minimal and driven by legal compliance, control over the curriculum tends to stay with the #home_campus, and much of the real alignment work is done informally by teachers and students. The article is organised as follows. Section 2 explains the approach used. Section 3 reviews recent research on branch campuses, curriculum and culture. Section 4 sets out the conceptual framework. Section 5 analyses the six mechanisms of adaptation and their limits. Section 6 discusses what the analysis means for practice and for students. Section 7 concludes and notes the limits of the study. 2. Approach and Method This is a conceptual and critical study rather than a report of new fieldwork. It is based on a critical integrative review of recent peer-reviewed research, which is a common method when a field has many small studies from different countries and the aim is to bring their insights together into a clearer explanation. The review focused on journal articles and scholarly book chapters published mainly between 2022 and 2026, so that the analysis reflects the current state of knowledge rather than older debates. Sources were identified through searches of large scholarly databases using terms such as international branch campus, transnational education, curriculum, localisation, quality assurance, student identity and teaching staff. Each source used here was checked against its registered publication record to confirm that it exists and that the authors, journal and publication details are correct. Studies were included when they said something directly about one of four themes: the relationship between the home campus and the branch campus; the curriculum and its adaptation; the experience of students and teachers; and the governance and quality assurance of TNE. The final body of evidence includes qualitative interview studies, surveys, systematic and scientometric reviews, and conceptual papers. They cover branch campuses and other TNE models in China, Hong Kong, #Malaysia, the #United_Arab_Emirates, #Qatar, Kazakhstan, Egypt, Oman, Bangladesh, Vietnam, Germany and the United Kingdom, among other places. The analysis was carried out in three steps. First, the findings of each study were summarised and coded according to the four themes. Second, recurring patterns were identified, especially the ways in which institutions try to change, or decide not to change, the curriculum. These patterns were grouped into six mechanisms. Third, each mechanism was examined critically by asking three questions: what problem does it solve, whose interests does it serve, and what are its limits? Two points should be made clear. First, a review of this kind cannot measure how often each mechanism is used across the world, and it does not claim to do so. Second, much of the available research comes from China and the Gulf, which reflects where branch campuses are concentrated and where researchers have been most active. Readers should therefore treat the framework as a well-grounded explanation that invites testing, not as a final statistical picture. 3. Literature Review 3.1 The growth and changing purpose of branch campuses Research on branch campuses has grown quickly over the last decade. A scientometric review of studies indexed in the Web of Science traced the development of this field, its main contributors and its topics, and noted the academic power relations that shape who studies branch campuses and from where (Zhang and Dai, 2025). Early work focused heavily on why universities open campuses abroad and why some of them fail. A systematic review of successful and unsuccessful ventures by UK universities proposed a decision-making framework combining strategic, leadership, academic, financial and operational factors (Hickey and Davies, 2024). More recent work on small and newly established campuses shows that many never grow beyond a few hundred students and must choose between growth, keeping the status quo, transformation, sale or closure (Wilkins and Hazzam, 2026). The purposes behind branch campuses have also become more varied. Research on German universities, which come from a less market-driven system than those in the English-speaking world, found that leadership decisions about branch campuses were still shaped by a neoliberal paradigm, and that such campuses could push the home institution further in that direction (Rottleb et al., 2026). Critical reviews have highlighted both the benefits of TNE, such as wider access and better #employability, and its risks, including #Western_centric curriculum design, financial dependency and concerns about quality (Qureshi, 2026). These debates matter for curriculum because the reasons a campus exists shape how much the institution is willing to invest in adapting what it teaches. 3.2 The home campus relationship and the question of equivalence At the centre of any branch campus is its relationship with the home campus. Quality assurance in TNE is built on the idea that the award must mean the same thing wherever it is earned. Recent work on the recognition of TNE qualifications points to inconsistent terminology, fragmented quality assurance frameworks and weak data collection, and calls for more harmonised systems so that cross-border qualifications can be trusted (Lantero and Francesca, 2025). Research from Malaysia shows how this relationship works in practice and how it can change. Interviews with leaders, staff and students at seven branch campuses found that the #COVID19 pandemic altered communication and collaboration with the home campus and gave the branch campuses a larger role in areas such as #student_wellbeing and recruitment (Merola et al., 2023). This suggests that the balance of control between home and branch is not fixed and can shift under pressure. The relationship also has a strong element of power. A study of a Hong Kong university's campus in mainland #China found that the institution held both international and Chinese identities, but that these were unequal: the international identity dominated academic matters such as curriculum, teaching and research, while the Chinese identity was mainly about following government regulations (Dai et al., 2026). This finding is very important for the present article, because it suggests that local identity often enters the curriculum through regulation rather than through academic design. 3.3 Students' experience, satisfaction and identity Student experience research gives the clearest signal that equivalence on paper does not guarantee equivalence in practice. Using data from more than two thousand undergraduate international students, one study found that students at branch campuses were significantly less satisfied with their academic experience, including teaching quality, academic environment and engagement, than students at the related home campuses (Merola et al., 2022). The authors called for the particular experiences of branch campus students to be understood and considered in planning. Other studies explore why students choose branch campuses and how they come to see themselves. Research with Chinese students at a Hong Kong institution's campus in the Greater Bay Area found that global, national and local factors all shaped enrolment decisions, including a high level of #internationalisation, a shared culture and good value for money (Dai et al., 2024). Students in Malaysia and the United Arab Emirates reported that the home country of a branch campus influenced both their choice and their overall satisfaction, while facilities mattered but were secondary to education-related factors (Wilkins et al., 2024). Several studies examine #student_identity. A comparison of branch campuses and Sino-foreign institutes in China found that differences in the purposes and strategies of the two types of institution affected students' experiences and their developing sense of self (Cai et al., 2025). Studies drawing on the idea of a third space show that students do not simply absorb a foreign culture. They negotiate it. Students at four branch campuses in China described an ambivalent sense of freedom, a politics of hybridity and the growth of a critical transnational self (Qu et al., 2026). Chinese mainland students at another campus exercised agency through clear goals, adaptability and reflection, while also noticing Western-centric hierarchies and reinterpreting them (Zhang et al., 2026). 3.4 Teachers, language and pedagogy Teaching staff are the people who turn a written curriculum into real learning. A systematic review of academic expatriates at branch campuses identified their different motivations and found that the main challenges came from balancing global integration with local responsiveness in their work, their interactions on campus and their careers (Yao and Yang, 2024). Teachers' emotional work is also significant. English teachers at an engineering branch campus in Qatar described #emotion_labour linked to finding purpose in their teaching and to confronting their roles within what they saw as a context of educational #neocolonialism (Hillman et al., 2024). Language is a recurring issue. A meta-synthesis of studies at Qatar's Education City branch campuses described emotional, academic, language and sociocultural barriers faced by multilingual students, and recommended #linguistically_responsive_instruction, a shift towards multilingual approaches and more space for local ways of knowing and teaching (Hillman, 2025). Research on teaching in the United Arab Emirates found that the meaning of student-centred education changed when Western teachers tried to apply it locally, and that centralised curricula and high stakes final assessments limited their ability to rethink their practice (Kinuthia, 2024). In nursing, a grounded theory study of educators' perceptions of transnational teaching shows that the views of the staff who carry programmes across borders have become a subject of research in their own right (Straughair et al., 2023). 3.5 Critical perspectives on power and knowledge A strong strand of recent research asks whose knowledge the curriculum represents. One study of promotional materials found that Western branch campuses in China linked world-class education to whiteness and Western images, raising questions about how internationalisation is presented (Xu, 2023). An analysis of master's programmes at Sino-foreign cooperative universities found marketisation and a Western orientation, and identified practices of mimicry and resistance in the way programmes were run (Lai and Jung, 2024). These studies do not claim that every imported curriculum is harmful. They warn, however, that a curriculum which is never questioned can quietly reproduce a hierarchy in which foreign knowledge is treated as better than local knowledge. 3.6 From transfer to co-creation The most recent research moves from describing problems to proposing alternatives. One conceptual framework treats TNE as a blend of institutional logics and proposes mechanisms such as #co_governance, curriculum co-design, revenue sharing and inclusive access to balance market goals with academic standards and public good (Tsiligiris, 2026). Another, drawing on survey, interview and audit data, proposes an ecosystem model built on collaborative governance and contextual adaptation (Wang, 2026). Work on #intercultural challenges in TNE has proposed further practical approaches for addressing cultural difference (Wang, 2025). A study using glocalisation as a lens showed that a foundation year delivered in China was not a simple copy of the UK version and that individual agency and creativity helped staff manage tensions around #English_medium teaching, attendance, equity ideals and AI policy (Miani, 2026). There are also examples of co-design in action. In marketing education, stakeholders from universities in the UK, Vietnam and Malaysia, including students as partners, worked together to co-produce module learning outcomes, which the authors argue improved the #student_experience and well-being (Barr et al., 2025). A case study of an international college in China found that its curriculum incorporated inter#national_content and comparative approaches while also embedding local socio-cultural values in joint programmes (Li et al., 2024). In health education, internationalisation of the curriculum has been described as requiring alignment between academics, senior leaders and professional bodies, and as carrying risks of power imbalance and neo-colonial attitudes (Davey, 2023). 3.7 The gap this article addresses The literature is rich, but it is spread across many separate studies of identity, satisfaction, staffing, governance and power. Few studies bring these strands together to ask the practical question: through which specific mechanisms is a curriculum adapted at a branch campus, and how does each mechanism protect or threaten the standard of the award? This article addresses that gap by building a framework that connects the protection of standards with the adaptation of content, and by examining each mechanism critically. 4. Conceptual Framework: The Layered Alignment Model 4.1 Three theoretical lenses The framework used in this article draws on three ideas from the literature, each of which explains part of the problem. The first is #glocalisation. This idea holds that global forms are not simply copied into local settings. Instead, global, national and local forces meet, mix and sometimes clash, producing something new (Miani, 2026). Applied to curriculum, glocalisation reminds us that even a programme that looks identical on paper is changed in practice by the setting in which it is taught. The question is therefore not whether a curriculum will be localised, but whether this happens by design or by accident. The second is #institutional_logics. This idea holds that organisations act according to different and sometimes competing sets of values and rules, such as the logic of the market, the logic of the academic profession and the logic of the state. Branch campuses must satisfy several of these at once: the home university's academic standards, the host government's regulations, the commercial need to recruit students and the community's expectations (Tsiligiris, 2026). Curriculum decisions are where these logics collide. The third is #constructive_alignment, the principle that intended learning outcomes, teaching activities and assessment should be designed to support one another. A recent study across UK and international institutions found that constructive alignment is often applied as a mechanical, linear exercise and has lost touch with its constructivist roots, which focus on how students build meaning from their own experience (Alamad, 2026). This point matters greatly for branch campuses. If learning is built on what students already know and experience, then a curriculum that ignores their context is not only less relevant, it is also less effective. 4.2 The three layers Bringing these lenses together, this article proposes the Layered Alignment Model, shown in Figure 1. The model separates the curriculum of a branch campus programme into three layers that should be treated differently. Figure 1. The Layered Alignment Model: a protected core of standards, an adaptive layer, and the contextual layer that surrounds them. The protected core contains the elements that define the standard of the award and must therefore be equivalent at home and abroad. These include the level of the qualification, the programme and module learning outcomes, the academic standards against which work is judged, the rules on #academic_integrity, and the final decisions about awards. Any change to the core requires the approval of the home institution and, where relevant, its accreditors and professional bodies. The core answers the question: what must every graduate of this programme be able to do, and how well? The adaptive layer contains the elements through which the core is delivered. These include the examples and #case_studies used in teaching, the choice of supplementary readings, the style of teaching, the language support offered, the timing and sequencing of topics, the form of some assessment tasks, and the support services around the programme. Changes in this layer do not alter the standard, as long as the learning outcomes are still met and assessed with equal rigour. The adaptive layer answers the question: how can these students best reach the same standard? The contextual layer surrounds the programme. It includes the host country's laws and regulations, any national content that the government requires all degrees to include, the cultural and religious norms of the society, the languages students speak, the expectations of families and employers, and the local labour market. The branch campus does not control this layer, but it must respond to it. The contextual layer answers the question: what does this society require of and expect from its graduates? 4.3 Why the model matters The main value of the model is that it moves the debate away from the false choice between copying and changing. Much confusion arises because people treat the whole curriculum as if it were part of the protected core. When this happens, any change, even replacing a London case study with a local one, can look like a threat to quality. Equally, critics who call for radical localisation sometimes forget that the core does need protecting, because students paid for a degree whose value depends on its equivalence. The model also helps to identify the main risks. The first is #core_drift, in which changes made in the adaptive layer slowly lower the standard, for instance when assessment tasks are simplified rather than adapted. The second is adaptive layer neglect, in which the branch campus leaves the adaptive layer untouched and delivers a curriculum that is formally equivalent but poorly suited to its students. The evidence reviewed in the next section suggests that the second risk is at least as common as the first, and probably more so. Figure 2 shows the two forces that pull on the curriculum. On one side is the pull of the home institution and its accreditors, which favours uniformity. On the other is the pull of the local context, which favours #relevance. Between them sit the mechanisms that institutions use to manage the tension. Figure 2. The two pulls on a branch campus curriculum and the mechanisms that mediate between them. 5. Analysis: Six Mechanisms of Curriculum Adaptation This section analyses six mechanisms that appear across the literature. Each is described, its strengths are identified and its limits are examined critically. Figure 3 shows how the mechanisms can work together as a cycle rather than as separate tools. Figure 3. The alignment cycle: six mechanisms linking design, delivery and review. 5.1 Mechanism one: anchoring on learning outcomes The first and most basic mechanism is to anchor equivalence on #learning_outcomes rather than on content. In this approach, the home institution guarantees that every module at the branch campus has the same intended learning outcomes as its home version. Local teachers are then free, within agreed limits, to choose examples, cases and materials that help their students reach those outcomes. This mechanism has clear strengths. It respects the logic of accreditation, which in most systems is concerned with outcomes and standards, and it creates a legitimate space for adaptation. A finance module can keep its outcome that students should be able to evaluate investment decisions, while using examples from the local stock market and regional companies. A law-related module can compare home country principles with host country practice. Evidence from China shows that this kind of approach is possible: one international college combined international content and comparative methods with local socio-cultural values in its joint programmes (Li et al., 2024). The limits are also important. First, outcomes are only as useful as they are well written. Many learning outcomes are so general that they say little about what students must actually know, while others are so specific that they lock in particular content. Second, as the work on constructive alignment shows, outcomes can be treated as a box-ticking exercise, separated from any real thinking about how students learn (Alamad, 2026). When this happens, the outcome remains equivalent on paper while the learning experience is not. Third, outcomes themselves are not culturally neutral. An outcome that requires students to critique authority figures in open debate assumes a particular classroom culture. Anchoring on outcomes is therefore necessary, but it does not settle the question of which outcomes are appropriate. A more ambitious version of this mechanism involves stakeholders in writing the outcomes themselves. In the marketing education study mentioned earlier, partners from three countries, including students, co-produced module learning outcomes, treating them as shared value propositions rather than fixed requirements handed down from the home campus (Barr et al., 2025). This suggests that outcome anchoring can become a site of collaboration rather than control. 5.2 Mechanism two: assessment equivalence and moderation Assessment is the point at which standards become visible, so it is also the point at which home institutions are most cautious. Typical practices include shared or centrally set examinations, second marking of branch campus scripts by home campus staff, #external_examiners who see work from both sites, and common marking criteria. These practices form the backbone of #assessment_equivalence, and they are one of the main ways in which accreditors are reassured that a degree earned abroad meets the home standard. The strength of this mechanism is obvious. Without some form of comparable judgement, the claim of equivalence would be impossible to check. Yet the evidence suggests that assessment can also be the place where cultural alignment is most restricted. Research in the United Arab Emirates found that centralised curricula and high stakes final assessments left teachers little room to rethink their teaching, even when they could see that their methods fitted poorly with their students (Kinuthia, 2024). Where an identical examination is used in every location, teachers are under pressure to teach to that examination, and the adaptive layer shrinks. A critical point should be made here. Equivalence of assessment does not require identical assessment tasks. Two tasks can test the same outcome to the same standard while using different contexts. A case-based examination question about market entry can describe a firm entering the Gulf in one version and a firm entering Europe in another, while both are marked against the same criteria. What must be equivalent is the cognitive demand, the criteria and the rigour of marking. This distinction is often missed. When institutions treat identical tasks as the only safe option, they protect the appearance of equivalence at the cost of relevance. When they adopt equivalent but contextualised tasks supported by shared criteria and robust #moderation, they can protect both. Language raises a further issue. Many branch campuses teach in English to students for whom English is a second or third language. If assessment rewards fluent academic English as much as subject knowledge, students may be judged partly on language rather than on the outcome being tested. Research at Qatar's branch campuses recommended language-aware approaches and a move away from purely monolingual practices (Hillman, 2025). This does not mean lowering standards of communication where communication is itself an outcome. It means being clear about what each assessment is designed to measure. 5.3 Mechanism three: co-design and shared governance The third mechanism concerns who has the power to make curriculum decisions. In many branch campuses, the curriculum is designed at home and delivered abroad, with local staff having limited formal influence. The evidence from the Hong Kong institution's campus in mainland China, where the international identity dominated curriculum, teaching and research, is a clear example of this pattern (Dai et al., 2026). Recent conceptual work argues for a different model. The THRIVE framework proposes co-governance and #curriculum_co_design as ways of bringing academic, market and community logics into balance (Tsiligiris, 2026). The ecosystem model argues that collaborative governance and long-term relational investment, rather than one-way transfer, make partnerships more sustainable and more locally embedded (Wang, 2026). Both suggest that branch campus staff, local partners, employers and students should have a real voice in shaping the adaptive layer, and some voice in reviewing the core. A practical way to think about this is to assign #decision_rights to each element of the curriculum. Figure 4 offers an illustrative matrix. Some decisions, such as the award level, the final approval of learning outcomes and the pass standard with its marking criteria, are reserved for the home institution because accreditation depends on them. Others, such as the design of assessment briefs and the moderation process, are shared. Still others, such as the choice of local case studies, teaching methods and student support, can be delegated to the branch campus within agreed limits. Figure 4. An illustrative decision-rights matrix for branch campus curricula (reserved, shared and delegated decisions). The strength of shared governance is that it makes adaptation deliberate and visible, which in turn makes it easier to defend to accreditors. When a change is documented, justified against the learning outcomes and approved through an agreed process, it becomes part of the quality system rather than a quiet departure from it. The limit is that co-design takes time, trust and resources. Small or new campuses, which often struggle to grow and must make difficult strategic choices, may lack the capacity to support it (Wilkins and Hazzam, 2026). There is also a risk that shared governance becomes a formality, with local voices invited to meetings but not given real influence. 5.4 Mechanism four: mandated national content In some host countries, the state requires every university, including foreign branch campuses, to teach certain content. This may include courses on national history, culture, language, religion or citizenship. This mechanism adds local content to the curriculum by law rather than by academic choice. Mandated content has some clear benefits. It ensures that students learn about their own society, and it signals that the branch campus is part of the national system rather than a foreign island. However, it is a weak form of cultural alignment for two reasons. First, it is usually added alongside the imported curriculum rather than woven into it. Students may take a national studies course in one room and then go to a business course in which every example comes from another continent. Second, it can make institutions feel that their duty to localise has been met, which reduces the incentive for deeper change. Evidence from China supports this concern. In the case discussed above, the Chinese identity of the campus showed itself mainly through compliance with government rules, while academic matters remained internationally defined (Dai et al., 2026). A critical analysis therefore suggests that mandated national content should be seen as a minimum, not as a solution. It answers the question of what the state requires, but not the question of how the whole programme can become meaningful to its students. 5.5 Mechanism five: pedagogical mediation by teaching staff The fifth mechanism is the least formal but perhaps the most important. In practice, much cultural alignment is done by individual teachers who adjust how they teach, how they explain, which examples they use and how they relate to students. This is #pedagogical_mediation: the work of translating a curriculum written elsewhere into something students can engage with here. The evidence shows how much depends on this work. Expatriate academics at branch campuses face the challenge of balancing global integration with local responsiveness in their daily work (Yao and Yang, 2024). Teachers of English at a branch campus in Qatar described the emotional cost of reflecting on their purpose and their position (Hillman et al., 2024). Teachers in the United Arab Emirates found that the idea of student-centred learning had to be understood in relative rather than absolute terms when they tried to apply it in a new place (Kinuthia, 2024). In the foundation year studied in China, the agency and creativity of individual staff were central to managing tensions between the UK model and local realities (Miani, 2026). The strength of this mechanism is its sensitivity. Teachers who know their students can make small, fast, intelligent changes that no committee could plan. Its weakness is that it is invisible, uneven and fragile. It depends on the skill and goodwill of particular people, it is rarely documented, and it disappears when those people leave. Because it is informal, it may also be seen by quality systems as a risk rather than as good practice. A teacher who changes an example to make it more meaningful may worry that this counts as a departure from the approved module. A critical reading suggests that institutions are often relying on teachers to close the gap between equivalence and relevance without recognising or supporting this work. Training in linguistically and culturally responsive teaching, time for staff to share practice, and clear guidance on what may be changed in the adaptive layer would turn this hidden mechanism into a recognised part of quality assurance. 5.6 Mechanism six: student agency and the third space The final mechanism is often overlooked because it is not controlled by the institution at all. Students themselves adapt the curriculum to their lives. They interpret foreign ideas in light of local experience, they choose which parts of the programme to take seriously, and they build #hybrid_identities that draw on both worlds. Research using the idea of a #third_space shows this clearly. Students at branch campuses in China were not passive receivers of a foreign culture. They negotiated, resisted and formed new ways of understanding themselves (Qu et al., 2026). Mainland Chinese students at a Hong Kong institution's campus exercised agency through their motivations, their adaptability and their reflection on the campus environment, and they reinterpreted the Western-centric hierarchies they encountered (Zhang et al., 2026). Studies comparing types of TNE institution show that these identity processes are shaped by the purposes and strategies of the institution (Cai et al., 2025). The strength of #student_agency is that it can produce genuinely new and creative forms of learning that neither the home curriculum nor the local tradition would produce alone. The limit is that it places the burden of alignment on the students, some of whom are better equipped than others to carry it. Students with strong language skills, international experience or family support may thrive in the third space, while others may feel excluded or confused. The lower satisfaction reported by international students at branch campuses compared with home campuses (Merola et al., 2022) suggests that not all students find this negotiation easy. Institutions should therefore treat student agency as a resource to be supported, for instance through student partnership in curriculum review, rather than as a substitute for institutional responsibility. 5.7 The overall picture: a spectrum from replication to co-creation When the six mechanisms are considered together, a pattern emerges. Branch campuses can be placed along a spectrum, shown in Figure 5, from pure replication of the home curriculum, through compliance-driven localisation and outcome-based adaptation, to genuine co-creation. Figure 5. A spectrum of curriculum localisation at branch campuses, from replication to co-creation. The evidence reviewed suggests that many campuses sit towards the left of this spectrum. Their localisation is often limited to mandated national content and the informal efforts of teachers and students, while formal control over curriculum and assessment stays with the home campus. This position is understandable, because it feels safe from the point of view of accreditation. But the analysis in this article suggests that it is not as safe as it looks. A curriculum that is formally equivalent but poorly aligned with its students may produce lower satisfaction and weaker learning, and it may expose the institution to criticism that it is reproducing a hierarchy of foreign over local knowledge (Xu, 2023; Lai and Jung, 2024). In the long run, these are also threats to reputation and to the quality of the award. The positions further to the right are more demanding. They require clear separation of the protected core from the adaptive layer, assessment designed for equivalent standards rather than identical tasks, shared decision rights, investment in staff, and real partnership with students. They also require trust between the home campus and the branch campus. But they offer a more defensible form of quality, in which equivalence is shown through evidence about learning rather than through sameness of content. 6. Discussion 6.1 Rethinking what equivalence means The most important conclusion of the analysis is that the meaning of equivalence needs to be clarified. In everyday discussion of branch campuses, equivalence is often used loosely to mean that the programme abroad is the same as the programme at home. But sameness can refer to many things: the same outcomes, the same standards, the same content, the same textbooks, the same assessment tasks, the same teaching methods or the same student experience. Some of these are essential to the value of the degree. Others are not. This article argues for a clear distinction between equivalence of standards and uniformity of content. Equivalence of standards means that graduates at every location have achieved the same learning outcomes, at the same level, judged with the same rigour. This is what accreditation exists to protect. Uniformity of content means that every student studies identical materials in identical ways. This is sometimes convenient for administration, but it is not required for quality, and it can actually work against learning when the content is poorly suited to the students. Once this distinction is accepted, many of the tensions described in the literature become easier to manage. A branch campus can use local cases, examples and readings without any loss of standard. It can design assessment tasks set in local contexts, as long as they are marked against shared criteria and moderated across sites. It can teach in ways that suit its students' prior experience while still developing the skills the outcomes require. The question changes from whether the curriculum is the same to whether the learning is equivalent, and this is a question that can be answered with evidence. 6.2 The authenticity paradox The discussion of equivalence leads to a second issue, which this article calls the #authenticity_paradox. Students choose branch campuses partly because they want an authentic foreign education. Research shows that the curriculum, the teaching style and foreign accreditation are central to what country-branded universities transfer (Wilkins and Huisman, 2025), and that perceived authenticity is linked to students' satisfaction and to the brand image of the institution (Wilkins et al., 2026). At the same time, the student experience research shows that a foreign curriculum delivered without enough attention to local realities can leave students less satisfied (Merola et al., 2022). The paradox is that students want both the foreign and the familiar. They want the international degree, but they also want to see their own society, their own examples and their own future in what they study. The way out of this paradox lies once again in the layered model. Authenticity can be located in the protected core, in the standards, outcomes and academic culture of the home institution, while relevance is built into the adaptive layer. A student in Doha or Suzhou can receive an authentic British, Australian or American education in terms of its standards and intellectual expectations, while studying cases that come from the economy he or she will enter. Authenticity and relevance are not opposites. They belong to different layers. 6.3 Power, knowledge and the direction of learning A critical analysis must also ask who decides. The evidence suggests that, in many branch campuses, the home campus holds the main power over curriculum, while the host context is represented mainly through regulation and through the informal work of teachers and students (Dai et al., 2026). This is partly a technical result of how accreditation works. Accreditors hold the home institution responsible for its awards, so the home institution keeps control. But it is also a result of deeper assumptions about whose knowledge counts. The critical studies reviewed in Section 3 warn that branch campuses can reproduce an image of Western education as superior (Xu, 2023) and that programmes can show patterns of mimicry and resistance (Lai and Jung, 2024). Even where students exercise agency, they may still perceive Western-centric hierarchies within the institution (Zhang et al., 2026). These findings matter for #curriculum_localisation because they show that adaptation is not only a matter of making content more familiar. It is also a matter of recognising local knowledge as valuable in its own right. A fully aligned curriculum would allow learning to flow in both directions. Local cases would not be used only to illustrate theories developed elsewhere. They would also be used to test, question and extend those theories. Insights from the branch campus could return to the home campus and enrich its own curriculum. Some recent frameworks for TNE point in this direction by emphasising mutual capacity building, partnership and equity (Wang, 2026; Tsiligiris, 2026). This would turn the branch campus from a site of delivery into a site of #knowledge_creation, which is arguably what a university campus should be. 6.4 Practical principles for institutions Drawing the analysis together, six practical principles can be proposed for institutions that wish to improve cultural alignment without weakening their standards. First, map the layers. Every programme delivered at a branch campus should have a clear statement of what belongs to the protected core, what belongs to the adaptive layer and what external requirements form the contextual layer. This simple step removes much uncertainty for teachers and quality staff. Second, write outcomes that allow for context. Learning outcomes should describe the knowledge and abilities graduates need without tying them to a single national setting where this is not necessary. Where possible, outcomes should be reviewed with input from branch campus staff, students and employers. Third, design assessment for equivalent standards, not identical tasks. Shared criteria, calibration exercises in which markers from both campuses mark the same samples together, and robust moderation can protect standards while allowing assessment tasks to be set in local contexts. Fourth, make decision rights explicit. A matrix of reserved, shared and delegated decisions, similar to Figure 4, can show who may change what, and through which approval process. This turns informal adaptation into documented, defensible quality practice. Fifth, invest in teaching staff. Teachers carry much of the alignment work. They need induction into the local context, training in linguistically and culturally responsive teaching, time to share practice with colleagues at both campuses, and recognition for adaptation that improves learning. The emotional demands of this work should also be acknowledged (Hillman et al., 2024). Sixth, treat #students_as_partners. Students can help identify where the curriculum feels distant, suggest relevant local examples and take part in review. Experience with co-produced learning outcomes suggests that this can improve the student experience (Barr et al., 2025). Students' own negotiation of identity in the third space (Qu et al., 2026) can become a source of curriculum insight rather than a private struggle. 6.5 Implications for regulators and accreditors The principles above depend partly on the approach taken by external bodies. Accreditors and quality agencies in the home country, and regulators in the host country, can either encourage or discourage thoughtful adaptation. Home accreditors can help by making clear that they judge equivalence of standards and outcomes rather than uniformity of materials, and by asking institutions to show how they have adapted delivery for their students, not only how they have kept it the same. Host regulators can help by moving beyond requirements for separate national content courses towards encouraging the integration of local context throughout programmes. Both sides can help by sharing information and aligning their requirements where possible, reducing the double burden that branch campuses often face. Calls for more harmonised quality assurance and clearer terminology in TNE point in this direction (Lantero and Francesca, 2025). 6.6 What this means for students Because this article is written for students, it is worth considering what the analysis means for them directly, whether they are studying at a branch campus, thinking about enrolling, or studying TNE as a subject. Students considering a branch campus can ask practical questions. Are the learning outcomes and the degree certificate the same as at the home campus? How is assessment moderated between campuses? Do teachers use local examples and cases? Is there language support? Do students have a voice in reviewing the curriculum? The answers will reveal where the institution sits on the spectrum shown in Figure 5. Students already at a branch campus can use the layered model to understand their own experience. If a course feels distant from their lives, the problem may lie in the adaptive layer rather than in the standard of the degree, and it is reasonable to suggest, through student representatives and course feedback, that local cases or different teaching methods could help. Students can also recognise the value of their own position. The ability to move between two academic cultures, to translate ideas across contexts and to question both, is itself a valuable form of learning. Students who study education, management or international relations can treat branch campuses as a rich case study of how global institutions work. Questions of #quality_assurance, power, identity and knowledge come together in a very concrete way in a single classroom where a curriculum written in one country is taught in another. 6.7 An illustrative example To make the framework more concrete, consider a hypothetical second-year module in marketing at a branch campus in the Gulf of a university based in the United Kingdom. The example is constructed for teaching purposes and does not describe a real institution. In the protected core, the module keeps its home learning outcomes: students must analyse consumer behaviour using recognised theories, evaluate marketing strategies and communicate recommendations in professional form. The level, the marking criteria and the pass standard are identical at both campuses, and samples of marked work are moderated across sites. In the adaptive layer, the branch campus teacher replaces several home case studies with cases on regional retail, family-owned businesses and seasonal consumer patterns that matter in the local economy. Group work is organised with attention to students' expectations about mixed-gender collaboration where this is relevant, while still developing teamwork skills. Students write a report on a regional brand rather than a British one, assessed against the same criteria as the home report. Extra sessions support academic writing in English, without changing what is assessed. In the contextual layer, the module respects local rules on advertising content and cultural sensitivities, and it links to employers in the local market for guest talks. Has the standard changed? No. Every student is still judged against the same outcomes and criteria, and moderation checks this. Has the curriculum become more relevant? Yes. The example shows that most of the adaptation needed for cultural alignment happens in the adaptive layer, where it can be documented, approved and defended. 7. Conclusion This article set out to examine how international branch campuses can adapt imported curricula to the socio-cultural contexts of local students without compromising the standards on which the home institution's accreditation depends. Drawing on a critical integrative review of recent research, it proposed the Layered Alignment Model, which separates a protected core of standards from an adaptive layer of delivery and a contextual layer of national rules, culture and labour market needs. It then analysed six mechanisms through which alignment is managed: outcome anchoring, assessment equivalence, co-design and shared governance, mandated national content, pedagogical mediation by teachers, and student agency. Four main findings emerge. First, equivalence is best understood as equivalence of standards and learning, not uniformity of content. Second, much adaptation at branch campuses remains narrow and compliance-driven, often limited to required national content, while academic control remains with the home campus. Third, a large share of real alignment work is carried out informally by teachers and students, which makes it fragile and unevenly distributed. Fourth, a clearer separation of layers, explicit decision rights, contextualised but moderated assessment, investment in staff and partnership with students can allow branch campuses to become more culturally responsive while strengthening rather than weakening the defensibility of their awards. The study has limits that readers should keep in mind. It is a conceptual review rather than a new empirical study, and it cannot say how common each mechanism is. The available research is concentrated in China and the Gulf, and fewer studies examine branch campuses in Africa, Latin America or South Asia. Much of the evidence comes from interviews with relatively small numbers of participants, which offers depth but limits generalisation. The decision-rights matrix and the spectrum of localisation are proposed as tools for thinking and should be tested in real institutions. Future research could take several directions. Comparative studies could examine how the same programme is adapted at different branch campuses of one university. Studies of assessment could test whether contextualised tasks with shared criteria produce comparable standards in practice. Research could also follow the flow of knowledge from branch campuses back to home campuses, asking whether TNE can become a genuinely two-way exchange. Finally, more research that centres the voices of local students and teachers would strengthen understanding of what cultural alignment means to the people who live it. Branch campuses will continue to grow in some regions and close in others, and the geopolitical setting in which they work is changing. Whatever their future, the question at the centre of this article will remain. A degree that claims to be the same everywhere must find a way to be meaningful somewhere. The evidence suggests that this is possible, but only when institutions stop treating sameness as the definition of quality and start treating equivalent learning, achieved in context, as the real standard. References Alamad, S. (2026). Examining transnational education in British universities for enhancing internationalisation: The impact of constructivism on alignment. Future in Educational Research, 4(1), 56-73. https://doi.org/10.1002/fer3.70035 Barr, M., Relja, R., Ward, P., Hill, J. L., Tran, Q. P., Hoang, D. T. 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Advance online publication. https://doi.org/10.1080/07294360.2026.2681641 Hashtags #transnational_education #international_branch_campuses #curriculum_adaptation #cultural_alignment #quality_assurance #accreditation_standards #TNE_curriculum #glocal_curriculum #culturally_responsive_teaching #higher_education_internationalisation #branch_campus_students #academic_equivalence #localised_learning #global_degrees_local_lives #STULIB

Extended reality (XR), the family of technologies that includes virtual, augmented, and mixed reality, is moving from isolated pilot projects into regular teaching. Headsets marketed as spatial computing devices now let learners see and handle digital objects placed in real space. Yet many schools and universities adopt these tools before they have a clear plan for where immersion belongs in a curriculum, how it should be sequenced, and how its effects on learning should be measured. This article addresses that gap. It is a conceptual study built on an integrative review of recent systematic reviews, meta-analyses, and controlled experiments, most published between 2022 and 2025. The review shows that immersive learning tends to produce small to moderate gains over less immersive methods, that the gains are larger when learners act, manipulate, and create rather than watch, and that poorly designed immersion can overload learners and reduce understanding. Drawing on the Cognitive Affective Model of Immersive Learning, multimedia learning research, and experiential learning theory, the article proposes a five-layer Immersive Curriculum Design Framework, a five-phase design cycle, a pre-brief, immersion, and debrief structure for linking theory to practice, and a measurement model that separates immediate recall, delayed retention, transfer, and real-world performance. The article closes with practical guidance for curriculum designers and teachers, and with a research agenda that calls for longer studies and better retention measures. Keywords: extended reality, spatial computing, curriculum design, immersive learning, virtual reality, cognitive retention, experiential learning, theory-practice gap 1. Introduction Every generation of teachers meets a new technology that promises to change the classroom. Film, television, the personal computer, and the internet were each expected to reshape how people learn, and each ended up being useful in some places and disappointing in others. The lesson from that history is simple. A technology does not improve learning on its own. What matters is how it is placed inside a #curriculum, what learners are asked to do with it, and whether anyone checks what they actually learned. #Extended_reality (XR) is the newest technology to face this test. XR is an umbrella term for three related experiences. In virtual reality (VR), the learner wears a headset and is surrounded by a computer-generated world. In augmented reality (AR), digital information is laid over the real world, usually through a phone, a tablet, or glasses. In mixed reality (MR), digital objects are anchored in the real room and can be moved and handled as if they were physically there. In recent years, technology companies have started to describe the newest headsets as #spatial_computing devices. The term points to a shift in how people use computers: instead of looking at a flat screen, the user works with information that sits in three-dimensional space around them, often controlled by hand movements, eye gaze, and voice. For education, this shift matters because many things students struggle to learn are spatial, dangerous, expensive, or invisible. A student in #medical_education needs to understand how organs sit in relation to one another. An #engineering_education student needs to see how forces move through a structure. A chemistry student needs to picture molecules that no one can see with the naked eye. A trainee in #vocational_training needs to practise procedures where a mistake could be fatal. In each case, a textbook or a lecture gives the theory, but the student has few safe chances to experience the thing itself. This distance between what students are told and what they can do is often called the #theory_practice_gap, and it is one of the oldest problems in professional and vocational education. XR seems well suited to narrowing this gap, especially in #professional_education. It can put learners inside a heart, at the base of a volcano, or on a factory floor, and let them act, make mistakes, and try again. The research evidence is encouraging but mixed. Meta-analyses report positive average effects of #immersive_learning on learning outcomes (Coban et al., 2022; Wu et al., 2020), while some carefully controlled experiments show that immersion can also distract learners and lead to weaker understanding than a simple slideshow (Parong & Mayer, 2021). A large review of training studies found that VR, AR, and MR training were, on average, about as effective as conventional training, which suggests that their value lies mainly in situations where conventional training is unsafe, costly, or impossible (Kaplan et al., 2021). This mixed picture points to a design problem rather than a technology problem. Several reviews have noted that many XR learning applications are built without a clear learning theory behind them and are evaluated mainly for usability rather than for learning (Radianti et al., 2020). A review in health professions education found that very few XR studies used any instructional design model at all (Asoodar et al., 2024). In other words, the field has many promising tools but few shared frameworks for building them into a #curriculum_design process. 1.1 Aim and research questions The aim of this article is to develop theoretical and practical frameworks that help educators design curricula that use immersive environments well. It asks three questions. First, what does recent research say about when and why XR improves learning? Second, how can curriculum designers turn that evidence into a structured design process that links classroom theory with real practice? Third, how should the effects of immersive curricula on #cognitive_retention and transfer be measured, so that schools can judge whether their investment is working? 1.2 Contribution and structure of the article The article makes four contributions. It brings together recent evidence on immersive learning in a form that students and new researchers can follow. It proposes a layered framework that connects learning theory to curriculum decisions and measured outcomes. It offers a practical design cycle and a simple structure for linking immersive experiences to theory and practice. Finally, it sets out a measurement model that treats retention and transfer as separate outcomes that must be tested over time. Section 2 explains the approach. Section 3 reviews the literature. Section 4 presents the theoretical framework. Section 5 turns the framework into practice. Section 6 deals with measurement. Section 7 discusses implications, limits, and future research, and Section 8 concludes. 2. Approach and Method This is a conceptual article supported by an integrative literature review. Integrative reviews bring together different types of evidence, such as experiments, meta-analyses, and qualitative studies, to build or refine a framework. This type of review suits the present aim because the questions are about design and theory, not about a single effect size. The #integrative_review focused on peer-reviewed journal articles and scholarly books in English. Priority was given to work published from 2022 onwards, so that the framework reflects the current state of the field. A small number of earlier but heavily cited works from 2020 and 2021 were kept where they provide the theoretical base that later studies build on, such as the Cognitive Affective Model of Immersive Learning (Makransky & Petersen, 2021). Four types of sources were included: meta-analyses of learning outcomes, systematic reviews of design and practice, controlled experiments that test specific design features, and studies of teacher attitudes and adoption barriers. The analysis followed three steps. First, findings were grouped by theme, for example the effect of immersion on learning, the role of interactivity, the risks of cognitive overload, and the problems of integration into existing programmes. Second, recurring explanations for success and failure were identified. Third, these explanations were organised into frameworks that link theory, design, and measurement. Because this is a conceptual study, the frameworks are proposals grounded in existing evidence. They are not tested here, and Section 7 explains how they could be tested. 3. Literature Review 3.1 Defining the field: XR, immersion, and spatial computing Researchers in #educational_technology use the words immersion and presence in a specific way. #Immersion usually refers to the technical features of a system, such as a wide field of view, head tracking, and spatial sound, that cut the user off from the outside world. #Presence is the psychological feeling of being there, inside the virtual place. A headset that fills the whole visual field is more immersive than a desktop screen, and it usually produces a stronger sense of presence (Makransky & Mayer, 2022). The term #metaverse is also common in this area. It describes persistent, shared virtual spaces where people meet and work through avatars. Mystakidis (2022) describes the metaverse as a convergence of XR technologies with social and networked platforms, and notes both its educational promise and its limits. For curriculum design, the key point is that XR is not one thing. A phone-based AR app, a fully immersive VR simulation, and a shared mixed reality classroom each offer different possibilities and different risks, and a curriculum needs to match each to the right learning goal. Mixed reality headsets deserve special mention because they are closest to what is now called spatial computing. A case study of HoloLens 2 in a vocational nursing programme found that the device improved student motivation and learning, but it also found technical and design limits and a poor fit with the wider learner journey (Adams et al., 2022). This finding is typical. The newest devices can do impressive things, but their place in a programme of study has to be designed. It is worth noting that peer-reviewed evidence on the very latest consumer spatial computing headsets in education is still thin, so much of what we know comes from earlier VR, AR, and MR research. 3.2 What meta-analyses say about learning outcomes A #meta_analysis combines the results of many studies and gives an average effect. Several recent meta-analyses report positive effects for immersive VR. Wu et al. (2020) analysed 35 studies and found that head-mounted display VR outperformed non-immersive approaches with a small effect size of 0.24. The effect was larger for school-age learners, for science subjects, for simulation-based designs, and when VR was compared with lectures. Importantly, the review found that the learning gains were kept over time. Coban et al. (2022) analysed 48 studies with 105 effect sizes and found a small positive overall effect of immersive VR on learning, with an effect size of 0.38. In primary education, Villena-Taranilla et al. (2022) analysed 21 studies and found a larger overall effect of 0.64. They also found that fully immersive systems had a much larger effect (1.11) than semi-immersive (0.19) or non-immersive (0.32) systems, and that shorter interventions of under two hours had larger effects than longer ones. Figure 4, presented later in Section 6, shows these values side by side. AR research shows a similar pattern. A meta-analysis of 134 studies covering ten years of AR in education found positive effects on learner responses, knowledge and skills, and performance, with the largest effect on performance. The length of the intervention also mattered (Chang et al., 2022). These results are encouraging, but they need careful reading. An average effect hides wide variation. Some studies show large gains, others show none, and a few show losses. The question for a curriculum designer is not whether XR works on average, but under what conditions it works for a particular group of learners and a particular goal. 3.3 When immersion helps and when it hurts Controlled experiments help answer that question. In one of the clearest positive studies, Makransky and Mayer (2022) compared a virtual field trip taken in a headset with the same trip shown as a two-dimensional video. The 102 middle-school students who used the headset reported higher presence, enjoyment, and interest, and they also scored higher on retention, both immediately and on a delayed test. The authors call this the immersion principle: when an immersive lesson is well designed, the extra sense of presence can support deeper engagement and better memory. Other studies show the opposite. Parong and Mayer (2021) found that students who learned biology through an immersive VR lesson performed worse on transfer tests than students who watched a desktop slideshow. The VR group showed higher emotional arousal and higher extraneous load, which is mental effort spent on things that do not help learning. Brain activity measures also suggested lower engagement with the content. In short, the virtual world was exciting, but the excitement got in the way. Mayer et al. (2023) bring these findings together under the idea of promise and pitfalls. Immersive VR can encourage the kind of active, sense-making thinking that leads to learning. But it can also pull attention towards features that are vivid but irrelevant. The solution, they argue, is to add instructional supports that guide learners towards the core material. This point is central to curriculum design. #Immersion is a powerful ingredient, but it needs a recipe. 3.4 The role of interactivity and embodiment A growing body of work on #learner_agency shows that what learners do in XR matters more than which device they use. Johnson-Glenberg et al. (2021) compared desktop and VR versions of STEM lessons at different levels of physical involvement. They found that #embodied_learning and a sense of agency drove learning more than the platform did. Highly embodied VR lessons produced the best learning and retention, while passive VR lessons did worse than both desktop versions. The title of their study, which states that platform is not destiny, sums up the finding well. Petersen et al. (2022) tested immersion and interactivity in a two by two experiment with 153 participants. They found that situational interest and embodied learning carried the learning process, and that immersion and interactivity each had separate and combined effects on cognitive load, presence, agency, and embodiment. A systematic review by Conrad et al. (2024) reached a similar practical conclusion. Across 30 studies, immersive VR generally performed better than other media, and it worked best when learners were actively manipulating or creating things, rather than only watching. For procedural skills, Jongbloed et al. (2024) analysed 16 studies and found a medium-sized advantage for immersive procedural training over less immersive environments, with the largest effects on transfer. This is important for the theory-practice gap, because #transfer_of_learning is the ability to use what was learned in a new situation, which is exactly what professional practice demands. 3.5 Instructional design features that support learning If immersion needs guidance, which kinds of guidance work? Research on #multimedia_learning offers clear answers. The Cambridge Handbook of Multimedia Learning brings together many principles, such as segmenting complex lessons into parts, reducing irrelevant detail, and prompting learners to explain what they see (Mayer & Fiorella, 2021). Klingenberg et al. (2023) tested two of these ideas in immersive VR with 190 sixth and seventh graders. Splitting the lesson into segments, known as #segmenting, or asking learners to write summaries, each improved transfer. Using both together gave no extra benefit, and neither improved simple factual knowledge. This study offers a useful warning for curriculum designers. Different supports affect different outcomes. A support that helps transfer may do little for recall, and adding more supports is not always better. Good #instructional_design therefore requires matching each support to the outcome that matters most. 3.6 Theory-based design and the experiential learning cycle Several reviews have asked whether XR applications are designed with learning theory in mind. Radianti et al. (2020) mapped 18 application areas in higher education and found that learning theories rarely guided VR design. Lui et al. (2023) reviewed theory-based learning design with immersive VR in science education, and Marougkas et al. (2023) reviewed learning theories, approaches, and methods used in VR education over a decade. Together, these reviews show a field that is slowly moving from technology-led projects towards theory-led design. One theory that fits XR well is #experiential_learning. In the #Kolb_cycle, learners move through concrete experience, reflective observation, abstract conceptualisation, and active experimentation. Fromm et al. (2021) used design workshops and focus groups to identify VR design elements that support all four stages of this cycle. Crogman et al. (2025) combined a literature review with pilot classes and faculty training, and reported gains in performance, engagement, and inclusivity from XR-supported #experiential_learning, together with problems of access, cost, and cognitive overload. 3.7 Integration into curricula and adoption barriers Even good XR lessons can fail if they are added to a programme without planning. Mergen et al. (2024) reviewed 69 reviews of VR in medical education and found mostly positive outcomes, but also financial, technical, and pedagogical barriers to integrating VR into curricula, as well as a lack of standard guidelines for #evaluation. Asoodar et al. (2024) reviewed 184 studies of XR in health professions education and found that very few used an instructional design model. They proposed adapting an established design model as a framework for placing XR within the curriculum. Teachers are a key part of the picture, and #technology_adoption depends heavily on them. Interviews with educators in Saudi Arabia showed real interest in XR, along with concerns about awareness, access to suitable content, the readiness of teachers and students, and wider institutional and social challenges (Meccawy, 2023). A survey of 158 Swiss mentor teachers found moderately positive attitudes towards AR, but little experience with it and a lack of technical skills, teaching skills, and resources. The teachers said they needed training and support (Wyss & Bauerlein, 2024). These findings show that #teacher_readiness is not a side issue. A curriculum that depends on XR must also plan for the people who will teach it. 3.8 Gaps in the literature The review points to four gaps. First, many studies are short, often a single session, and rarely test retention after days or weeks (Hamilton et al., 2021). Second, outcome measures are often weak or limited to recall, so we know less about transfer and real-world performance. Third, most studies test single lessons rather than whole units or programmes, so evidence about curriculum-level integration is limited. Fourth, few studies describe a design process that others could repeat. The frameworks proposed below are meant to address these gaps. 4. Theoretical Framework: The Immersive Curriculum Design Framework This section builds a framework from the evidence. It draws on three bodies of theory. The first is the #CAMIL model, the Cognitive Affective Model of Immersive Learning. The second is research on cognitive load and multimedia learning. The third is experiential learning theory. Each explains part of the picture, and together they guide curriculum decisions. 4.1 The Cognitive Affective Model of Immersive Learning Makransky and Petersen (2021) proposed CAMIL to explain how immersive VR affects learning. The model starts with two features of immersive technology: presence, the feeling of being there, and agency, the feeling of being in control. These two features do not lead to learning directly. Instead, they work through six factors: interest, intrinsic motivation, self-efficacy, embodiment, cognitive load, and self-regulation. When presence and agency raise interest and motivation and support embodiment without overloading the learner, learning improves. When they raise cognitive load or weaken self-regulation, learning may suffer. CAMIL is useful for curriculum design because it explains the mixed results in the literature. It tells designers that the goal is not maximum immersion, but the right balance of presence and agency for the learning task, with careful control of load. Later experiments have supported and extended the model (Petersen et al., 2022). 4.2 Cognitive load and multimedia principles #Cognitive_load_theory holds that working memory has limited capacity. When a learner must process too much at once, especially information that is not relevant to the goal, learning breaks down. Immersive environments are rich in sights, sounds, and movement, so they can easily push learners past this limit. The principles of multimedia learning, such as segmenting, signalling key information, and removing extra detail, offer ways to manage this load (Mayer & Fiorella, 2021). The VR experiments reviewed above show that these principles still apply inside a headset (Klingenberg et al., 2023; Parong & Mayer, 2021). 4.3 Experiential and embodied learning Experiential learning theory explains why XR can bridge theory and practice. Classroom teaching often begins with abstract ideas and rarely reaches concrete experience. XR can supply the missing experience in a safe, repeatable form. Embodied cognition adds that physical movement and gesture are part of thinking, not separate from it. When learners use their hands and bodies to act on a concept, such as rotating a molecule or tracing the path of blood, they build richer mental models (Johnson-Glenberg et al., 2021). 4.4 The five layers of the framework Combining these theories, this article proposes the #Immersive_Curriculum_Design_Framework (ICDF). Figure 1 shows its five layers. Figure 1. The Immersive Curriculum Design Framework (ICDF), showing five layers from learning theory to evaluation, with a feedback loop to design decisions. Layer 1 is the theoretical foundation. It reminds designers that every XR activity should be justified by a learning theory. The four theories named in the figure are not the only possible choices, but they are the ones with the strongest evidence in immersive learning. Layer 2 contains the curriculum design decisions. Here the designer asks which learning outcomes truly need space, risk, or scale that cannot be provided another way. The designer then chooses the right XR modality, decides how the immersive activity will be sequenced with other activities, and adds supports such as segmenting and summarising. Layer 3 has two parts. Layer 3a describes the affordances of the immersive environment: presence, agency and interactivity, and embodiment. Layer 3b describes the learner factors through which those affordances work, based on CAMIL: interest, motivation, self-efficacy, self-regulation, and managed cognitive load. The arrow between them shows that affordances only matter through their effect on the learner. Layer 4 sets out the outcomes. It separates immediate recall, delayed retention, transfer to new problems, and performance in real or simulated practice. This separation is important because the literature shows that a design can improve one of these without improving the others (Klingenberg et al., 2023). Layer 5 is the evaluation and revision loop. Assessment evidence returns to Layer 2, so that the design, the length of exposure, and the debrief can be adjusted. This loop turns the framework from a one-time plan into a continuous improvement process. 4.5 Propositions derived from the framework The framework leads to six propositions that can guide practice and be tested in future research. Each is grounded in the evidence reviewed above. Proposition 1. XR adds the most value to a curriculum when it is reserved for outcomes that are spatial, dangerous, costly, or impossible to experience directly. This follows from the finding that XR training is, on average, about as effective as conventional training, which means its main advantage lies where conventional training is not available (Kaplan et al., 2021). Proposition 2. Active and embodied tasks in XR will produce stronger learning than passive viewing in XR. This is supported by evidence that passive VR can be worse than desktop learning, while embodied VR can be better (Conrad et al., 2024; Johnson-Glenberg et al., 2021). Proposition 3. Immersive activities supported by instructional design features such as segmenting, signalling, and generative tasks will produce better transfer than unsupported immersion (Klingenberg et al., 2023; Mayer et al., 2023). Proposition 4. Short, focused immersive sessions embedded in a wider sequence will be more effective than long, unstructured exposure. This is consistent with the finding that shorter interventions showed larger effects in primary education (Villena-Taranilla et al., 2022). Proposition 5. Immersive learning will narrow the theory-practice gap most when it is placed between explicit theory teaching and real practice, and when it is followed by structured reflection (Crogman et al., 2025; Fromm et al., 2021). Proposition 6. The benefits of XR for retention can only be judged through delayed testing and transfer tasks, not through immediate recall alone (Hamilton et al., 2021; Makransky & Mayer, 2022). 5. From Framework to Practice: Designing the Immersive Curriculum A framework is only useful if it can be applied. This section turns the ICDF into a practical design process. It presents a five-phase design cycle, a structure for bridging theory and practice, and three short design examples. 5.1 A five-phase design cycle Figure 2 shows the design cycle. It has five phases: Analyse, Align, Design, Integrate, and Evaluate. The phases form a loop, with learner and curriculum outcomes at the centre. Figure 2. The five-phase immersive curriculum design cycle: Analyse, Align, Design, Integrate, and Evaluate. Phase 1, Analyse. The designer studies the learners, the intended learning outcomes, and the constraints. Who are the students and what do they already know? Which outcomes have students found hard in the past? What equipment, space, time, and support staff are available? This phase also checks for #accessibility needs. Some learners experience motion sickness, some have visual or physical impairments, and some may feel anxious in a headset. A curriculum must offer equivalent alternatives for these learners. Phase 2, Align. The designer matches each outcome to the XR affordance that best supports it. If the outcome is about understanding spatial structure, such as the layout of a building or the shape of a protein, a 3D model that can be walked around or turned in the hand is a strong match. If the outcome is about procedure under risk, such as handling a chemical spill, a fully immersive simulation is a strong match. If the outcome is about simple facts, XR is usually not needed. This alignment step applies Proposition 1 and prevents XR from being used just because it is available. Phase 3, Design. The designer builds or selects the immersive scenario and plans the tasks. The tasks should require learners to act, decide, and create, not simply watch. Supports should be added: the lesson can be split into short segments, key information can be highlighted, and learners can be asked to explain or summarise at the end of each segment. Distractions that add excitement but not learning should be removed. This phase applies Propositions 2, 3, and 4. Phase 4, Integrate. The designer places the immersive activity inside the wider unit. This is where the gap between theory and practice is addressed directly, using the bridge structure described in the next section. Integration also includes practical matters such as timetabling, group sizes, device hygiene, and the role of the teacher during the session. Phase 5, Evaluate. The designer collects evidence about learning for #continuous_improvement, using the measurement model in Section 6. Results feed back into Phase 1 for the next cycle. Over time, this loop builds a body of local evidence about what works for a particular programme and group of students. Readers familiar with instructional design will recognise similarities to established models such as ADDIE. The difference is in the Align and Integrate phases, which are specific to immersive learning. Asoodar et al. (2024) noted that XR studies rarely use any instructional design model, so even a simple, explicit cycle like this one would be an improvement on current practice. 5.2 Matching XR modality to learning outcomes One of the most practical decisions in the Align phase is which form of XR to use. The three forms of XR do different jobs, and choosing the wrong one wastes time and money. A simple way to think about the choice is to ask how much of the real world the learner needs to keep. #Augmented_reality keeps the real world in view and adds information on top of it. It suits outcomes where learners must connect digital information to real objects, for example labelling the parts of a real engine, seeing hidden pipes behind a wall, or following step-by-step instructions while handling real equipment. Because AR often runs on phones and tablets that schools already own, it can also be the most affordable starting point. The ten-year AR meta-analysis found its strongest effects on performance, which fits this kind of hands-on use (Chang et al., 2022). Fully immersive #virtual_reality replaces the real world. It suits outcomes where the real setting is unavailable, dangerous, or too large or too small to visit, such as the inside of a cell, the surface of another planet, a burning building, or an operating theatre during an emergency. It also suits practice that needs many repetitions, because a scenario can be reset in seconds. The strong subgroup effect for fully immersive systems in primary education suggests that, when the content suits it, full immersion can make a real difference (Villena-Taranilla et al., 2022). #Mixed_reality sits between the two. It anchors digital objects in the real room, so that a group of students can stand around the same virtual model, point at it, and discuss it while still seeing one another. This makes it well suited to collaborative and spatial tasks, such as examining an anatomical model together or planning the layout of a workspace. It is also the form most closely linked with current spatial computing headsets. However, as the HoloLens case showed, the technology still has limits, and designers should test it carefully with real students before committing a whole course to it (Adams et al., 2022). Finally, designers should remember the option of using no XR at all. If an outcome can be met just as well with a diagram, a video, a physical model, or a real visit, then those options are usually cheaper and simpler. Choosing not to use XR is a valid design decision, and in many parts of a curriculum it will be the right one. 5.3 Bridging theory and practice: the XR bridge The phrase theory-practice gap describes a familiar experience. Students pass exams on the theory, but when they enter a clinic, a laboratory, a building site, or a workplace, they struggle to apply it. XR can act as a bridge between the two, but only if it is placed and structured with care. Figure 3 shows a simple structure for this bridge. Figure 3. The XR bridge: a pre-brief, immersive practice, and debrief sequence that links classroom theory to authentic practice, mapped onto the experiential learning cycle. The bridge has three parts. The #pre_brief comes first. Before entering the immersive environment, learners review the key concepts and are told what they will be asked to do and why. This prepares working memory and reduces the risk that learners will be overwhelmed by novelty. It also tells them which parts of the experience matter most. The second part is immersive practice. Learners enter the #virtual_simulation and act on what they have learned. They make decisions, see the results, and try again. A nursing student might assess a virtual patient whose condition changes over time. An engineering student might load a virtual bridge and watch where it fails. The aim is #hands_on_learning through concrete experience and active experimentation, the two stages of the experiential learning cycle that classroom teaching most often misses. The third part is the #debrief. After the immersive session, learners reflect on what happened, explain their decisions, and link what they saw to the theory. The teacher asks questions such as: Why did the patient's condition get worse? Which principle explains where the bridge failed? What would you do differently? Debriefing turns experience into understanding. Without it, learners may enjoy the session but fail to connect it to the concepts they need. The finding that summarising activities improved transfer in VR supports this step (Klingenberg et al., 2023). After the bridge, learners move into authentic practice, such as a clinical placement, a laboratory session, or a work-based project. The dashed line in Figure 3 shows a return path. Errors and difficulties met in real practice should feed into the next pre-brief, so that the immersive curriculum responds to what learners actually struggle with. This loop is what makes the bridge a two-way connection rather than a one-way path. 5.4 Design example: a nursing skills unit The following three examples show how the framework could be applied. They are illustrative designs, not reports of completed studies. Consider a second-year nursing unit on recognising a deteriorating patient. In the Analyse phase, the teaching team notes that students can list the warning signs in exams but often miss them in placement. The Align phase identifies the outcome as a decision-making skill under time pressure, which is hard to practise safely with real patients. This makes it a good candidate for XR. In the Design phase, the team builds a short immersive scenario, split into three segments, in which a virtual patient's vital signs change. At the end of each segment, students must record their observations and state their next action. In the Integrate phase, the scenario is placed after the lecture on early warning scores and before the first clinical placement, with a pre-brief and a structured debrief. In the Evaluate phase, students take a short test straight after the session, a delayed test three weeks later, and are observed during placement using an existing clinical assessment form. This design draws on findings from health education, where VR outcomes are mostly positive but integration into curricula is a persistent challenge (Mergen et al., 2024), and on the HoloLens case in nursing education, where motivation and learning improved but the fit with the wider learner journey needed more work (Adams et al., 2022). 5.5 Design example: a secondary school science unit Consider a unit on cell biology for students aged 13 to 15. The Analyse phase shows that students struggle to picture the structures inside a cell and how they relate to one another. The Align phase identifies this as a spatial understanding outcome. In the Design phase, the team selects an immersive cell model that students can walk through and interact with, for example by moving a molecule through the cell membrane. The lesson is broken into short segments, each focused on one structure. Students write a short summary after each segment. The Integrate phase places the XR session after an introductory lesson and before a practical microscope session, so that students first learn the names, then explore the structures in three dimensions, then look at real cells. This design takes account of Parong and Mayer's (2021) warning that an immersive biology lesson can lead to worse transfer than a simple slideshow if it overloads learners. Segmenting and summarising are used to reduce that risk. It also follows evidence that effects for school-age learners and for science subjects tend to be larger (Wu et al., 2020). 5.6 Design example: an engineering safety unit Consider an engineering or vocational unit on working safely with high-voltage equipment. Mistakes here can be fatal, so real practice must be tightly controlled. In the Align phase, the team identifies procedural outcomes, such as following a lock-out sequence, as a strong match for immersive procedural training. The Design phase creates a simulation in which learners carry out the full procedure with their hands, receive feedback on errors, and repeat until they reach a set standard. The Integrate phase places the simulation between classroom instruction on electrical safety standards and supervised work on real equipment. This example draws on the meta-analytic finding that immersive procedural training has a medium-sized advantage over less immersive methods, with the strongest effects on transfer (Jongbloed et al., 2024). It also reflects the broader view that XR training is most valuable where conventional training is dangerous or costly (Kaplan et al., 2021). 5.7 Supporting teachers and institutions A curriculum is only as strong as the people who deliver it. The studies of teacher attitudes reviewed above show interest in XR, but also gaps in skills, resources, and confidence (Meccawy, 2023; Wyss & Bauerlein, 2024). Institutions that adopt an immersive curriculum should therefore plan for #professional_development. Teachers need time to try the tools themselves, guidance on running pre-briefs and debriefs, and technical support during sessions. They also need a voice in the design process, since they understand what students find difficult. Institutions also need to think about cost and fairness. Headsets are expensive, need maintenance, and may not be available to every student. #Digital_equity requires that no student is disadvantaged because they cannot use a headset, whether for reasons of cost, disability, or health. One practical approach is to design each immersive activity with a lower-tech alternative, such as a desktop or tablet version, and to check that both versions support the same learning outcome. 5.8 Common design mistakes to avoid The research reviewed above also points to mistakes that recur in immersive projects. Naming them helps designers avoid them. The first mistake is the #novelty_effect trap. A new headset is exciting, and the first session often produces strong enthusiasm. It is tempting to read that enthusiasm as proof of learning. But excitement is not the same as understanding, and the extra arousal can even get in the way, as Parong and Mayer (2021) found. Designers should judge an immersive lesson by what learners can do afterwards, not by how much they enjoyed it. The second mistake is passive immersion. Some XR content simply places the learner inside a 360-degree video or a guided tour with nothing to do. The evidence suggests that passive VR can be less effective than a well-made desktop lesson (Johnson-Glenberg et al., 2021). If learners are not making decisions, handling objects, or creating something, the designer should ask whether immersion is adding anything at all. The third mistake is overload. Designers sometimes fill a virtual world with detail because they can. Every extra sound, animation, and object competes for the learner's limited attention. Removing what does not serve the learning goal is one of the simplest ways to improve an immersive lesson. The fourth mistake is the island lesson. An XR session that is not connected to what comes before and after it tends to be remembered as a fun day rather than as part of the course. Without a pre-brief, learners do not know what to look for. Without a debrief, they do not connect what they saw to the theory. The XR bridge described above exists to prevent this. The fifth mistake is measuring too early. If the only test is a quiz straight after the session, the designer cannot know whether the learning lasted or whether it transfers to practice. Section 6 explains how to measure more fully. 6. Measuring Impact: Cognitive Retention, Transfer, and Performance The final part of the framework deals with measurement. Many XR studies end with a test given straight after the session. This tells us something about short-term recall, but it says little about whether learners will remember the material weeks later, or whether they can use it in practice. Hamilton et al. (2021) found that most immersive VR studies were short and rarely tested retention. If schools and universities are to make sound decisions about XR, they need better ways of measuring its effects. 6.1 Four outcomes worth separating The ICDF separates four outcomes. Immediate recall is what a learner can remember straight after the lesson. Delayed retention is what they still remember after a gap of days or weeks. #Knowledge_transfer is the ability to use what was learned to solve a new problem. Performance is what the learner can do in a real or realistic setting. These outcomes are related, but they are not the same, and a design can affect them differently. Klingenberg et al. (2023), for example, found that supports in VR improved transfer but not factual knowledge. #Cognitive_retention deserves special attention because it is often what teachers mean when they say they want learning to last. Evidence on retention in XR is promising. Makransky and Mayer (2022) found that the immersive field trip group outperformed the video group on a delayed retention test, not only on an immediate one. Wu et al. (2020) also reported that learning gains from head-mounted display VR were maintained over time. Johnson-Glenberg et al. (2021) found that highly embodied VR produced the strongest retention. These findings suggest that when immersion is combined with active, embodied tasks, it may help learners build memories that last. But the number of studies with delayed tests is still small. Figure 4 summarises the pooled effect sizes reported in three of the meta-analyses discussed in this article. The dotted lines mark the conventional thresholds for small, medium, and large effects. Figure 4. Pooled effect sizes of virtual reality on learning outcomes reported in three meta-analyses. Values from Wu et al. (2020), Coban et al. (2022), and Villena-Taranilla et al. (2022). The figure shows two things. First, the average effects of immersive VR are positive but mostly small to moderate. Second, the subgroup values from Villena-Taranilla et al. (2022) suggest that the level of immersion matters a great deal, at least for young learners. These values should be read with care, because the meta-analyses differ in the studies they include, the age groups they cover, and the outcomes they measure. 6.2 A measurement model for immersive curricula Figure 5 sets out a simple measurement model that any teacher or curriculum designer can adapt. It places five measurement points on a timeline. Figure 5. A measurement timeline for immersive curricula, separating pre-testing, process data, immediate testing, delayed retention, and practice-based assessment. The first point is a #pre_test, given before the immersive activity. It measures prior knowledge, which is needed to calculate learning gains, and it can also measure interest and self-efficacy, two of the CAMIL factors. The second point is the immersive lesson itself, during which process data can be collected. Many XR systems can record how long learners spend on each task, which errors they make, and which objects they interact with. Short questionnaires can capture presence and perceived #mental_workload immediately afterwards. The third point is an immediate post-test. It should include both recall questions and transfer questions. Recall questions ask learners to state or recognise what they learned. Transfer questions ask them to apply it to a new case. The fourth point is a #delayed_post_test, given two to four weeks later. It uses new but equivalent questions, so that learners are not simply remembering the test. The fifth point is a practice-based assessment, such as an observed clinical skill, a laboratory task, or a workplace evaluation. This #performance_assessment is the true test of whether the theory-practice gap has narrowed. The model also suggests a comparison group where possible. In a school or university setting, a full controlled experiment may not be practical, but teachers can often compare cohorts, or compare the immersive version of a lesson with a well-designed non-immersive version. The comparison should be fair. Comparing a carefully designed VR lesson with a poorly designed lecture tells us little. As Wu et al. (2020) found, effects were larger when VR was compared with lectures, which shows how much the choice of comparison can shape the result. 6.3 Using learning analytics responsibly XR devices can collect very detailed data, including head and hand movements and, in some newer devices, eye gaze. This kind of #learning_analytics can help teachers understand how learners engage with a task and where they struggle. It also raises serious questions about privacy and consent. Learners should be told what data is collected, how it will be used, and who will see it. Data that is not needed for learning should not be collected. Institutions should treat XR data with at least the same care as other sensitive student records. 6.4 Measuring affective outcomes Interest, motivation, and enjoyment are often treated as side effects of XR. CAMIL suggests a different view: they are part of the path through which immersion affects learning (Makransky & Petersen, 2021). Measuring them helps explain why a design worked or failed. However, high enjoyment is not the same as high learning. Parong and Mayer (2021) showed that learners in VR can feel more engaged while learning less. Affective measures should therefore be read alongside learning measures, never in place of them. 7. Discussion 7.1 Main findings The review and frameworks presented here lead to four main findings. First, immersive learning can improve learning outcomes, but the average effects are small to moderate, and they vary a great deal between studies. Second, the variation is explained more by design than by device. Active, embodied, and well-supported immersive tasks tend to work. Passive or overloaded immersion tends not to. Third, XR is most valuable for outcomes that are spatial, risky, or hard to experience directly, and it can help bridge the theory-practice gap when it is placed between theory and real practice and followed by structured reflection. Fourth, the evidence on long-term retention and real-world performance is promising but still limited, because few studies measure these outcomes well. 7.2 Implications for curriculum designers For curriculum designers, the main implication is to start with outcomes, not with technology. The question should not be how to use the new headsets, but which learning outcomes our students struggle with that immersion could help. The ICDF and the five-phase cycle give a structure for answering that question. Designers should also plan for the whole sequence, not just the immersive moment. The pre-brief and debrief are as important as the virtual experience itself. 7.3 Implications for teachers For teachers, XR changes their role during the lesson. While learners are inside a headset, the teacher cannot see what they see unless the system mirrors the view to a screen. Teachers need to plan how they will observe, support, and step in. After the session, the teacher's role as a guide of reflection becomes central. Teachers should also watch for learners who are uncomfortable, anxious, or unwell, and be ready to switch them to an alternative activity. 7.4 Implications for students This article is written for students, so it is worth addressing them directly. If your course uses XR, treat the immersive session as practice, not entertainment. Before you start, make sure you know what you are supposed to learn. During the session, try things, make mistakes, and notice what happens. Afterwards, take time to explain to yourself or to a classmate what you saw and why it happened. That last step, putting the experience into words, is where much of the learning takes place. If you find the headset uncomfortable or distracting, say so. A well-designed course should offer an alternative. 7.5 Implications for policy and institutions For institutions and policymakers, the main message is that XR adoption needs a plan that goes beyond buying equipment. The plan should include curriculum design time, teacher training, technical support, accessibility provisions, data protection, and evaluation. The reviews of medical education and health professions education both found that integration barriers and the lack of design models were major obstacles (Asoodar et al., 2024; Mergen et al., 2024). Funding that pays only for devices, without paying for design and evaluation, is unlikely to deliver lasting benefits. 7.6 Wellbeing, ethics, and inclusion Immersive learning raises questions of wellbeing and ethics that a curriculum must address openly. Some learners experience #cybersickness and feel dizzy or nauseous in headsets, especially during long sessions or when the virtual view moves without the learner moving. Short sessions, seated activities, and regular breaks reduce this risk. Learners should always be able to stop without penalty. Emotional effects also matter. Immersive scenarios can feel very real, which is part of their power, but a scenario involving a medical emergency, an accident, or a conflict can be distressing. Designers should warn learners about the content in advance and make sure the debrief includes space to talk about how the experience felt, not only what was learned. #Inclusive_education requires attention to learners with disabilities. A learner who uses a wheelchair, has limited hand movement, or has a visual or hearing impairment may not be able to use a standard XR application. Designing alternative controls, captions, audio descriptions, and non-immersive versions from the start is far easier than adding them later. Equity also extends to cost. If an immersive activity is assessed, every learner must have fair access to it. Finally, there are questions of data and ownership. As discussed in Section 6, XR systems can record detailed information about learners' bodies and behaviour. Institutions should protect #data_privacy and decide in advance what they will collect, why, and for how long, and should explain this clearly to learners. These are not reasons to avoid XR, but they are reasons to plan carefully. 7.7 Limitations This article has several limitations. It is a conceptual study based on an integrative review, not a systematic review with a formal search protocol, so some relevant studies may have been missed. The frameworks are proposals and have not been tested as a whole. The effect sizes in Figure 4 come from meta-analyses that used different inclusion criteria and cover different learners, so they should not be compared directly as if they came from one study. Much of the evidence comes from VR research, while research on the newest spatial computing headsets in education is still at an early stage. Finally, most studies reviewed here come from well-resourced settings, and the findings may not apply in the same way where access to technology is limited. 7.8 A research agenda Future research should address the gaps identified in Section 3. Studies should run for longer, as true #longitudinal_studies, ideally across a full unit or semester, rather than a single session. They should include delayed retention tests and transfer tasks as standard. They should test whole curriculum designs, not just individual lessons, and they should describe the design process clearly enough for others to repeat it. The six propositions in Section 4 offer specific hypotheses that could be tested in this way. Research is also needed on the newest mixed reality and spatial computing devices, on teacher development for immersive teaching, and on how immersive curricula work for learners with disabilities and in low-resource settings. Finally, there is a need for studies that follow learners from the immersive classroom into real practice, to test whether the theory-practice gap really narrows. 8. Conclusion Spatial computing and extended reality offer educators something genuinely new: the ability to let learners step inside the concepts they study and practise skills that would otherwise be out of reach. But the research is clear that this ability does not translate into learning by itself. Immersion can motivate and engage, and it can also distract and overload. The difference lies in design. This article has proposed a set of connected frameworks to guide that design. The Immersive Curriculum Design Framework links learning theory to curriculum decisions, immersive affordances, learner factors, and measured outcomes, with a loop for continuous improvement. The five-phase design cycle turns the framework into a practical process. The XR bridge, built from a pre-brief, immersive practice, and a debrief, offers a simple way to connect classroom theory with real practice. The measurement model separates immediate recall, delayed retention, transfer, and performance, so that institutions can judge what their investment is really achieving. For students, teachers, and curriculum designers, the central message is the same. Use XR where it fits, design it carefully, connect it to what comes before and after, and measure what lasts. When these conditions are met, immersive learning can become more than a memorable experience. It can become a reliable path from knowing about something to being able to do it, which is what #lifelong_learning and professional competence require. References Adams, J., Flavell, F., & Raureti, R. (2022). Mixed reality results in vocational education: A case study with HoloLens 2. Research in Learning Technology, 30. https://doi.org/10.25304/rlt.v30.2803 Asoodar, M., Janesarvatan, F., Yu, H., & de Jong, N. (2024). Theoretical foundations and implications of augmented reality, virtual reality, and mixed reality for immersive learning in health professions education. Advances in Simulation, 9(1), Article 36. https://doi.org/10.1186/s41077-024-00311-5 Chang, H.-Y., Binali, T., Liang, J.-C., Chiou, G.-L., Cheng, K.-H., Lee, S. W.-Y., & Tsai, C.-C. (2022). 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Effectiveness of immersive virtual reality using head-mounted displays on learning performance: A meta-analysis. British Journal of Educational Technology, 51(6), 1991-2005. https://doi.org/10.1111/bjet.13023 Wyss, C., & Bauerlein, K. (2024). Augmented reality in the classroom - Mentor teachers' attitudes and technology use. Virtual Worlds, 3(4), 572-585. https://doi.org/10.3390/virtualworlds3040029 #ExtendedReality #SpatialComputing #XR_in_education #VirtualReality #AugmentedReality #MixedReality #ImmersiveLearning #CurriculumDevelopment #EdTech #FutureOfLearning #learning_retention #theory_to_practice #STEM_education #digital_pedagogy #STULIB

Generative artificial intelligence tools such as ChatGPT can now write essays, summarise readings, solve routine problems and produce code in seconds. This has exposed a weakness in many university courses: a large share of assessment still rewards the retrieval and reproduction of information, which is exactly what these tools do well. This article asks how generative AI can be built into curriculum design in a structural way, rather than handled through rules and warnings alone, and how assessment can move its focus from information retrieval towards critical analysis, evaluative judgement and innovation. Using an integrative review of peer-reviewed research published mainly between 2022 and 2025, the article brings together work on constructive alignment, assessment validity, AI literacy and the cognitive effects of AI use. It proposes a four-part framework that links AI-aware learning outcomes, aligned learning activities, structurally redesigned assessment tasks, and evaluation criteria that reward reasoning and originality, all set inside a context of AI literacy, ethics and fair access. The review finds that bans and detection tools are unreliable and can be unfair, that unguided AI use can weaken learning, and that well-designed, structured use can support it. The article concludes that the most durable response is to redesign what is assessed and how, so that students show the thinking that machines cannot do for them. Practical implications for students, teachers and institutions are discussed, together with the limits of the evidence. Keywords: generative artificial intelligence, curriculum design, assessment redesign, learning outcomes, critical thinking, constructive alignment, academic integrity, higher education 1. Introduction In late 2022 the public release of #ChatGPT changed the conversation about #teaching_and_learning almost overnight. Within weeks, students and teachers around the world were testing what a #large_language_model could do with an essay question, a case study or a set of exam problems. The answer surprised many people. The tool could produce fluent, organised and often reasonable answers to the kind of questions that universities had been setting for decades. Since then, newer and more capable systems have appeared, and #generative_AI is now part of the daily study routine of a large number of students. This development did not create a new problem so much as reveal an old one. Many #assessment tasks in #higher_education ask students to locate information, summarise it and present it in a tidy form. A take-home essay that asks students to "discuss" a well-known topic, a report that repeats textbook content, or a short-answer test that checks definitions all reward the retrieval and reorganisation of knowledge. These tasks were never perfect measures of deep learning, but for a long time they worked well enough because producing a good answer still required the student to read, think and write. Generative AI has broken that link. A student can now submit a competent answer without having done much of the thinking the task was meant to require. Early reactions focused on cheating. Some institutions tried to ban AI tools, others turned to software that claims to detect machine-written text, and many added new rules to course outlines. These reactions are understandable, but research published since 2023 suggests they are not enough. Detection tools have been shown to be unreliable (Weber-Wulff et al., 2023) and biased against students who write in English as a second language (Liang et al., 2023). Rules that tell students what they may or may not do are hard to enforce when the work is done out of sight (Corbin et al., 2025a). At the same time, students themselves see real value in these tools for learning, even while they worry about accuracy and fairness (Chan and Hu, 2023). This article takes a different starting point. It treats generative AI not mainly as a threat to be policed but as a design condition that every curriculum now has to take into account. The central question is how AI tools can be structurally integrated into #learning_outcomes, teaching activities and assessment, so that the focus of academic work moves away from information retrieval and towards #critical_thinking, analysis, judgement and #innovation. The word "structural" matters here. It means changing the design of tasks and courses, not just adding a paragraph of rules to the syllabus. 1.1 Aim and research questions The aim of the article is to give students, teachers and #curriculum_designers a clear, evidence-based account of how generative AI is reshaping curriculum and assessment, and what a sound response looks like. Three questions guide the discussion. First, what does recent research tell us about how generative AI affects learning and assessment in higher education? Second, how can AI be built into learning outcomes and course structure in a way that keeps teaching, learning and assessment aligned? Third, what kinds of assessment can shift the emphasis from retrieving information to analysing, evaluating and creating? 1.2 Why this matters for students Although much of the debate is aimed at teachers and administrators, students have the most at stake. A degree is supposed to certify that a graduate can do certain things. If assessment no longer measures those things, the value of the qualification falls for everyone, including the honest student who did the work. There is also a more personal risk. Several recent studies suggest that heavy, unreflective reliance on AI can reduce the mental effort students invest in learning and may weaken their independent reasoning over time (Fan et al., 2025; Gerlich, 2025). Students therefore have a direct interest in courses that teach them to use these tools well, and in assessments that reward the abilities that will still matter when AI is everywhere. 1.3 Contribution and structure The article makes three contributions. It brings together a scattered and fast-growing body of research into a single readable account. It proposes a simple conceptual framework for the structural integration of AI into curriculum design, grounded in the established principle of #constructive_alignment. And it offers concrete examples of #assessment_designs that move from recall to reasoning. Section 2 explains the review approach. Section 3 reviews the literature. Section 4 sets out the conceptual framework. Section 5 discusses the main themes in depth, and Section 6 offers practical implications. Section 7 concludes and notes the limits of the study. 2. Approach and Method This is a conceptual article based on an integrative literature review. An integrative review brings together different types of research, including empirical studies, reviews and theoretical papers, in order to build a new understanding of a topic. It is well suited to a field like this one, where the technology is changing quickly and the evidence comes from many disciplines and research designs. Sources were selected from peer-reviewed journals in education, educational technology, assessment and related fields, with a focus on work published between 2022 and 2025. The selection gave priority to three kinds of work: studies that directly examine generative AI in assessment or curriculum; empirical studies, including experiments, that measure the effects of AI use on learning; and influential conceptual papers on assessment validity, authenticity and alignment. A small number of slightly older but widely used works were included where they provide core theory, such as the principle of constructive alignment. The review does not claim to be a systematic review with a fixed search protocol and a count of included studies. Its purpose is interpretive. It aims to identify the main lines of argument and evidence and to organise them into a framework that students and teachers can use. Where the evidence is limited or mixed, this is stated openly. 3. Literature Review 3.1 What generative AI is and what it can do in education Generative AI refers to systems that produce new content, such as text, images, audio or code, in response to a prompt. The tools most relevant to education are large language models, which are trained on very large collections of text and learn to predict likely sequences of words. The result is a system that can answer questions, explain ideas, draft and edit writing, translate, summarise, and generate examples and practice questions. Kasneci et al. (2023) offered one of the earliest broad assessments of these tools for education. They described clear opportunities, such as personalised explanations, support for writing and help for learners with different needs. They also warned about risks, including factual errors, bias, over-reliance and threats to academic honesty. Importantly, they argued that both teachers and students need new competencies to use these tools well, and that education systems should not simply wait to see what happens. A wider, multi-author analysis led by Dwivedi et al. (2023) reached a similar conclusion from many disciplines. Contributors agreed that tools like ChatGPT could raise productivity and support learning, but they disagreed about the scale of the threat to traditional assessment, and many called for urgent rethinking of how knowledge and skill are certified. Before ChatGPT, research on #AI_in_education had already grown steadily. In a systematic review of studies in higher education, Crompton and Burke (2023) found that AI had been used mainly for assessment and evaluation, for predicting student performance, as an assistant to learners and teachers, in #intelligent_tutoring, and for managing student learning. Holmes and Tuomi (2022) similarly mapped the field into tools aimed at students, at teachers and at institutions, and stressed that ethical and social questions had received too little attention. What changed with generative AI was not the presence of AI in education but its availability, its fluency and its reach. Students no longer need a special platform. A general-purpose chatbot on a phone can do much of the work. 3.2 Early reactions: integrity, panic and policy The first wave of research after late 2022 was dominated by concern about #academic_integrity. Cotton et al. (2024) described how AI chatbots made it easy for students to submit work they had not written, and they discussed strategies such as clear policies, education about integrity and changes to assessment design. Rudolph et al. (2023) reviewed the early debate and argued that, rather than relying on detection, teachers should rethink their assessments, for example by making them more personal, more applied and more process-based. Universities moved at different speeds. Moorhouse et al. (2023) examined the published guidance of the world's top-ranked universities and found that many had not yet produced public guidelines on AI and assessment at the time of the study. Among those that had, the guidance tended to focus on academic integrity, on advice for designing assessment, and on communicating expectations to students. Chan (2023) surveyed students and teachers in Hong Kong and used the findings to build an AI policy framework for universities with three dimensions: a pedagogical dimension concerned with teaching and assessment, a governance dimension concerned with privacy, security and accountability, and an operational dimension concerned with infrastructure and training. Bearman et al. (2023) looked more critically at how AI is talked about in higher education. Their review of the literature found that AI was often presented either as an unstoppable force that demands change or as a threat to the authority and control of teachers and institutions. They argued that these discourses can hide important questions about power, responsibility and what education is for. This critique is useful because it reminds us that the response to AI is a set of choices, not an automatic process. 3.3 The limits of detection and prohibition One response that has been studied closely is the use of AI detection software. The evidence here is discouraging. Weber-Wulff et al. (2023) tested a range of detection tools and found that none of them was fully accurate or reliable, and that simple changes to AI-generated text, such as manual editing or paraphrasing, made detection even harder. Liang et al. (2023) found that popular detectors wrongly labelled a large share of essays written by non-native English speakers as machine-generated, while performing much better on texts by native speakers. This means that relying on detection may not only fail to catch misuse but may also treat some groups of students unfairly. Prohibition faces similar problems. A ban on AI use in an unsupervised task cannot be checked in practice, and it may push use into the shadows rather than stopping it. Corbin et al. (2025a) made this point sharply. They distinguished between "discursive" changes, which tell students what they may do, and "structural" changes, which alter the design of the task itself. They argued that discursive approaches, such as colour-coded systems that label tasks as allowing no AI, some AI or full AI, can give an illusion of control without changing what students can actually do. Only structural changes, they argued, can make an assessment valid in a world where AI is freely available. This distinction is central to the present article. 3.4 From cheating to validity A related shift in the literature moves the focus from cheating to validity. Dawson et al. (2024) argued that the main question about any assessment should be whether it allows teachers to make accurate judgements about what students can do. Cheating matters because it damages validity, but so do many other things, such as poorly designed tasks, unclear criteria, or tasks that measure the wrong skill. Seen this way, the challenge of AI is not only that some students may misuse it. It is that many existing tasks no longer provide good evidence of the learning they are supposed to measure, whether or not anyone cheats. This argument connects with earlier work on #authentic_assessment. Ajjawi et al. (2024) suggested that instead of treating "authentic assessment" as a fixed category of task, educators should think about authenticity in assessment as something with several dimensions, including how a task relates to real practice, to the student's own development and to the wider society. In the context of AI, this matters because real #professional_practice increasingly includes AI tools. An assessment that pretends these tools do not exist may become less authentic, not more. Corbin et al. (2025b) went further and described AI and assessment as a "#wicked_problem", meaning a problem with no single correct solution, where every response involves trade-offs and creates new difficulties. For example, moving to supervised exams may protect integrity but reduce authenticity and increase stress, while allowing full AI use may support authenticity but make it harder to see what the student contributed. Recognising this complexity helps explain why simple solutions keep failing. 3.5 Evidence on how AI affects learning A growing body of empirical work asks a simple question: does using generative AI help students learn, or does it get in the way? The answer depends heavily on how the tool is used. Some studies show clear benefits when AI is carefully designed to support learning. Kestin et al. (2025) ran a #randomised_controlled_trial in an undergraduate physics course and found that students who learned with a specially designed AI tutor, built on established teaching principles, learned more in less time than students in an active-learning class. The AI tutor in that study was not a general chatbot. It was set up to guide students step by step, to avoid simply giving answers, and to encourage effort. Other studies show the risks of unguided use. Bastani et al. (2025) studied nearly a thousand high school mathematics students in a field experiment. Students who practised with a standard chatbot did better on practice problems while they had access to it, but when access was removed they performed worse on exams than students who had never used it. A version of the tool designed as a tutor, which gave hints rather than answers, largely reduced this harm. The authors concluded that generative AI used without guardrails can harm learning even while it appears to improve performance. Fan et al. (2025) compared university students who received support from ChatGPT, from a human expert, from a writing checklist, or from no extra tool during a writing task. The ChatGPT group improved their essay scores the most, but this did not lead to greater gains in knowledge or transfer. The authors described a risk of #metacognitive_laziness, in which learners hand over the work of planning, monitoring and evaluating their own learning to the tool. Gerlich (2025), in a survey and interview study, found a negative relationship between frequent AI tool use and critical thinking scores, which appeared to be linked to #cognitive_offloading, the habit of handing mental work to a tool. Younger participants showed higher dependence on AI and lower critical thinking scores. Gerlich's study was correlational, so it cannot prove cause and effect, but its findings fit with the experimental results above. Taken together, these studies tell a consistent story. Generative AI can boost the quality of a product, such as an essay or a solved problem, without boosting the learning behind it. When the tool does the thinking, the student may learn less. When the tool is designed or used to make the student think harder, learning can improve. This is the strongest argument for structural redesign: it is not enough to permit or forbid AI. Courses must shape how it is used. 3.6 Students' perspectives Students are not passive in this story. Chan and Hu (2023) surveyed university students in Hong Kong and found generally positive attitudes towards generative AI. Students valued its help with writing, brainstorming, research and #personalised_learning support. At the same time, they were concerned about accuracy, privacy, ethical issues and the effect on their own personal development and future careers. Lim et al. (2023), writing from the perspective of management educators, described the situation as a set of paradoxes. Generative AI can be both a friend and a foe to learning, both a source of capability and a source of dependence, and both a tool for equity and a cause of new inequality. These views suggest that students are open to guided, transparent use of AI and want help to use it well. They also suggest that a purely punitive approach may damage trust between students and teachers. A curriculum that openly teaches #AI_literacy, meaning the ability to understand, use, question and evaluate AI tools, is more likely to match what students need. 3.7 Assessment research since generative AI Several reviews now map how assessment is changing. Xia et al. (2024) conducted a scoping review on how generative AI transforms assessment in higher education. They found implications at three levels: for students, who need to develop new skills such as AI literacy and #self_regulation; for teachers, who need to redesign tasks and criteria; and for institutions, which need policies and support. Chiu (2024) set out recommendations for future research on generative AI in higher education and stressed the need to study how AI changes learning outcomes themselves, not only how it affects existing tasks. Even before ChatGPT, Swiecki et al. (2022) argued that traditional assessment tends to be discrete, uniform and inauthentic, and that AI could support assessment that is more continuous, more adaptive and closer to real practice. Their argument now looks prescient. Generative AI has made the weaknesses of one-off, uniform, product-only assessment impossible to ignore. Practical frameworks have also appeared. The most widely discussed is the #AI_Assessment_Scale developed by Perkins et al. (2024), which describes five levels of AI use in an assessment, from no AI at all to full use of AI. The scale is designed to help teachers state clearly how AI may be used in each task, and to help students understand what is expected. It has been adopted or adapted in many institutions. However, as Corbin et al. (2025a) note, a scale like this is mainly discursive: it describes permitted use, but it does not by itself change what students can do. It is most useful when combined with structural changes to the task. 3.8 Rethinking teacher knowledge and pedagogy Finally, research has begun to ask what teachers need to know. Mishra et al. (2023) revisited the well-known #TPACK framework, which describes the combination of technological, pedagogical and content knowledge that good teaching with technology requires. They argued that generative AI is different from earlier technologies because it acts in some ways like a social partner, producing language and ideas rather than simply storing or transmitting them. Teachers therefore need to think carefully about how AI changes both what is worth learning and how it is learned. Bearman and Ajjawi (2023) made a related argument. Because AI systems are often "black boxes" whose workings cannot be fully understood, students need to develop #evaluative_judgement, the ability to judge the quality of work, including work produced by AI, and to act wisely under uncertainty. This capacity, they suggested, should become a central aim of higher education. 3.9 Summary of the literature and the gap The literature shows a clear movement over three years. The first reaction centred on cheating and detection. This has given way to an understanding that detection is unreliable, that rules alone cannot secure valid assessment, and that the deeper issue is whether tasks still measure what matters. Empirical studies show that AI can either support or undermine learning depending on how it is used. What is less developed is a clear, practical account of how to join these insights into a coherent curriculum design, linking learning outcomes, activities and assessment. The next section offers a framework intended to fill that gap. 4. Conceptual Framework: Structural Integration through Constructive Alignment 4.1 The starting point: constructive alignment The framework proposed here builds on the principle of constructive alignment, developed by John Biggs and set out in its most recent form by Biggs et al. (2022). The idea is simple. Teachers first decide what students should be able to do by the end of a course, expressed as #intended_learning_outcomes. They then design teaching and learning activities that give students practice in doing those things. Finally, they design assessment tasks that require students to show they can do them, and they judge performance against clear criteria. When outcomes, activities and assessment point in the same direction, students are more likely to engage in deep learning, because the easiest route to success is to learn what the course intends. Biggs and colleagues also use the #SOLO_taxonomy, which describes levels of understanding from simple to complex. At the lower levels, students can identify or list relevant information. At the middle levels, they can describe, combine and explain several pieces of information. At the higher, "relational" and "extended abstract" levels, they can analyse relationships, apply ideas to new situations, theorise and create. A similar progression appears in the familiar #Bloom_taxonomy, which moves from remembering and understanding through applying and analysing to evaluating and creating. This matters for the AI debate. Generative AI is very good at producing outputs that look like the lower and middle levels of these taxonomies: recalling, listing, describing and summarising. It can also imitate some higher-level performance, producing text that appears analytical or creative. But producing a plausible analysis is not the same as a student understanding a problem, judging the quality of an argument, or making a well-reasoned decision in a specific context. A curriculum aligned to higher-level outcomes, and assessed in ways that make that higher-level thinking visible, is therefore less exposed to AI substitution and more likely to develop the abilities students actually need. 4.2 The four components of the framework The proposed framework has four connected components, set inside a wider context layer. Figure 1 shows how they fit together. Figure 1. A framework for the structural integration of generative AI into curriculum design, adapted from the principle of constructive alignment (Biggs et al., 2022). The first component is AI-aware learning outcomes. Learning outcomes should be reviewed and, where needed, rewritten with generative AI in mind. This does not mean adding "use ChatGPT" to every course. It means asking two questions about each outcome. What should students be able to do on their own, without any tool, because this ability is foundational or because they will need it in situations where AI is not available or not trustworthy? And what should students be able to do with AI, because working well with these tools is now part of competent practice in the field? Some outcomes will fall clearly into one group, others into both. For example, a nursing student must be able to calculate a drug dose without help, while a marketing student may reasonably be expected to use AI to generate campaign ideas and then evaluate them critically. The second component is aligned learning activities. If outcomes include working critically with AI, then students need structured practice in doing so. This could include comparing AI answers with expert sources, identifying errors and bias in AI output, improving a weak AI draft and explaining the changes, or using AI as a sparring partner in an argument and then reflecting on what was learned. If outcomes include working without AI, students need practice in that as well, for example through in-class #problem_solving, discussion and writing. The key point, drawn from the empirical studies reviewed above, is that activities should keep students mentally active. AI should be used in ways that require more thinking, not less (Bastani et al., 2025; Kestin et al., 2025). The third component is structurally redesigned assessment. This is the core of the framework. Assessment tasks should be designed so that the abilities named in the learning outcomes are actually visible in the evidence students produce. Following Corbin et al. (2025a), the emphasis is on structural design: changing the shape of the task, its conditions, its stages and its outputs, rather than relying only on instructions. Section 5 discusses several forms this can take. The fourth component is evaluation and #feedback. Marking criteria must reward what the outcomes value. If the outcome is #critical_analysis, criteria should give weight to the quality of reasoning, use of evidence, recognition of limits and justification of judgements, not to fluency or length. If the outcome is innovation, criteria should reward #originality, relevance and feasibility. Feedback should help students improve their reasoning, and should be given at stages where it can still change the work. 4.3 The context layer Surrounding these four components is a context layer that includes AI literacy, ethics, integrity policy and equity of access. These conditions shape every stage. Students cannot be expected to evaluate AI output if they have not been taught how these tools work and where they fail. Institutions need clear, fair policies on #data_privacy, acknowledgement of AI use and responsibility for errors, as outlined in Chan's (2023) governance and operational dimensions. And because not all students have equal access to the most capable tools, courses that build AI into assessment must make sure that access is fair, for example by providing institutional tools. 4.4 The feedback loop Finally, the framework includes a loop from evaluation back to learning outcomes. Generative AI is changing quickly, and so are the professions that graduates will enter. A task that was well designed in 2024 may be easy to automate in 2026. Curriculum designers therefore need to review outcomes and tasks regularly, using evidence from student performance, feedback from students and staff, and developments in the technology. This loop reflects the "wicked problem" view of Corbin et al. (2025b): there is no final solution, only continuing, informed adjustment. 5. Analysis and Discussion This section develops the framework through five themes: the shift in what assessment values, the difference between rules and redesign, the use of frameworks for permitted AI use, assessment formats that make reasoning visible, and the protection of learning against cognitive offloading. 5.1 From retrieval to reasoning: what assessment should value For a long time, much assessment in higher education has rewarded students for knowing things and being able to present them clearly. This was never the whole of university learning, but it was a large part. Generative AI has made information retrieval and fluent presentation cheap. A student with a chatbot can produce a summary of the main theories of motivation, a description of the causes of the First World War or an explanation of supply and demand in a few seconds. If the purpose of a task is simply to produce such a text, the task now tells a teacher very little about the student. The response proposed in this article is to shift the centre of gravity of assessment, as shown in Figure 2. On the left are features typical of retrieval-centred assessment: recalling facts, summarising known sources, producing a polished final text, judging only the product, and relying on a single submission at the end. On the right are features of reasoning-centred assessment: evaluating and questioning AI output, comparing and justifying positions, creating new ideas and solutions, judging both process and product, and using staged, visible work that the student must be able to defend. Figure 2. The shift in assessment focus from information retrieval to critical analysis, judgement and innovation. This shift does not mean that knowledge no longer matters. On the contrary, students cannot evaluate an AI answer about pharmacology, law or engineering unless they already know a good deal about the subject. Expert judgement depends on deep knowledge. The point is that knowledge should be assessed through its use, not only through its recall. A student shows that they understand a theory when they can apply it to a new case, notice where it fails, compare it with alternatives and defend a conclusion. This view also fits with the argument of Bearman and Ajjawi (2023) that evaluative judgement should be a core aim of education. In an AI-rich world, one of the most valuable things a graduate can do is to look at a piece of work, whether written by a person or a machine, and decide whether it is accurate, relevant, well-reasoned and fit for purpose. This ability cannot be outsourced to the same tool whose output is being judged. It is, in a real sense, the human contribution. 5.2 Rules versus redesign Many universities responded to generative AI by writing new rules. Course outlines now often include statements such as "AI may be used for brainstorming but not for writing" or "students must declare any use of AI". These statements have a role. They communicate expectations, they support honest students, and they give a basis for action when misuse is clear. But as Corbin et al. (2025a) argue, they do not change what is possible. In an unsupervised take-home task, a teacher has no reliable way to know whether AI was used only for brainstorming, and detection tools cannot fill this gap (Weber-Wulff et al., 2023). Figure 3 summarises the difference between discursive and structural change. Figure 3. Discursive versus structural responses to generative AI in assessment, based on the distinction drawn by Corbin et al. (2025a). Discursive change relies on rules, declarations and student compliance. It is hard to verify and therefore offers limited assurance. Structural change alters the design of the task, for example by adding a live discussion, breaking the task into supervised and unsupervised stages, or requiring work that depends on local, personal or in-class material. Its strength is that validity is built into the task. It works even if some students ignore the rules, because the evidence the teacher collects shows what the student can actually do. This is not an argument against rules. It is an argument that rules alone are not a strategy. A sound assessment system uses both. Rules set out the ethical and practical expectations. Structural design makes sure that grades rest on trustworthy evidence. Dawson et al. (2024) put the emphasis in the right place: the aim is valid judgement of student capability, and cheating is one of several threats to that aim. There is also an argument from fairness. When assessment relies heavily on rules that cannot be checked, honest students may be disadvantaged compared with those who quietly ignore them. Detection tools add a further risk, because of their documented bias against some writers (Liang et al., 2023). Structural redesign spreads the burden more fairly, because every student faces the same task conditions. 5.3 Making permitted AI use clear: scales and their limits Even with structural redesign, students need to know what is expected of them. Scales such as the AI Assessment Scale (Perkins et al., 2024) are useful for this purpose. Figure 4 shows the five levels of the original scale. Figure 4. The five levels of the AI Assessment Scale, from no AI use to full AI use (Perkins et al., 2024). At Level 1, no AI is used, and the task is typically completed under conditions where this can be checked. At Level 2, AI may be used for idea generation and structuring, but the final work is the student's own. At Level 3, AI may be used to edit and improve the clarity of work the student has written. At Level 4, AI may complete parts of the task, but the student must critically evaluate and direct its output. At Level 5, AI may be used throughout, often as a creative partner, and the assessment focuses on what the student achieves with it. The value of such a scale is clarity. It gives teachers a shared language and helps students avoid accidental #misconduct. It also encourages teachers to think about why AI should or should not be used in a given task, which links directly to the learning outcomes component of the framework. A Level 1 task makes sense when the outcome is a foundational ability that students must have on their own. A Level 4 or Level 5 task makes sense when the outcome is skilled, critical use of AI in professional practice. The limit of the scale, as discussed above, is that it is mainly discursive. Stating that a take-home essay is a Level 2 task does not prevent a student from using AI at Level 5. The scale works best when its levels are matched to task conditions that make them meaningful. A Level 1 outcome is best assessed in a supervised setting or through a live conversation. A Level 4 outcome is best assessed through a task where the student must show and explain how they evaluated and changed the AI output. In other words, the scale should describe the design, not replace it. 5.4 Assessment designs that make reasoning visible What does structurally redesigned assessment look like in practice? The literature and growing practice suggest several families of design. None is new in itself, but generative AI gives them fresh importance. The common thread is that the student's thinking becomes visible and must be defended. The first family is #oral_assessment and live dialogue. Short viva-style conversations, presentations followed by questions, or discussions of a submitted piece of work allow teachers to test whether students understand what they have produced. A student who submits a strong report but cannot explain its central argument, justify a key choice or respond to a simple "what if" question has shown the limits of their learning. Oral components need not be long. Even ten minutes can be enough to confirm understanding. They do require time and careful design for fairness, including attention to students who are anxious or who speak the language of instruction as a second language. The second family is #process_based_assessment. Instead of a single final submission, students submit a proposal, an annotated plan, drafts with comments, and a final product, with feedback at each stage. Some stages may be completed in class. A #portfolio of this kind shows how the work developed and gives the teacher multiple points of evidence. Rudolph et al. (2023) and Cotton et al. (2024) both suggested process-focused designs as a response to AI, and Swiecki et al. (2022) argued more generally for assessment that is continuous rather than one-off. The third family is critique of AI output. Here AI is built into the task in the open. Students are given, or generate, an AI answer to a question in their field and must evaluate it. They might check its claims against sources, identify errors, missing perspectives and bias, and then write an improved version with a commentary explaining every change. This type of task directly assesses evaluative judgement (Bearman and Ajjawi, 2023) and AI literacy. It also teaches students a habit that will serve them well in professional life: never accepting AI output without checking it. The fourth family is contextual and personal tasks. Generative AI works from general patterns in its training data. It is weaker when a task depends on specific local information, on data the student has collected, on a class discussion, on a visit, or on the student's own experience and reflection. Tasks such as analysing data from a laboratory session the student attended, applying a theory to a local organisation the student has studied, or reflecting on how one's own views changed during a debate are harder to complete with AI alone. They are also often more meaningful, which links to the multi-dimensional view of authenticity proposed by Ajjawi et al. (2024). The fifth family is design and innovation tasks. Here the outcome is not a description of what is known but the creation of something new: a product concept, a policy proposal, a research design, a creative work, or a solution to a real problem set by a partner organisation. AI may be allowed or even encouraged as a tool for generating options. The assessment then focuses on the student's choices: why this option rather than another, how it was tested, what risks were identified and how the idea fits the real context. Lim et al. (2023) argued that generative AI could support reformation rather than collapse in education if it is used to push students towards higher-order work like this. The sixth family is supervised, AI-free assessment of foundations. Not every outcome should involve AI. For core knowledge and skills that students must hold on their own, #supervised in-class tests, practical examinations and written exams remain valid. The aim is not to return entirely to exam halls, which would lose much of the authenticity and flexibility that coursework provides. The aim is to use supervised assessment where it is the best way to secure a particular outcome, and to combine it with other designs elsewhere in the program. Table-like comparisons of these designs can mislead, because their value depends on the outcome being assessed. A design is good not because it resists AI but because it produces valid evidence of the learning the course intends. Some designs, like oral assessment, are strong for checking individual understanding. Others, like critique of AI output, are strong for assessing evaluative judgement. Others, like innovation tasks, are strong for assessing creative and applied thinking. A well-designed program will use a mix. 5.5 Protecting learning from cognitive offloading The empirical studies reviewed in Section 3.5 raise a concern that goes beyond assessment. Even if grades are valid, students may learn less if AI does too much of their thinking during the course. Bastani et al. (2025) showed that unguided chatbot use can improve practice performance while damaging later exam performance. Fan et al. (2025) found that AI support improved the quality of essays without improving knowledge gain, and raised the risk of metacognitive laziness. Gerlich (2025) found that heavier AI use was linked with lower critical thinking, through cognitive offloading. These findings have direct implications for curriculum design. First, learning activities should be designed so that AI increases rather than reduces mental effort. The AI tutor in the study by Kestin et al. (2025) was built to guide students through problems step by step rather than giving answers, and the tutor version in the study by Bastani et al. (2025) gave hints rather than full solutions. Teachers can apply the same principle with general tools, for example by asking students to attempt a problem first, then use AI to check their reasoning, and then explain where they went wrong. Second, curriculum should deliberately develop #metacognition, the ability to plan, monitor and evaluate one's own learning. Students should be asked to reflect on how they used AI, what they learned from it, where it misled them and what they could now do without it. Such #reflections can be part of assessment, not to police use, but to build the habit of thinking about thinking. Third, programs should protect certain spaces for unassisted thinking. Just as physical fitness requires exercise that is hard, intellectual development requires effortful practice that is not always made easier by tools. This is one reason why some outcomes should be assessed at Level 1 of the AI Assessment Scale and why in-class writing, discussion and problem solving remain important. It is important not to overstate the evidence. Much of it comes from short studies, specific subjects and particular tools, and some, like Gerlich (2025), shows association rather than cause. But the direction of the findings is consistent enough to support a careful approach. Students should be helped to use AI as a coach rather than as a substitute. 5.6 Writing AI-aware learning outcomes Because learning outcomes sit at the start of the aligned chain, changes there have the largest effect. In practice, rewriting outcomes for an AI-rich world tends to involve three moves. The first move is to raise the verb. Outcomes that use verbs such as "describe", "list", "identify" or "summarise" point to lower-level performance that AI can easily simulate. Where these abilities are truly foundational, they can remain, but they should usually be assessed in supervised conditions. Elsewhere, outcomes can be raised towards verbs such as "evaluate", "justify", "critique", "design", "adapt" and "defend". For example, an outcome in a business course that reads "describe the main theories of leadership" might become "evaluate the usefulness of competing leadership theories for a specific organisation and justify a recommendation". The second move is to name the role of AI where it is relevant. Some outcomes can directly include critical use of AI. In a law course, an outcome might read "critically evaluate AI-generated legal research for accuracy, relevance and authority, and correct its errors using primary sources". In a computing course, it might read "use AI #coding_assistants to develop a working program and explain, test and justify the design decisions in the final code". In a language course, it might read "compare machine translations with one's own translation and analyse differences in meaning, tone and cultural context". Each of these outcomes treats AI as part of the field and asks students to show judgement in using it. The third move is to make the context specific. Outcomes that refer to particular settings, data, communities or problems make room for contextual tasks that AI cannot complete alone. In a public health course, an outcome might ask students to "design a health promotion intervention for a defined local population, using data the student has gathered and analysed". The specificity is not a trick to beat AI. It reflects the reality that professional work always takes place in particular contexts. Mishra et al. (2023) remind us that these changes require teachers to think about content, pedagogy and technology together. Rewriting an outcome is not a matter of swapping verbs. It requires a judgement about what knowledge and abilities will matter to graduates in a world where AI is common, which in turn requires teachers to understand both their discipline and the capabilities and limits of the tools. 5.7 Building AI literacy into the curriculum If students are to evaluate AI output, they need to understand what these tools are and how they fail. AI literacy therefore belongs inside the curriculum, not only in optional workshops. Kasneci et al. (2023) argued early on that both teachers and learners need specific competencies to use large language models well, including an understanding of their limits and biases. AI literacy in this sense has several parts. Students should understand, at a basic level, that large language models generate text by predicting likely words rather than by checking facts, which is why they can produce confident but false statements, including invented references. They should know how to write effective prompts but, more importantly, how to check and verify outputs. They should understand issues of privacy, such as what happens to information they put into a tool, and issues of fairness and bias in the data these systems learn from. And they should understand the norms of their discipline about acknowledging AI use. Much of this is best taught within subjects rather than in isolation. A history student learns about AI literacy most effectively by checking an AI-generated account of a historical event against primary sources. A biology student learns it by testing an AI explanation of a process against a textbook and a laboratory result. When AI literacy is woven into disciplinary learning in this way, it supports both the subject outcomes and the general capability. 5.8 The changing role of the teacher Structural redesign asks a lot of teachers. Oral assessments take time. Staged tasks require more frequent feedback. Contextual tasks need to be refreshed regularly. Teachers also need to keep up with fast-changing tools. Bearman et al. (2023) observed that discussions of AI in higher education often carry an anxiety about the authority and role of teachers. It is reasonable to ask whether the approach proposed here is realistic. Three points are relevant. First, not every task must be redesigned at once. Programs can start with the assessments that carry the most weight or that are most exposed to AI substitution. Second, some redesigns save time elsewhere. A short oral check can be quicker than a lengthy integrity investigation, and well-designed critique tasks can use AI to generate the material that students then evaluate. Third, generative AI can itself support teachers, for example in drafting varied case studies, generating practice questions or suggesting feedback comments, provided the teacher checks and owns the final result. Crompton and Burke (2023) noted that assessment and evaluation were already among the most common uses of AI in higher education, and teacher-facing uses are likely to grow. Still, institutions must recognise that this is a real change in academic work. Time, training and recognition are needed. Chan's (2023) operational dimension, which covers training and support, is not an optional extra. Without it, structural redesign will remain uneven, and the burden will fall on the most committed teachers. 5.9 Equity, ethics and trust Any change to curriculum and assessment has effects on fairness. Generative AI raises several #equity concerns. Students differ in their access to paid, more capable versions of tools. They differ in their prior experience and confidence with technology. Some may avoid AI for ethical, religious or personal reasons. And as Liang et al. (2023) showed, some groups face greater risk of being wrongly accused when detection tools are used. Structural redesign can help with some of these problems. When AI use is built into tasks openly, institutions can provide equal access to approved tools and teach all students how to use them. When assessment relies less on detection, the risk of false accusation falls. When oral and contextual tasks are well designed, with clear criteria and reasonable adjustments, they can be fair to a wide range of students. #Trust is a further consideration. A climate of suspicion, in which every piece of good writing is treated as possibly machine-made, damages the relationship between students and teachers. Chan and Hu (2023) found that students want guidance and are aware of the risks of AI. A curriculum that is open about AI, that explains why some tasks are AI-free and others are AI-integrated, and that invites students into the conversation is more likely to build the trust on which good education depends. Holmes and Tuomi (2022) stressed that the ethics of AI in education is not only about individual misconduct but also about wider questions of power, data and purpose, which institutions should address openly. 6. Implications for Practice The analysis above leads to practical implications for three groups: students, teachers and curriculum designers, and institutions. Figure 5 summarises a simple redesign cycle that brings these together. Figure 5. A six-stage cycle for redesigning curriculum and assessment for generative AI. The cycle begins with an audit of existing outcomes and tasks, asking which tasks mainly reward retrieval and which already require higher-level reasoning. The second stage is to decide, for each outcome, what role AI should play, using a scale such as the AI Assessment Scale as a shared language. The third stage is to redesign the structure of the task so that the intended level of AI use is meaningful. The fourth stage is to build in evidence of process, such as drafts, reflections or a short discussion. The fifth stage is to assess reasoning and innovation through criteria that reward them. The sixth stage is to review the results with students and staff and feed what is learned back into the next audit. 6.1 For students Students can take several practical lessons from this review. Use AI as a coach, not a ghost writer. Attempt problems and drafts yourself first, then use AI to check, question and extend your work. Always verify what AI tells you, especially facts, figures and references, because these tools can produce confident errors. Keep a record of how you used AI in your work, both because many courses now ask for this and because it helps you reflect on your own learning. Most importantly, invest in the abilities that AI cannot supply for you: understanding your subject deeply, judging the quality of arguments and evidence, and creating ideas that respond to real contexts. These are the abilities that redesigned assessment will reward, and they are the abilities #employers will value. 6.2 For teachers and curriculum designers Teachers and designers should begin with outcomes, not with tools. Review each outcome and decide what students must be able to do on their own and what they should be able to do with AI. Redesign the most exposed assessments first, using structural changes such as oral components, staged submissions, AI-critique tasks, contextual tasks and innovation projects, and keep supervised assessment for foundations. Revise #marking_criteria so that they reward reasoning, evidence, judgement and originality rather than fluency and length. Build AI literacy into disciplinary teaching. And be open with students about why each task is designed the way it is. 6.3 For institutions Institutions should move beyond policies that focus mainly on prohibition and detection. Given the evidence on the unreliability and bias of detectors (Weber-Wulff et al., 2023; Liang et al., 2023), decisions about misconduct should not rest on detector scores alone. Institutional policy should support structural redesign through time, training and recognition for teachers. It should provide fair access to approved AI tools, clear guidance on data privacy and acknowledgement, and a shared language for describing permitted AI use. And it should support program-level planning, so that across a whole degree students experience a sensible mix of AI-free and AI-integrated assessment and graduate with both strong foundations and the ability to work critically with AI. 7. Conclusion Generative AI has not made learning obsolete, but it has made some forms of assessment obsolete. Tasks that mainly ask students to retrieve, summarise and present information no longer provide reliable evidence of learning, because a machine can produce the same output in seconds. This article has argued that the most durable response is not to fight the technology with bans and detectors, which research shows to be unreliable and sometimes unfair, but to redesign curriculum and assessment structurally. The review of recent literature showed a clear movement from early panic about cheating towards a focus on validity, authenticity and the quality of learning. Empirical studies show that generative AI can either support or weaken learning, depending on whether it is used to increase or replace student thinking. Building on the principle of constructive alignment, the article proposed a framework that links AI-aware learning outcomes, aligned learning activities, structurally redesigned assessment and evaluation criteria that reward reasoning and innovation, all inside a context of AI literacy, ethics, integrity and fair access, and subject to continual review. The central message for students is simple. The value of a university education now lies even more clearly in the abilities that cannot be handed to a machine: deep understanding, critical analysis, sound judgement and the creation of new ideas that fit real situations. Well-designed curricula and assessments should help students build those abilities, and students should use AI in ways that strengthen rather than replace them. 7.1 Limitations and future research This article has several limitations. It is an integrative review rather than a systematic review, so the selection of sources reflects judgement and may have missed relevant work. The research field is young and changing quickly, and many studies are short-term, focused on particular subjects, tools or countries, or based on self-reported data. The cognitive effects of AI use, in particular, need longer-term studies across different disciplines and settings. The framework proposed here is conceptual and has not been tested empirically. 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International Journal of Educational Technology in Higher Education, 21, Article 40. https://doi.org/10.1186/s41239-024-00468-z #GenerativeAI #AI_in_higher_education #curriculum_redesign #assessment_reform #future_of_assessment #critical_thinking_skills #AI_literacy_for_students #learning_outcomes_design #academic_integrity_and_AI #ChatGPT_in_education #evaluative_judgement #rethinking_assessment #constructive_alignment #STULIB #student_research