This substack represents the final section from Stage 1 of the 8-Stage Learning Design Framework (Student Personas) in an upcoming monograph. Comments welcome.

The delegation temptation

Artificial intelligence tools are now embedded in university workflows in ways that were barely conceivable a decade ago. Faculty use them to draft course materials, generate assessment rubrics, and locate relevant literature. Learning designers use them to accelerate the creation of first drafts of module specifications. Institutional leaders commission reports on their potential to transform everything from admissions to curriculum review. Against this backdrop, it would be surprising if Stage 1 of the 8-SLDF (Student Personas) were not subject to the same temptation: the temptation to ask an AI tool to generate the student personas and bypass the slow, contested, facilitated conversation that the previous Sbstacks have described.

That temptation deserves direct examination, because it is not irrational. AI tools are sophisticated. They generate fluent, plausible, structurally coherent text. Prompt a large language model (LLM: a type of AI that generates text by identifying patterns across enormous volumes of written material) with a description of a course and its institutional context. It will produce persona sketches within seconds: sketches that, on the surface, look like the output of a carefully conducted Stage 1 process. The friction and time that the facilitated approach demands are real costs. The question is whether the AI-generated shortcut produces an equivalent result.

It does not. And the reasons go beyond the current limitations of AI systems to something more fundamental about what the Stage 1 process is actually for.

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What AI tools do at Stage 1

To understand the structural problem, it helps to be clear about what a large language model actually does. An LLM generates text by predicting the most likely continuation of a sequence based on patterns in the data it was trained on. It does not consult a database of current facts, reason from disciplinary knowledge, or draw on any direct understanding of your institution or your students. It produces text that is statistically consistent with the written material it has encountered in training.

When asked to produce student personas for a given course, an LLM will produce personas that reflect the patterns most strongly represented in its training data: the demographic characteristics, learning dispositions, and prior knowledge profiles most commonly associated with that kind of course. The result will be plausible in the abstract. It will not be accurate for your course, your institution, or your cohort.

This is not merely the problem of hallucination, the well-documented tendency of LLMs to generate confident but factually incorrect statements (Rawte et al., 2023). That risk is real, but it is the lesser concern. The deeper problem is structural: AI-generated personas are, by definition, generalisations. They are constructed from patterns in historical data about students in broad terms, not from knowledge of the specific students who will enrol in your course next year. They will reflect what the training data suggests is typical. They will systematically underrepresent the students who diverge from those typical patterns: the mature students returning to education after years in employment; students from educational traditions where the relationship to academic authority differs markedly from Global North norms (the dominant training context); students whose prior learning and life circumstances do not appear in the published literature the model was trained on.

The research evidence on this point is consistent. A systematic review of 63 studies examining the use of LLM-generated personas in research contexts found poor ecological validity, meaning the generated personas did not accurately reflect the range and diversity of real-world populations. Models used without specific contextual input produced outputs that were systematically skewed towards the majority patterns in the training data, obscuring precisely the variation the researchers needed to account for (Batzner et al. 2025). What is true of research persona generation is at least as true, and probably more so, of educational persona generation, where the required level of contextual specificity is considerably greater.

The value of the student profiling process lies in the quality of the conversation it generates and the situated judgements it requires. Those judgements cannot be made in the abstract.

Three failure modes

Three failure modes at Stage 1 are particularly consequential. Understanding them helps clarify not just why AI-generated personas are inadequate, but where in the process the inadequacy arises.

The first is the reproduction of institutional assumptions. An LLM asked to generate personas for a course at a named institution will draw on whatever information about that institution appears in its training data. This will typically include marketing materials, published league-table reports, research publications, and institutional strategy documents. These sources are not neutral. They systematically represent the students the institution was designed to serve and aspires to attract. They do not represent the students who arrived through non-standard entry routes, who required significant additional support, or whose experience of the institution differed substantially from its public presentation. AI-generated personas built from this material reproduce those absences, designing in effect for the imagined student rather than the real one.

The second failure mode is the flattening of cultural and epistemological diversity. Epistemological beliefs are the assumptions students hold about the nature of knowledge itself: whether knowledge is fixed or uncertain, whether it comes from authority or from evidence and argument, whether learning is about acquiring information or about developing the capacity to think. As earlier chapters in this series establish, these beliefs are design-critical variables; they shape how students approach assessment, engage with debate, and respond to feedback, and they vary substantially across cultural and prior educational contexts. An AI tool will generate personas whose epistemological orientations reflect the central tendencies of its training data, which in higher education contexts means they will tend to mirror the assumptions of the dominant Western academic tradition. The students whose starting points diverge most significantly from those assumptions present the most demanding design challenge. They will be the least visible in the AI output.

The third failure mode is the substitution of pattern recognition for professional judgement. The Stage 1 profiling process is not, at its core, a data-gathering exercise. It is a reflective exercise for the design team. Its value lies in the conversations it provokes, the disagreements it surfaces, the assumptions it makes visible, and the shared understanding it builds. A team that has worked through the Student Inclusion Model, the discipline orientation framework, the POISEL dimensions, and the personal and social context analyses together does not merely produce persona documents. It produces a design team that understands its students in a way it did not before the process began. That understanding shapes every subsequent design decision. An AI tool can produce a set of persona documents without generating any of this. The output may look like the product of a Stage 1 process. It bypasses the process entirely. The design team that accepts AI-generated personas in place of a facilitated Stage 1 conversation has saved time and forfeited what that time would have bought.

What AI legitimately supports at Stage 1

None of the above constitutes an argument against using AI tools in course design. It is an argument against using them at the wrong moment and for the wrong purpose. There are roles that AI tools can play at Stage 1 that are genuinely useful and that do not carry the risks described above.

The most productive role is as a structured challenger: a source of provocation for a design team that has already done the substantive persona work. A team that has developed eight persona sketches through facilitated conversation can usefully ask an AI tool to engage with them. Does the tool generate student characteristics or profiles that the team had not considered? Does it identify assumptions in the team’s framing that warrant re-examination? Used in this way, AI functions as a form of peer review. Not a replacement for the substantive work, but a structured external perspective applied to work already done. That is a meaningful and appropriate role.

AI tools are also genuinely useful at Stage 1 for background research and synthesis. A team wanting to understand recent demographic shifts in the student population entering a given discipline, or to survey what is known about the prior educational experiences of students from a particular country or schooling system, can use AI tools to accelerate and organise that research. The right relationship here is between the tool’s capacity for pattern recognition across large bodies of text and the team’s own specific contextual knowledge: the tool provides a starting point; the team evaluates, challenges, and contextualises what it finds.

A third category warrants separate consideration: institutional AI tools trained on local data, including enrolment records, assessment performance, student support usage, and graduate outcomes. When these tools are well-designed, and their limitations are clearly understood, they can surface patterns in institutional data that a design team would not otherwise have access to. They carry their own risks, particularly around how historical data can reflect past biases, the privacy implications of using student-level data, and the tendency of such systems to focus on students who persist in a programme rather than those who leave early. But they are at least grounded in actual institutional data rather than in generic training material. Where such tools are available, they may provide useful evidence to inform Stage 1 work, alongside — not instead of — the human-led profiling process.

The principled case

The argument for human-led persona generation at Stage 1 is not primarily a technical argument about what AI tools cannot yet do. It is a principled argument about what the process is for.

Stage 1 has two purposes, both of which depend on the process being human-led. The first is to produce accurate personas: working documents that represent the design team’s best current understanding of who its students will be and what they will bring to the course. The second, and in some respects the more important, is to produce an oriented design team. A team that has genuinely worked through the question of who its students are, argued about it, examined its own assumptions, and reached provisional consensus will make different and better design decisions throughout Stages 2 to 8 than a team that began with a set of AI-generated persona documents.

An AI tool can produce a document. It cannot produce an oriented design team.

And an oriented design team, one that has genuinely worked through the question of who its students are and what the course needs to do for them, is the most important output of Stage 1.

I have argued throughout this chapter that the fundamental tension in course design is between designing for content and designing for students. Artificial intelligence, as it exists today and as it is likely to develop in the foreseeable future, offers tools that can support parts of that work. It cannot substitute for the human judgement, disciplinary knowledge, institutional understanding, and professional commitment that make it real. At Stage 1, those are not optional additions. They are the work itself.

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References

Atkinson, S. P. (2024). ‘8-Stage learning design framework: Using student profiles and personas.’ Sijen Education. ISBN: 978-0-473-70414-8

Batzner, J., Stocker, V., Tang, B., Natarajan, A., Chen, Q., Schmid, S., & Kasneci, G. (2025). Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 8(1), 343–354. https://doi.org/10.1609/aies.v8i1.36553

Rawte, V., Sheth, A., & Das, A. (2023). A survey of hallucination in large foundation models. arXiv. https://arxiv.org/abs/2309.05922

These Substack posts are draft excerpts from an upcoming monograph. Any comments or reflections are welcome.

From Individual Profile to Cohort

The step that most design teams skip

The previous three briefs in this series have moved progressively inward: from the broad mapping of student pre-conditions through the Student Inclusion Model to the discipline-orientation questions of the Four Quadrants to the epistemological beliefs of the POISE framework. Each has asked the design team to think more precisely about the individual student and what they bring. That precision is necessary. But it carries a risk. A design team that focuses exclusively on the individual student profile can lose sight of the fact that individual students will not encounter the course alone; they will encounter it as a cohort.

The two analytical lenses that complete Stage 1, personal context and social context, redirect attention from the individual to the collective. They do not introduce new categories of student characteristics; they take what the team has already established and ask a further set of questions: where is each student coming from in concrete and practical terms, and how will the range of individuals who constitute this cohort function together as a learning community? These are not peripheral concerns. The social architecture of a learning cohort is a design variable, and treating it as a background condition rather than an active design consideration is one of the more consequential oversights in course design practice.

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Personal context: five dimensions of practical circumstance

Personal context shifts the analysis from who students are, in terms of orientation and belief, to where they are coming from, in terms of recent experience. The five dimensions that structure this enquiry overlap with the circumstantial and educational dimensions of the Student Inclusion Model explored in Brief 2. My focus here is more specific: to validate and refine the nascent persona sketches developed earlier in Stage 1 by grounding them in practical evidence from students’ most recent learning experiences.

Prior study asks whether students have already engaged with the discipline at an appropriate level in a comparable context, what learning patterns they are familiar with, and whether transitional arrangements make sense. A student who has studied undergraduate sociology may arrive at a postgraduate course expecting seminar-based discussion and independent research; a student who has not will need far more explicit orientation to those modes of working.

Mode of study asks what delivery patterns students are likely to encounter and whether they are already familiar with them. A student transitioning from a predominantly face-to-face undergraduate programme to a distance-learning postgraduate course faces a mode shift with design implications that are entirely independent of disciplinary content. Failing to account for this results in courses that assume fluency in independent, self-directed learning that many students simply do not yet have.

Campus or distance experience asks about the broader physical and digital context of students’ prior learning. Students who have been educated in very small or remote schools, or who have been home-schooled, bring different orientations to peer learning and institutional structure than those who have experienced large, resource-rich campus environments. Students who have studied entirely online may have sophisticated digital literacy but limited experience of the relational dynamics of a physical seminar. Neither background is superior; both are relevant to design.

Discipline familiarity asks how much of the discipline’s language and fundamental concepts students are likely to carry into the course. This connects directly to the discipline orientation exercise in Brief 3. A course that assumes familiarity with disciplinary vocabulary that a significant portion of the cohort does not yet possess will lose those students before the substantive work begins. Making that vocabulary explicit, not as remediation but as transparent orientation, is a design act.

National or regional context familiarity asks how acculturated students are to the specific national or regional context in which the course is embedded. A course that takes for granted students’ familiarity with the UK welfare system, the history of a particular national policy debate, or the professional regulatory environment of a given jurisdiction will systematically disadvantage those who do not share that context. This is not a question of lowering standards; it is a question of not embedding unnecessary cultural assumptions into what should be discipline-level learning.

Social context: the cohort as a design variable

Once the team has a clearer picture of who the individual students are likely to be and where they are coming from, the final analytical step is to consider how those individuals will function as a collective. Wenger’s (1999) account of communities of practice establishes that learning is not only a cognitive process occurring within individuals; it is a social process occurring between them. The design team that has thought carefully about individual student profiles but not at all about how those students will work together has addressed only half the question.

Four dimensions structure the social context analysis. The first is the composition and capacity of the peer cohort: can the team anticipate sufficient diversity in skills and perspectives to make collaborative work intellectually productive? Will there be enough variation to prevent a highly homogeneous group from reinforcing a single, narrow perspective, and enough common ground to enable productive collaboration?

The second dimension is homogeneity: if the cohort is expected to be largely similar in profile, how will the course accommodate students who do not conform to that profile? High homogeneity is not inherently problematic, but it requires specific design attention: a cohort of students who are very similar in background, orientation, and expectation will need explicit prompts to interrogate their shared assumptions and engage with perspectives that challenge them.

The third dimension, positive difference, asks whether the diversity within the cohort can be actively leveraged as a learning resource rather than managed as a complication. A cohort that includes students from multiple professional backgrounds or different national and linguistic contexts carries a form of collective expertise that well-designed collaborative activities can make productive. This requires deliberate design; it does not happen by default.

The fourth dimension is relevance: how closely the learning environment aligns with real-world conditions, and what prior professional or lived experience do students bring that could inform and enrich others’ learning? A course that treats prior experience as irrelevant to the academic task forfeits one of the most significant resources available to it.

The Introduction to Social Theory: a workthrough

The following account draws on a design facilitation exercise conducted with the team responsible for redesigning a first-year undergraduate course, ‘Introduction to Social Theory’, at a British university (Atkinson, 2024). It is included here not as a formal case study but as an illustration of how the personal and social context, in conjunction with the earlier Stage 1 exercises, can reorient a design team’s fundamental assumptions about its students.

The team arrived at the workshop already in disagreement about content sequencing. A heated argument about whether Augustine or Socrates was the appropriate starting point had developed before the facilitation properly began. This was, in retrospect, a useful diagnostic: the team was designing for its own intellectual preferences rather than for its students.

“How would you like to be taught about Chinese social theories if you were starting a degree in Sociology in China? A version of that question is always relevant, regardless of the level and the discipline.”

The first exercise asked each team to examine the assumptions current first-year courses make about students’ prior knowledge. What emerged was a revealing pattern: assumptions about general knowledge (why hadn’t students heard of Aristotle or Spinoza?), assumptions about linguistic competence in academic English, and assumptions about familiarity with a broadly Western philosophical tradition that many students simply did not share. The team’s initial framing of the design challenge, how to provide a broad overview of social theories, was almost immediately revealed to be premature: before any overview was possible, the team needed to address a fundamental gap in contextual orientation.

The question that crystallised this shift was the one quoted above. One participant had offered it, but it immediately resonated with the whole group. It reframed the question from ‘what content should we cover?’ to ‘how would a student who does not share our cultural and intellectual formation experience this course?’ That reframing is precisely what the personal and social context analyses are designed to produce.

The team subsequently spent two sessions using the Student Inclusion Model and Four Quadrants to develop eight persona sketches for probable students. The process surfaced significant internal disagreement about the cohort profile, precisely the kind of disagreement that needed to be resolved before design work could proceed coherently. It also produced a consensus that the course needed to begin earlier and provide more explicit contextual framing of the Western European intellectual tradition from which social theory largely emerges, than the team had originally envisaged.

The individual and the cohort, together

Student personas, at the end of Stage 1 of the 8-SLDF, are not fixed objects. They are working documents, the best current estimate of who the students will be, where they are coming from, and how they are likely to function together. Their value lies in the design decisions they enable and in the conversations they generate within the team. A team that has worked through all five analytical lenses available in Stage 1, the Student Inclusion Model, discipline orientation, the POISEL framework, personal context, and social context, is in a substantially better position to make those decisions well than one that has worked through fewer.

The Social Theory case study illustrates a general point that applies across disciplines and contexts. The most consequential design conversations are not about content sequencing, assessment weighting, or learning activity type. They are about who the students are and what they bring. A design team that has settled those questions with rigour and honesty has a foundation on which every subsequent decision can be made well. A team that has not settled them, or that has settled them based on assumption rather than analysis, is building on ground that will shift.

The next brief considers how to keep personas useful across the life of a course, and how evaluation can be designed alongside Stage 1 so that the team’s understanding of its students remains live rather than historical.

References

Atkinson, S. P. (2024). 8-Stage learning design framework: Using student profiles and personas. Sijen Education. ISBN: 978-0-473-70414-8

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Wenger, E. (1999). Communities of practice: Learning, meaning, and identity. Cambridge University Press.

Learning by Design is currently featuring draft chapters from an upcoming monograph.

The hidden variable in course design

Course designs are full of implicit epistemological commitments. When a course requires students to construct an argument from primary sources and defend it against alternative interpretations, it assumes that knowledge is tentative, contested, and arrived at through reasoned inquiry. When a course asks students to master an established body of technique and apply it accurately to prescribed problems, it assumes that knowledge is, at least provisionally, settled and that correctness is an achievable and assessable state. When a course treats students’ own professional or lived experience as valid evidence, it assumes that knowledge has multiple legitimate sources and that epistemic authority is distributed rather than conferred. These assumptions are not mistakes; they are disciplinary commitments. The problem is that they are rarely made explicit.

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The design team that has not examined its own epistemological commitments cannot anticipate which students will find those commitments natural and which will find them disorienting. A student who enters a humanities degree expecting to encounter a body of settled, authoritative knowledge will be confused and potentially alienated by seminars in which every text is treated as a contested position in a live debate. A student who enters a social science degree expecting to engage in critical analysis and personal interpretation will be frustrated by an assessment regime that rewards only accurate reproduction of established theory. Neither mismatch is the student’s fault; both are design failures.

The scholarship on personal epistemology in the Occidental world has been establishing, since at least the work of Schommer (1990) and the comprehensive synthesis offered by Hofer and Pintrich (1997), that students hold beliefs about the nature of knowledge and knowing that are meaningfully related to how they engage with academic work. These beliefs, though not fully stable across domains, tend to cluster around a small number of recurring dimensions: beliefs about the certainty and simplicity of knowledge, the source and authority of expertise, the pace and permanence of learning, and whether intellectual capacity is fixed or developed through effort. Students who hold more sophisticated beliefs on these dimensions tend to engage more deeply and strategically. Students who are deemed to hold ‘naive’ beliefs tend to approach learning as the acquisition of settled facts from authoritative sources, and struggle when they encounter disciplines that expect something more uncertain and more demanding.

What is notable about this body of research is that it developed primarily within Western university contexts and predominantly with student populations whose educational backgrounds had already provided some degree of exposure to academic critical inquiry. Hofer and Pintrich (1997) themselves acknowledged the cultural and contextual limits of the existing research base. More recent work on epistemic dominance in higher education has demonstrated that epistemological beliefs are substantially shaped by cultural and educational context, and that the assumptions embedded in internationalised curricula frequently reflect a narrow range of epistemological traditions that not all students share (Stein, 2017). A design team working with a diverse or international cohort is working with epistemological diversity, whether it acknowledges it or not.

The POISEL framework

The POISEL framework (derived from Atkinson, 2014) offers a practical framework for examining these epistemological dimensions in the specific context of course design. Drawing on scholarship in personal epistemology, the framework identifies five key orientations that students may hold, each with implications for how they are likely to experience academic learning. The six dimensions are Pace, Ownership, Innateness, Simplicity, Exactness, and Locality.

A critical caution must be stated before the dimensions are introduced. The table below presents each dimension as a binary concept. These binary formulations are heuristic anchors for team discussion; they are not classifications of student types. In practice, students’ epistemological beliefs exist on a spectrum, and individual students routinely hold more sophisticated orientations on some dimensions while holding more naive ones on others. The POISEL framework is a reflective tool for design teams, not a diagnostic instrument for student assessment. Its value is in the conversation it generates about what the course implicitly assumes, not in its capacity to predict what any given student believes.

Table 1. The POISEL framework dimensions (after Atkinson, 2014). Western-defined ‘Naive’ belief statements are presented as heuristic anchors for team discussion; they represent orientations on a spectrum, not fixed positions.

Pace concerns students’ beliefs about how quickly knowledge is or should be acquired. Students who hold the naive orientation that learning is quick or not at all will treat persistence through difficulty as evidence of inadequacy rather than of intellectual engagement. They may disengage when material does not resolve quickly, or interpret sustained uncertainty as a sign that they are unsuited to the discipline. A course designed without considering pace beliefs will not routinely address this. A course that has considered them can explicitly name the value of sustained, effortful engagement and design activities that treat difficulty as the expected condition of learning rather than an exception.

Ownership concerns students’ beliefs about who holds the authority to determine what counts as valid knowledge. The naive orientation holds that knowledge is handed down from authority; the sophisticated orientation holds that knowledge is arrived at through reasoned argument, and that its validity is in principle open to challenge. This dimension is consistently identified in the literature as culturally specific (Atkinson, 2024; Hofer & Pintrich, 1997). In cultural and educational contexts where epistemic authority is conferred by social position, age, or institutional role, students may find it genuinely disorienting to be asked to critique published scholarly work, disagree with a lecturer’s interpretation, or adjudicate between competing claims. A course that embeds critical evaluation as a central graduate attribute without acknowledging this dimension will produce exactly the kind of assessment performance gap it was designed to avoid.

Innateness concerns students’ beliefs about whether the capacity to learn and understand is a fixed, innate attribute or something that can be acquired and developed through effort. Students who hold the naive orientation will interpret early difficulty as evidence of permanent limitation rather than a normal feature of novice learning. This dimension maps closely onto the distinction between fixed and growth mindsets in the more recent psychological literature (Dweck, 2007). Its design implication is specific: courses that do not make the developmental arc of disciplinary learning visible, explaining how a novice is expected to progress and what difficulty at each stage is normal, leave students with fixed-ability orientations without the scaffolding they need to reframe struggle as progress.

The value of the POISEL framework lies in the conversation it generates about what the course implicitly assumes, not in its ability to predict what any given student believes.

Simplicity concerns beliefs about whether knowledge is fundamentally clear-cut or inherently complex and contested. The naive orientation assumes that each question has a single correct answer and that complexity is a failure of explanation rather than a property of the subject itself. Students holding this orientation will struggle in disciplines where the appropriate response to a well-formed question is ‘it depends’, and will seek resolution where the discipline expects them to maintain productive tension. Assessment feedback that rewards a nuanced, qualified argument will feel arbitrary to these students unless the design explicitly explains and models why complexity is valued and how it is evidenced.

Exactness concerns beliefs about whether knowledge is certain and determinate or provisional and subject to revision. Alongside Ownership, this is the dimension most frequently associated with cultural specificity (Atkinson, 2024). In the dominant European academic tradition, provisional knowledge is the norm: nothing is proven, only yet to be disproved. This is so thoroughly taken for granted in many disciplines that it is never stated. For students whose educational formation has transmitted knowledge as fixed and certain, encountered through authoritative sources rather than constructed through argument, this assumption presents an epistemic challenge that no amount of generic academic-skills support is well-positioned to address. It is a design challenge that needs to be addressed.

Locality concerns students’ beliefs about whether knowledge is universal and context-independent, or fundamentally rooted in place, community, and ecological or ancestral relationships. The detached or universalist orientation holds that valid academic knowledge is abstract and generalisable, true regardless of where it is produced or applied. This is the dominant assumption of the Cartesian Western epistemological tradition, and it is so thoroughly embedded in internationalised curricula that it is rarely made explicit. The situated or ecocentric orientation holds that knowledge is inseparable from geography, ecosystems, land, and ancestral history. This is not a deficit position: in many indigenous, Pacific, and Southern epistemological traditions, knowledge disconnected from place is impoverished, not elevated.

A design team working with students from situated epistemological traditions may find that those students’ reluctance to engage with decontextualised or universalised knowledge claims is not a failure of academic capability but a principled epistemic stance. Students who hold an ecocentric orientation may ask, ‘valid for whom, and in what conditions?’ rather than simply accepting that a principle holds universally. This is an analytically sophisticated position, not a naïve one. A course that explicitly names the Locality dimension and acknowledges that place-based and relational knowledge traditions carry their own forms of rigour and authority creates conditions in which a wider range of students can engage on equitable terms. The design implication is consistent with those of the other dimensions: the task is not to convert students from one epistemological orientation to another, but to make the course’s own assumptions visible, and to assess the intellectual work students do rather than the orientation from which they start.

What POISEL means for design

The value of the POISEL framework in course design is not primarily diagnostic. Its purpose is to prompt a design team to examine the implicit epistemological commitments embedded in the course under development and to ask whether those commitments are named for students or remain unspoken assumptions that some students will navigate easily and others will not understand they are being asked to navigate at all. The question is not ‘what do our students believe?’ but ‘what does our course assume, and have we said so?’

The practical implication is direct and actionable. A course whose epistemological commitments are made explicit, through the language of learning outcomes, through the framing of assessment criteria, through the explicit valuing of uncertainty and intellectual risk-taking, and through the modelling of what good work actually looks like in this discipline, is a course that students with a wider range of epistemological orientations can engage with productively. This does not lower expectations; it names them. Naming them at the design stage is substantially easier and more effective than attempting to address them after students have already encountered and been confused by them.

The connection back to the discipline orientation exercise in Brief 3 is direct. A design team that has profiled its discipline’s epistemological commitments, its characteristic relationship to evidence, authority, complexity, and certainty, is now positioned to ask: which students are likely to hold orientations that align naturally with those commitments, and which are likely to find them disorienting? The POISEL exercise does not answer that question definitively; it makes it worth asking. In most design team conversations, simply asking it generates a more nuanced set of design decisions than would otherwise have been reached.

Note on the evolving framing of the framework.

The original foundational proposition underpinning the POISEL framework was that ‘all students are international students’: an attempt to build empathy across the design team by universalising the experience of epistemological dislocation. Ongoing engagement with scholarship on epistemic violence and pluriversal education has prompted a reframing of this proposition. The revised formulation, ‘all learners occupy situated epistemological territories’, better reflects the framework’s pluriversal intent. It positions POISEL not as an assimilation toolkit designed to transition students into Western academic norms, but as a compass for navigating dialogue between epistemological traditions on equal terms. The addition of the Locality dimension is the first structural expression of that reframing and signals the direction of the framework’s future development.

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References

Atkinson, S. P. (2014). Rethinking personal tutoring systems: The need to build on a foundation of epistemological beliefs. BPP University College. https://sijen.com/wp-content/uploads/2011/05/rethinkingpersonaltutoring.pdf

Atkinson, S. P. (2024). 8-Stage Learning Design Framework: Using student profiles and personas. Sijen Education.

Dweck, C. S. (2007). Mindset: The New Psychology of Success (Rep Upd edition). Ballantine Books.

Hofer, B. K., & Pintrich, P. R. (1997). The Development of Epistemological Theories: Beliefs about Knowledge and Knowing and Their Relation to Learning. Review of Educational Research, 67(1), 88–140.

Schommer, M. (1990). Effects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology, 82(3), 498–504. https://doi.org/10.1037/0022-0663.82.3.498

Stein, S. (2017). The Persistent Challenges of Addressing Epistemic Dominance in Higher Education: Considering the Case of Curriculum Internationalization. Comparative Education Review, 61(S1), S25–S50. https://doi.org/10.1086/690456

These ‘Learning by Design’ posts are drafts of chapters in upcoming monographs. Your feedback is welcome.

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The discipline you inhabit

There is a particular kind of confidence that comes with expertise. For faculty and learning designers who have spent years working within a discipline, its fundamental nature, characteristic questions, modes of inquiry, and the habits of mind it cultivates can feel entirely self-evident. This is understandable and, in most professional contexts, benign. In course design, it is a systematic obstacle.

Students arriving at a course do not share this depth of familiarity. Their expectations about the discipline have been shaped by everything except sustained engagement with it: subject choices at school, conversations with family members, popular representations, career guidance, and the language used in institutional recruitment materials. These expectations are often significantly at odds with the reality of the discipline as practitioners understand it. A student choosing to study law in the expectation of courtroom advocacy will encounter a predominantly analytical discipline built on careful textual reading. A student choosing psychology, expecting a therapeutic orientation, will find a field with a substantial quantitative and empirical component. A student choosing history, expecting a factual record of what occurred, will encounter a discipline built on contested interpretation of partial evidence. None of these misalignments is unusual; all of them have design implications that are rarely made explicit.

Meyer and Land (2005) describe a category of disciplinary knowledge that is simultaneously transformative, irreversible, and troublesome: concepts whose acquisition fundamentally changes how a student understands the field. Less frequently emphasised is that the gateway to this threshold knowledge is often blocked by prior misconceptions about the discipline. Before a student can encounter the troublesome knowledge that genuinely transforms their understanding, they must first relinquish their expectations about what kind of knowledge the discipline produces and how it produces it. A design team that has not examined what misconceptions students are likely to bring cannot anticipate where those gatekeeping moments will fall, or design past them.

McCune and Hounsell (2005) describe the tacit dimension of disciplinary learning as ‘ways of thinking and practising’: the characteristic approaches to inquiry, evidence, argument, and practice that define how work is done within a given field. These are rarely stated explicitly in course documentation; they are demonstrated, modelled, and acquired through extended engagement with the discipline. The design team that takes these ways of thinking for granted in its course structure creates a course that is, in effect, already at an advanced stage of disciplinary socialisation before the student has enrolled.

Four questions, asked from the student’s perspective

The discipline orientation workthrough in the Four Quadrants approach structures this examination through four deceptively simple questions. Their deceptiveness lies in the appearance of familiarity: design teams tend to assume they know the answers, and then discover, as the conversation unfolds, that they do not entirely agree (Atkinson, 2024). That disagreement is productive. It surfaces the implicit diversity of understandings within the team itself, before the team attempts to design a course for a cohort that holds an even wider range of understandings and expectations.

The first question asks: What is the fundamental nature of your discipline? This question is to be answered from the student’s perspective, not the practitioner’s. Specifically, what do students think this discipline is all about, and how accurate is that expectation? Is the discipline essentially reflective or analytical? Principally qualitative or quantitative? Socially responsive and transformative, or oriented towards the production of knowledge claims that aspire to neutrality? These are not abstract philosophical questions; they are practical design ones, because the answer shapes every subsequent decision about what students will be asked to do and how they will be assessed. A discipline whose fundamental character is contested within the design team, whether it is primarily interpretive or empirical, primarily critical or applied, is a discipline whose course contains unresolved tensions unless those tensions are surfaced and resolved before design work begins.

The second question asks: What is required to actually ‘do’ your discipline? Here, the focus shifts from what the discipline is about to what competent practitioners can do. Can the team define graduate attributes in terms of competencies rather than knowledge claims? Are there particular personal and emotional capacities, as well as cognitive ones, that characterise effective practitioners? A discipline that requires sustained tolerance of ambiguity makes different demands on students than one that rewards precision and logical resolution. A discipline in which professional credibility depends on relational intelligence and empathetic judgement is asking for something rather different from one in which credibility depends on technical accuracy and reproducibility. Recognising these distinctions early prevents the design team from building a course whose implicit demands remain invisible to the students who will be required to meet them.

Focus not on what students need to know, but on what they need to be able to do. That distinction, established at Stage 1, will shape every learning outcome written at Stage 4.

The third question is embedded within the second, but deserves its own formulation: What verbs describe what an accomplished person in your discipline can do? Atkinson (2015), in a review of 435 intended learning outcomes across 80 undergraduate modules at 20 UK institutions, found that the dominant pattern in ILO specification draws on a narrow cognitive vocabulary that systematically understates the affective and psychomotor demands of graduate-level work. The discipline orientation exercise addresses this from the start: by asking what verbs define disciplinary competence across the full range of domains, cognitive, affective, psychomotor, metacognitive, and interpersonal, the design team begins to articulate the actual demands of the discipline rather than its institutionally convenient version. This matters because a course designed around what students need to know, rather than what they need to be able to do, will produce graduates who can describe the discipline but cannot practise it.

The fourth question asks: What should a degree in your discipline actually involve? Is there a Subject Benchmark document that establishes minimum expectations? What do students typically expect to encounter in terms of learning modes: fieldwork, laboratory work, studio practice, seminar discussion, independent research, project-based work? And, critically, what would constitute a meaningful rehearsal for work in this field after graduation? This last question is the one design teams most consistently find difficult to answer honestly, because it requires confronting the gap between what a course currently provides and what sustained professional engagement in the field actually demands. That gap is worth identifying at Stage 1, when there is still time to address it, rather than at Stage 5 or 6, when the assessment and activity structures are being established.

The act of articulation

The discipline orientation exercise does not produce a document for students; it produces a shared document for the design team. Its purpose is to make explicit what the team has been assuming and to ensure that those assumptions are coherent enough to build a course on. Wingate (2007) argues that the most significant barrier to effective student learning at the point of transition into higher education is not students’ lack of generic skills but the discipline’s failure to make its own conventions and expectations transparent. A design team that has worked through the four questions above is in a substantially stronger position to build that transparency into the course design, because it has first established what needs to be made transparent.

This articulation has a further benefit that is easy to underestimate. When a design team works through these questions and discovers significant internal disagreement about what the discipline fundamentally is, that disagreement is a design risk that would otherwise have been expressed in the course itself: through inconsistent assessment criteria, contradictory feedback, or silently competing versions of what constitutes good work. Surfacing the disagreement at Stage 1 allows it to be resolved by design rather than managed by accident. It often results in a more coherent course than the team could produce when its internal differences remained implicit.

Subject Benchmark documents, where they exist, are a useful starting resource for this conversation rather than its conclusion. They establish minimum disciplinary expectations at a national level; the design team’s work is to use them as a provocation to think more specifically about what their particular course, in their particular institutional context, is seeking to cultivate in its particular cohort. The Benchmark document tells you what a degree in the field should involve; the discipline orientation exercise tells you what yours will.

Forward to the cohort

Discipline orientation is not complete as a design exercise until the team asks a further question: given what we have established about what this discipline requires students to do and how it expects them to think, who are the students about to attempt it, and what preconditions do they bring? That is the question the Student Inclusion Model began to address. The Four Quadrants approach brings these two lines of inquiry together, placing the discipline’s demands alongside the cohort’s likely profile and asking where the principal design challenges lie. The brief that follows extends this analysis by examining students’ beliefs about knowledge itself and the extent to which those beliefs align with or conflict with the disciplinary assumptions the course team has just established.

A design team that has not explicitly profiled its own discipline cannot reliably anticipate how students will experience it. Discipline orientation is not a background condition of course design; it is among its first acts. The four questions set out above do not take long to ask. The conversation they generate is never wasted.

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References

Atkinson, S. P. (2015). Graduate competencies, employability and educational taxonomies: Critique of intended learning outcomes. Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education, 10(2), 154–177. http://community.dur.ac.uk/pestlhe.learning/index.php/pestlhe/article/view/104

Atkinson, S. P. (2024). 8-Stage learning design framework: Using student profiles and personas. Sijen Education. ISBN: 978-0-473-70414-8

Land, R., Meyer, J. H. F., & Smith, J. (Eds.). (2008). Threshold concepts within the disciplines. Sense Publishers.

McCune, V., & Hounsell, D. (2005). The development of students’ ways of thinking and practising in three final-year biology courses. Higher Education, 49(3), 255–289. https://doi.org/10.1007/s10734-004-6666-0

Meyer, J. H. F., & Land, R. (2005). Threshold concepts and troublesome knowledge (2): Epistemological considerations and a conceptual framework for teaching and learning. Higher Education, 49(3), 373–388. https://doi.org/10.1007/s10734-004-6779-5

Wingate, U. (2007). A framework for transition: Supporting ‘learning to learn’ in higher education. Higher Education Quarterly, 61(3), 391–405. https://doi.org/10.1111/j.1468-2273.2007.00361.x

Design teams do not usually set out to ignore their future students. Most would describe themselves as student-centred, and many can point to institutional mission statements and quality frameworks that foreground student success. The problem is more structural than attitudinal. When a course design team gathers to begin work, the resources most immediately available to them tend to privilege one particular type of information about students: academic entry requirements. These are specific, verifiable, and institutionally sanctioned. They are also, by themselves, a profoundly limited basis for design.

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Academic entry requirements describe what a student has formally demonstrated. They say almost nothing about the circumstances in which that student is studying, the cultural assumptions about knowledge that shape how they expect learning to work, the degree of confidence with which they approach academic discourse, or the life commitments that will compete with their engagement in your course. When design decisions rest primarily on this narrow evidence base, the resulting course is shaped around an imagined student who maps neatly onto the profile of the design team itself: qualified, already acculturated into the discipline, available, and broadly confident in academic settings. For some institutions, in some disciplines, this imaginary student is close enough to the actual cohort that the mismatch goes unnoticed. For most, it is not.

Thomas and May (2010) identified this narrowing tendency as a structural problem in the design of higher education courses. Their work on inclusive learning and teaching established a four-dimensional framework for thinking about the range of preconditions that students bring to their studies. Those four dimensions, circumstantial, educational, cultural, and dispositional, collectively constitute what has since been adopted in the 8-Stage Learning Design Framework as the Student Inclusion Model (Atkinson, 2024). The model is not a screening instrument, and it produces no definitive outcome. Its function is to expand what a design team considers relevant before design decisions are made.

Four dimensions, one discipline team

The Student Inclusion Model organises student pre-conditions into four domains, each of which surfaces a different category of potential design relevance. These are not rankings; the framework does not imply that circumstantial factors matter more or less than cultural ones, or that educational background should take precedence over disposition. They are analytical lenses, and a design team that has worked through all four is in a substantially better position than one that has worked through only the most familiar.

Circumstantial characteristics include age, disability, paid or voluntary employment, caring responsibilities, geographical location, and access to technology. These are the practical features of a student’s life that do not disappear on enrolment. A working parent studying at a distance has a qualitatively different relationship to their workload from that of a full-time campus student whose principal commitment is their degree. A student without reliable internet access, in a course whose assessments depend on large digital file submissions, faces a material disadvantage that no amount of motivational scaffolding will fully address. These are not edge cases; for many contemporary higher education cohorts, they describe a significant proportion of the student group. A design team that has not explicitly asked what the circumstantial profile of its likely cohort looks like has not yet begun designing for its actual students.

Educational characteristics describe the formal and informal learning history a student brings: entry qualifications and their type, unique skills or evident abilities, the breadth or specialisation of prior study, and the educational context in which that prior learning took place. The significance of this dimension is not merely about the level of preparation. A student with narrow but deep preparation in one area of the discipline may navigate advanced material with confidence while struggling with foundational assumptions that a more generalist preparation would have established. A student arriving from a competency-based vocational qualification brings a different relationship to theoretical abstraction than one who has come from a conventional academic pathway. Neither is better prepared in any absolute sense; they are differently prepared. The design team that assumes homogeneity in educational backgrounds will unintentionally design assessments that disadvantage one group.

Cultural characteristics encompass written and spoken language abilities, personal values, cultural capital, religion and belief, and ethnicity. This is the dimension that most frequently generates anxiety in design teams, partly because cultural characteristics seem to raise questions about representation and identity that can feel politically charged. In the context of the Student Inclusion Model, however, the question is more straightforwardly a matter of practice. If a course assumes a particular cultural understanding of academic debate, of the relationship between student and teacher, of what constitutes an original argument, or of which bodies of knowledge form the legitimate intellectual inheritance of the discipline, it will systematically disadvantage students who do not share those assumptions. This is not a matter of lowering standards; it is a matter of making the standards visible. Hockings (2010) makes this point directly: inclusive curriculum design begins by making explicit what is usually left implicit, and that act of explication is as valuable for students who hold the assumed cultural background as for those who do not.

Dispositional characteristics include confidence, motivation, aspirations, expectations, attitudes, and epistemological beliefs. This fourth dimension is, in some ways, the most consequential for course design and, in others, the hardest to map, because it describes the student’s psychological orientation towards learning rather than any externally verifiable attribute. A student’s confidence in their academic ability is not fixed by their qualifications; some well-qualified students arrive with profound anxiety about their capacity to succeed at the next level. Instead, it is a student’s aspirations that

shape which elements of the course feel meaningful and which feel like arbitrary gatekeeping. Epistemological beliefs, the assumptions a student holds about how knowledge is generated and validated, will be explored in greater depth later in this series through the POISE model (Atkinson, 2014), but even at the level of the Student Inclusion Model it is worth asking what your future cohort is likely to believe about whose knowledge counts, how certainty is established, and who holds authority in your discipline. Those are design questions, not philosophical ones.

The Card Game: a structured team activity

The Student Inclusion Model is most productively worked through as a facilitated team activity, rather than as an individual checklist or desk-based exercise. The Card Game, derived from the student characteristics framework in Thomas and May (2010), operationalises the four dimensions by presenting each characteristic on a separate card. A design team works collectively through the deck, discussing which characteristics are likely to be present in the cohort, what design decisions those characteristics imply, and where the current or projected course design may be making unexamined assumptions.

The activity has a specific disciplinary logic. You are not simply asking whether your future students might be older, or international, or employed; you are asking what any of those characteristics implies for how the course is currently conceived. An institution with a predominantly mature student cohort may have already made sensible adjustments to its assessment timelines, but has it considered whether the pacing of formative feedback assumes a student who has no other significant commitments? A course designed with international students in mind may have addressed language support, but has it considered whether its case studies and worked examples draw on cultural referents that are opaque to students arriving from outside the dominant national context? The card format creates the conditions for these questions to be asked systematically rather than incidentally.

The purpose of the Card Game is to generate conversation and surface assumptions, not to produce a comprehensive audit with a definitive outcome.

The card format is intentionally open rather than prescriptive. You can create versions of the cards that reflect your own institutional or disciplinary terminology. The important thing is to be as broad as possible without becoming constrained by specific definitions (Atkinson, 2024). Design teams should anticipate that some cards will prompt immediate recognition and productive discussion; others will generate genuine uncertainty about who the students actually are. That uncertainty is itself a design signal: it identifies the points at which a team has been assuming a consensus that does not, in fact, exist.

Download the card deck here. Part of the exercise is discussing what each card represents.

Dimensions Of Student Diversity

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The activity works well distributed across more than one session. It is common for a design team to conclude a first session believing they have reached shared understanding, only to discover in a subsequent meeting that individual members have been operating from quite different assumptions about the cohort. Those divergences are worth surfacing before any substantive design work begins, not after it. When colleagues disagree about whether the likely student profile is predominantly campus-based or primarily distance, or whether the cohort will include a significant proportion of students for whom English is an additional language, they are discovering something important about the implicit assumptions that have been shaping their individual contributions to the design process. Making those assumptions explicit is the first step towards designing past them.

The case for breadth

Filippou, Acquah, and Bengs (2025), in a systematic review of inclusive policies and practices in higher education covering research published over four decades, identified curriculum design as the most consistently cited lever for inclusive practice. Crucially, they found that the primary barrier to inclusive design was not a lack of goodwill among educators but a tendency to begin design with disciplinary expertise rather than with a situated understanding of student diversity. The design team that begins with its own expertise designs, in effect, for students who already think like experts-in-the-making. The Student Inclusion Model redirects the starting point.

This redirection matters both practically and philosophically. A course whose assessments, examples, communication norms, and pacing have been designed around a narrow student profile will require adjustment when that profile turns out to be inaccurate. It will require that adjustments be made reactively, under pressure, and often at the expense of individual students who have already encountered difficulty. The case for working through the Student Inclusion Model before design begins is not only that it produces more inclusive courses, but also more efficient ones. Courses designed for the actual range of their cohort require fewer ad hoc modifications, fewer supplemental support interventions, and fewer instances of students disengaging before support can be offered.

Thomas and May (2010) describe this logic explicitly: beginning design with a broad understanding of student diversity is not an additional step for those with time to spare; it is the foundational step that makes subsequent design decisions coherent. Hockings (2010) reinforces the point, arguing that a curriculum designed with diversity at its core is more cost- and time-effective than one that attempts to provide individual accommodations after the fact. This is not a politically motivated argument; it is a design argument, grounded in evidence about what happens when courses meet cohorts they were not designed for.

Working towards personas

The Student Inclusion Model does not, by itself, produce student personas. It produces the raw material from which personas can be constructed: a shared and expansive understanding of the range of preconditions likely to characterise the cohort. The Four Quadrants approach, explored in the next brief, adds a further layer of analysis by examining discipline orientation and learning context alongside the inclusion dimensions. Together, the two approaches allow a design team to move from the question of who might be studying your course to a set of purposeful, actionable persona sketches that can serve as touchstones throughout the remaining stages of the 8-SLDF.

What the Student Inclusion Model establishes, as an essential precondition of that fuller process, is that your team has agreed to design for its actual students rather than its imaginary ones. That agreement, arrived at collectively and grounded in structured conversation rather than individual assumption, is what makes the subsequent design work coherent.

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References

Atkinson, S. P. (2014). Rethinking personal tutoring systems: The need to build on a foundation of epistemological beliefs. BPP University College.

Atkinson, S. P. (2024). 8-Stage learning design framework: Using student profiles and personas. Sijen Education. ISBN: 978-0-473-70414-8

Filippou, K., Acquah, E. O., & Bengs, A. (2025). Inclusive policies and practices in higher education: A systematic literature review. Review of Education, 13(1), e70034. https://doi.org/10.1002/rev3.70034

Hockings, C. (2010). Inclusive learning and teaching in higher education: A synthesis of research. Higher Education Academy. Retrieved from https://www.advance-he.ac.uk/knowledge-hub/inclusive-learning-and-teaching-higher-education-synthesis-research

Thomas, L., & May, H. (2010). Inclusive learning and teaching in higher education. Higher Education Academy.

When designers prioritise what the student is trying to achieve and the barriers they encounter, demographic data (age, ethnicity, gender) becomes secondary or illustrative rather than predictive.

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Purpose of this brief

The purpose of this brief is to justify the time and effort that learning design teams are required to engage with the process of articulating student personas. The argument here is that this is not a purely administrative task, nor is it acceptable to take institution-wide generic personas and apply them to any number of programmes or courses. Faculty or departmental-level personas are not sufficient.

An enduring tension

Courses, whether entire programmes or individual courses, reflect our institutional specialisms and priorities, social and economic priorities, and often our own interests. We risk forgetting that future cohorts of flesh-and-blood students will take them.

The tension between content-centred (information transfer/teacher-focused) and student-centred (conceptual change/learner-focused) course design remains a foundational axis of debate in higher education. However, its character has shifted significantly over the past decade. Ten years ago, the discourse frequently framed these two approaches as a binary opposition, with global policy mandates advocating a wholesale transition toward student-centred models (Lindström et al., 2025). Contemporary scholarship has moved beyond this binary, framing the relationship not as an absolute choice, but as a contextual, integrated, and culturally dependent continuum.

Recent academic literature from 2024 to 2026 highlights several critical evolutions in this design tension.

Rejecting the False Dichotomy: “Guided Participation”

Contemporary design theorists argue that defining student-centred learning in direct contradistinction to teacher- or content-centred models rests on a false premise (Mascolo, 2009). The assumption that increasing student autonomy necessitates a diminished role for the educator’s disciplinary expertise has been replaced by frameworks that emphasise guided participation (Mascolo, 2009). Bibliometric and co-word analyses of recent higher education literature demonstrate that while content-centred clusters focus heavily on instructional procedures, curriculum implementation, and disciplinary content, student-centred clusters focus on institutional success, accessibility, and formative feedback. Rather than choosing one over the other, modern course design emphasises balanced frameworks that integrate structural content delivery with active learning environments (Treve, 2024).

Context and Cultural Contingency

A significant trend in recent scholarship is the critique of the universal applicability of Western-centric student-centred models. Large-scale hierarchical linear modelling within non-Western tertiary systems highlights that the effectiveness of a design approach is highly dependent on student readiness, psychological capital (PsyCap), and cultural context. This creates a paradox of autonomy. In settings such as Chinese higher education, purely student-centred approaches (conceptual change/student-focused) have occasionally shown negative correlations with broader student engagement when implemented without sufficient structure. Conversely, structured, teacher-led information-transfer approaches can positively predict engagement when paired with targeted opportunities for student autonomy (Hao et al., 2025).

This suggests that content-centric scaffolding is often a prerequisite for successful student-centred execution, rather than its adversary.

Digital and Post-Pandemic Re-framing

The proliferation of digital learning management systems (LMS) and the post-pandemic stabilisation of hybrid delivery have altered where “content” sits in the design matrix. In modern online and blended design, the traditional teacher-centred method of delivering content during live sessions is increasingly obsolete, as foundational content is embedded in the digital environment through multimedia elements (Leslie, 2019). This decoupling of content delivery from class time changes the nature of our students’ roles. This shifting baseline allows the design tension to move from ‘what’ is delivered to ‘how’ relationships are mediated.

Disciplinary Baggage and Pragmatism

Recent comparative studies confirm that the nature of the discipline highly mediates the tension. Disciplines heavy on cumulative, foundational knowledge (such as the hard sciences or medicine) continue to rely on structured content delivery to manage cognitive load and ensure baseline competency, before transitioning to student-centred problem-based learning (Bhardwaj et al., 2025; Treve, 2024). Content-heavy curricula are still critiqued for limiting higher-order critical thinking (Arif, 2025). However, the current consensus dictates that content density and learner autonomy must be dynamically balanced across the programme lifecycle rather than treated as an all-or-nothing design choice.

The tension is as active as it was a decade ago, but it has matured from an ideological battle into a pragmatic, multi-dimensional optimisation problem.

A rationale for student personas

Anticipating the range of learning orientations and dispositions, approaches to learning, and relationships with the discipline among your future students is challenging. Developing meaningful student personas is foundational. It ensures you and your team are designing for students, not for the content knowledge itself. The intention is not to create a single archetype or to stereotype your students. Each student brings a personal history. There is nonetheless value in identifying trends in who is likely to comprise the student cohort of your future course or programme, and in understanding their future needs and expectations.

This enables us to ensure modules and programmes are designed to meet students’ expectations, capabilities and learning intentions. Well-designed courses reduce the need for supplemental support and future adjustments. The notion is that creating an “efficiency-through-accessibility” approach, that is proactive, upfront, and inclusive, significantly minimises the operational, financial, and pedagogical costs of reactive, retrospective remediation. In traditional educational structures, curriculum designs are often rigid, requiring specialised retrofitting (remediation) when a student encounters structural barriers. Upfront design quality, exemplified by Universal Design for Learning (UDL), shifts the burden of adaptation from the student to the educational environment itself.

Three concepts are worth summarising briefly:

Universal Design for Learning (UDL)

Sewell et al. (2022) frame UDL as an essential framework for proactive, inclusive practice. They emphasise that designers must engineer educational experiences to be inclusive for all potential learners from the outset. By embedding multiple means of engagement, representation, and action/expression into the foundational design, the need for subsequent specialised interventions for marginalised or struggling students is reduced.

Retention Scholarship & Institutional Departure

Tinto’s seminal work, ‘Leaving College’ (Tinto, 1993), argues that student persistence is a function of social and academic integration into the institutional climate. While Tinto did not explicitly use the modern lexicon of UDL or “upfront inclusive design,” his model strongly supports the contention: when an institution’s foundational academic architecture fails to integrate a student, “departure” (attrition) occurs, forcing costly institutional remediation or recruitment efforts.

Inclusive Design and Efficiency Implications

Thomas and May’s ‘Inclusive learning and teaching in Higher Education’ (2010) identifies that designing a curriculum with student diversity at its core benefits both staff and institutional efficiency. They argue that reactive adjustments, dealing with individual student crises, modifying assessments late in the term, and administering ad hoc remediation, create an unsustainable, fragmented workload. Upfront equity and flexibility yield systemic efficiency by neutralising these predictable friction points before delivery.

Why personas matter

These theoretical frameworks, established by Tinto (1993), Thomas and May (2010), and Sewell et al. (2022), have been validated by recent empirical literature across three main operational dimensions relevant to design team leaders.

Workload Sustainability and Reduced Remediation

Traditional course structures rely heavily on retrofitted accommodations to individual students’ requirements. Recent empirical evaluations demonstrate that when courses are engineered from the outset using UDL principles, such as integrating multimedia formats, structured digital accessibility, and predictable navigation structures, the volume of individual student accommodation requests, late-term assessment modifications, and reactive troubleshooting drops substantially (Afonso et al., 2025; Moriña et al., 2025; Sanderson et al., 2022).

Academic Integration and Student Retention

The conceptual bridge between upfront inclusive design and retention scholarship has been demonstrated by mixed-methods evaluations confirming that proactive course architecture directly drives student persistence. Implementing UDL-compliant designs directly counteracts the primary drivers of institutional departure by increasing student interest, academic self-efficacy, and continuous engagement (Chick et al., 2025). Comprehensive reviews confirm that structured, proactive course design leads to a notable decrease in student dropout rates across diverse student cohorts (Moriña et al., 2025). This directly addresses the heterogeneous profiles of modern learners (e.g., non-traditional and disabled students) by providing systemic flexibility without requiring separate, stigmatised remedial tracks.

Macroeconomic and Policy Justifications

Policy bodies and institutions now see inclusive design as a financial necessity rather than just a compliance requirement. With the pressure to improve completion rates, many are adopting frameworks like UDL to protect tuition revenue and use resources more effectively. Although the initial investment in instructional design and staff training is significant, it pays off by reducing the high costs associated with student attrition (Hakel & Magin, 2026; Moriña et al., 2025).

The anti-stereotyping argument

There is an understandable concern that generating student personas may lead to stereotyping future cohorts. This is a misunderstanding of the nature of personas. In curriculum and instructional design, the argument for personas is that they are an effective anti-stereotyping mechanism. While stereotypes reduce individuals to rigid, biometrically or culturally determined categories, and archetypes are based on the existing or former cohorts, professionally constructed personas serve as dynamic, goal-oriented representations of behavioural patterns, friction points, and educational objectives.

The Methodological Distinction from Stereotypes

Stereotypes rely on overgeneralised, often biased assumptions about group characteristics. Design personas are generated from direct qualitative and quantitative user data, such as interviews, informed projections, enrolment metrics, and learning analytics. Scholarship emphasises that personas do not create groups; they reflect observed behaviours within a specific design context (Rose, 2016).

The Functional Distinction from Archetypes

The distinction between archetypes and personas is functional rather than merely semantic. While archetypes reside in the realm of universal, symbolic patterns of human nature (frequently drawing upon Jungian psychology or literary theory), personas operate as highly contextual, pragmatic instruments. An archetype typically delineates a broad structural role, such as “the third-year student.” In contrast, an instructional persona is engineered to model the granular operational realities of the student experience, including technical fluencies, temporal constraints, and specific learning anxieties encountered within a particular digital environment (Lee et al., 2023).

Mitigating Bias Through Goal-Directed Design

The primary defence against the “persona-as-stereotype” critique is that intelligent and carefully curated persona generation focuses on ‘goals and behaviours’ rather than purely demographic variables. When designers prioritise what the student is trying to achieve and the barriers they encounter, demographic data (age, ethnicity, gender) becomes secondary or illustrative rather than predictive. This shifts the design focus from demographic categorisation to empathetic alignment (Goodwin, 2011).

Conclusion

It is my contention that every programme design necessitates a rigorous, fresh evaluation of future-oriented student personas. Far from reinforcing biases, scholarship suggests that personas derived from robust thematic analysis of authentic student cohorts actively dismantle instructional stereotypes by illuminating counterintuitive behavioural variations that conventional demographic metrics often mask (Rose, 2016).

The persona’s efficacy lies primarily in its operational utility. Rather than attempting to represent the entire student population, these instruments are designed to elevate specific, actionable requirements within the curriculum development process. In doing so, they provide a necessary safeguard against the generalisation of the “average student”, a reductive concept that constitutes, in itself, a significant form of design stereotyping.

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References

Afonso, A., Morgado, L., Carvalho, I. C., & Spilker, M. J. (2025). Facing Challenges in Higher Education: Enhancing Accessibility and Inclusion Through Flexible Learning Design. Education Sciences, 15(8), 1013. https://doi.org/10.3390/educsci15081013

Arif, M. (2025). A human-centered blended learning model for curriculum reconstruction in the industry 5.0 era. Education and Social Sciences Review, 6(2), 1–6. https://doi.org/10.29210/07essr610800

Bhardwaj, V., Zhang, S., Tan, Y. Q., & Pandey, V. (2025). Redefining learning: Student-centered strategies for academic and personal growth. Frontiers in Education, 10, 1518602. https://doi.org/10.3389/feduc.2025.1518602

Chick, J. C., Morello, L., & Vance, J. (2025). Universal Design for Learning as an Equity Framework: Addressing Educational Barriers and Enablers for Diverse Non-Traditional Learners. Education Sciences, 15(9), 1265. https://doi.org/10.3390/educsci15091265

Goodwin, K. (2011). Designing for the Digital Age: How to Create Human-Centered Products and Services. John Wiley & Sons.

Hakel, K., & Magin, M. (2026). The impact of professional development training on faculty’s integration of universal design for learning in daily teaching practices. International Journal of Inclusive Education, 30(2), 309–324. https://doi.org/10.1080/13603116.2024.2430527

Hao, X., He, Y., Zhang, D., & Li, X. (2025). Teacher-focused approach to foster student engagement—A hierarchical linear model based on Chinese college students’ psychological capital. BMC Psychology, 14(1), 2. https://doi.org/10.1186/s40359-025-03683-z

Lee, Y., Migut, G., & Specht, M. (2023). What Attention Regulation Behaviors Tell Us About Learners in E-Reading?: Adaptive Data-Driven Persona Development and Application Based on Unsupervised Learning. IEEE Access, 11, 118890–118906. https://doi.org/10.1109/ACCESS.2023.3327334

Leslie, H. J. (2019). Trifecta of Student Engagement: A framework for an online teaching professional development course for faculty in higher education. Journal of Research in Innovative Teaching & Learning, 13(2), 149–173. https://doi.org/10.1108/JRIT-10-2018-0024

Lindström, P. N., Bringsén, Å., Melén, M., & Ooms, A. (2025). Models for student-centered educational culture: Holistic University Systems for a Student-Centered Educational Culture (HOLUS) and Co-Created Student-Educator Engagement Model (CO-SEEM). Studies in Higher Education, 1–18. https://doi.org/10.1080/03075079.2025.2596124

Mascolo, M. (2009). Beyond Student-Centered and Teacher-Centered Pedagogy: Teaching and Learning as Guided Participation. Pedagogy and the Human Sciences, 1(1), 3–27.

Moriña, A., Carballo, R., & Doménech, A. (2025). Transforming higher education: A systematic review of faculty training in UDL and its benefits. Teaching in Higher Education, 30(7), 1722–1739. https://doi.org/10.1080/13562517.2025.2465994

Rose, T. (2016). The End of Average: How We Succeed in a World That Values Sameness. HarperOne.

Sanderson, N. C., Kessel, S., & Chen, W. (2022). What do faculty members know about universal design and digital accessibility? A qualitative study in computer science and engineering disciplines. Universal Access in the Information Society, 21(2), 351–365. https://doi.org/10.1007/s10209-022-00875-x

Sewell, A., Kennett, A., & Pugh, V. (2022). Universal Design for Learning as a theory of inclusive practice for use by educational psychologists. Educational Psychology in Practice, 38(4), 364–378. https://doi.org/10.1080/02667363.2022.2111677

Thomas, L., & May, H. (2010). Inclusive learning and teaching in Higher Education. Advance HE. https://www.advance-he.ac.uk/knowledge-hub/inclusive-learning-and-teaching-higher-education

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed). University of Chicago Press.

Treve, M. (2024). Comparative analysis of teacher-centered and student-centered learning in the context of higher education: A co-word analysis. Iberoamerican Journal of Science Measurement and Communication, 4(2), 1–12. https://doi.org/10.47909/ijsmc.117

Wednesday postings are draft chapters of an upcoming monograph aimed at senior curriculum, programme and course design teams and their leadership.

There is a particular kind of institutional amnesia that afflicts course design. A department identifies a gap, a need, or an opportunity. Enthusiasm is generated. A meeting is called. And within twenty minutes, someone is already arguing about whether the first seminar should focus on theory or practice. The foundational questions: what is this course actually for, who authorised it, and how does it fit into a programme that will outlast everyone currently in the room, have not been asked. This deliberate foundational Stage 0 of the 8-SLDF exists to ask them.

The purpose of orienting a design team before any design begins is not bureaucratic self-protection, though it will serve that purpose when the validation panel asks questions your team cannot answer. It is epistemological. Before a team can make good decisions about what students will learn, they must understand the institutional, regulatory, and political terrain in which that learning will take place. A course designed without this understanding is not designed; it is improvised. The distinction matters because improvised courses are almost impossible to defend, difficult to improve, and expensive to redesign.

The questions below are not a checklist. They are, more accurately, a pre-design audit, a structured attempt to establish what is already known, what is assumed, and what remains genuinely uncertain before Stage 1, identifying how the students will be, begins. Some will have clear answers. Others will expose gaps in institutional knowledge that the team will need to resolve before proceeding. Both outcomes are valuable. What is not valuable is discovering, six months into a design process, that a course was never formally commissioned, or that the validation timeline makes the intended delivery date impossible, or that a professional body has requirements no one on the team was aware of.

Who commissioned this design, and why? This is consistently the most politically sensitive question a design team can ask, and it is therefore the most frequently avoided. The answer shapes everything that follows. A course designed because a senior academic had a research interest is a different design challenge from one designed in response to student demand, employer pressure, or a gap in the programme architecture. A more politically demanding challenge is when a commercial provider is chasing immediate growth targets that are often unrealistic. Being presented with a ‘Business Plan’ and ‘Marketing Data’ from ‘Central Office’ does not invalidate the procedure outlined here. At the very least, it will either validate or contradict that ‘evidence’. Understanding the commissioning logic allows the team to identify whose interests are being served and whose are at risk of being overlooked. It also allows the team to determine whether the design has sufficient institutional backing to survive the validation process.

What is the programme context? Whether the design task is a single course or an entire programme, it exists within a broader structure. If a course, where does it sit within the programme sequence? Is it a foundation course introducing students to the discipline, a mid-programme course developing specialised capabilities, or a capstone course synthesising prior learning? Is it compulsory or optional? Does it have prerequisites, and if so, are those prerequisites reliably taught? Will students arrive with the background knowledge the course assumes? These questions are not administrative; they are design constraints. A course that assumes prior knowledge that is not reliably delivered will consistently underperform, and the fault will lie in the design, not in the students.

What validation and approval processes apply? Every course or programme designed within a formal tertiary institution will undergo some form of quality assurance, whether through internal programme approval, external validation, or professional accreditation. These processes have timelines, documentation requirements, and substantive expectations. In the United Kingdom, the QAA’s UK Quality Code provides the overarching framework; in Aotearoa New Zealand, the New Zealand Qualifications Authority (NZQA) sets the equivalent parameters through the New Zealand Qualifications and Credentials Framework (NZQCF). Understanding which framework applies, and what it requires at the level of design documentation, is not optional. It determines how your learning outcomes must be written, how credit values are assigned, and what external reference points the design must demonstrate awareness of. A design team that reaches the validation stage without this understanding will find itself having to rewrite work that could have been avoided.

What are the credit value, level, and notional student hours? These are the structural parameters within which all subsequent design decisions must fit. Credit value determines notional student hours; typically, in both UK and NZ frameworks, one credit equates to ten hours of student learning, inclusive of all contact time, independent study, and assessment. A 15-credit course at UK Level 5 requires 150 hours of student effort. Those hours must be accounted for. Designing a course without a clear understanding of its credit value and the level descriptors applicable to that award is designing without a brief. Every choice you make about timetabling constraints, every assessment load decision, and choices about contact hours and independent study will be made more coherently once this structural parameter has been established.

What are the timetabling and delivery constraints? Institutional timetabling is rarely neutral. A course may be expected to fit a standard pattern, for example, a one-hour lecture and a two-hour seminar each week, that was established on entirely different pedagogical assumptions. Before the design team commits to a learning and teaching strategy, they need to know what the institution can actually deliver. This is particularly important where blended, online, or work-based learning is intended: assumptions about student access to technology, campus facilities, and synchronous attendance need to be made explicit before design begins, not discovered during implementation.

Is there existing intelligence available? Good design teams do not start from nothing. Has a previous version of this course been taught? If so, what evaluation data exists, and why is a redesign necessary? Has a competitor analysis been conducted, not to replicate what comparable providers do, but to understand the landscape within which your graduates will be positioned? Has the institution’s library and learning support infrastructure been consulted? Are there employer relationships, advisory boards, or professional bodies whose perspectives should be captured before the design is fixed? All of this intelligence is easier and cheaper to gather before the design begins than to incorporate afterwards. These are preliminary questions, revisited in some detail in Stage 2 - Discipline and Professional Contextualisation.

The output of Stage 0 is not a fully formed design. It is a documented understanding of the constraints within which design will take place and a shared recognition within the team of the questions that remain open. That documentation, even if it takes the form of annotated meeting notes or a shared online document, becomes invaluable when design decisions are later questioned. The reasoning behind those decisions is already recorded. The defence of the design is partly written before the design itself begins.

One final observation, and it is not a procedural one. The design team assembled at Stage 0 will shape every subsequent decision. Who is in the room matters. A team composed exclusively of subject specialists will design differently from one that includes educational developers, library staff, students, and employer representatives. This does not mean every design team must be large or that every stakeholder has an equal voice. It means the composition of the team is itself a design decision, and one that should be made consciously rather than by institutional default. The most common failure at Stage 0 is not ignorance of the questions; most experienced academics have encountered them in one form or another. It is the assumption that someone else has already answered them.

The next sections will cover The Iterative Cycle: How the Eight Stages Connect and Workshop 0 (The Pre-Design Audit). The audit instrument in Workshop 0 consolidates the foundational questions raised above into a structured team protocol; this section does not pre-empt that instrument by providing a formatted checklist.

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Wednesday postings are draft chapters of an upcoming monograph aimed at senior curriculum, programme and course design teams and their leadership.

CH1 Section 3: The Positioning of a Learning Design Framework

Experienced learning designers may already be aware of several other learning design approaches. Some have been extensively documented; others are in-house-developed systems and processes that have become something of a “this is just how we do it here”. It is important to differentiate the levels of learning design, philosophies, principles, frameworks, models, and patterns, because if you approach the design process without the foundations of other supportive contexts, you risk ‘building on sand’.

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Figure 1 - Philosophies, Principles, Frameworks, and Patterns and Models

The web is awash with sites, often from university learning development units, that merge all of these conceptual levels and approaches under the banner of ‘learning design models’. The conflation of these levels leads to “conceptual fog.” They actually represent a hierarchy of abstraction, moving from why we teach to exactly how we build a specific teacher-student interaction.

Here is how these concepts differ in a functional, pedagogical context:

Identifying the ‘level’ at which you are establishing your course or programme design team is an important first step. For the sake of clarity, the 8-SLDF declares itself to work within a ‘Constructivist’ philosophy. In the author’s practice, it also aligns with the declared 10 Principles of Learning Design (Atkinson, 2026), although it is flexible enough for you and your institution to declare your own set of working principles.

CH 1 Section 4: A Guide to This Monograph

The chapters that follow are organised as a progressive journey through the 8-Stage Learning Design Framework, moving from institutional preparation through to strategic evaluation. Each stage builds upon the last, and whilst individual chapters can serve as standalone professional development resources, the monograph is designed to be read as a coherent whole. The logic is cumulative: decisions made in the earliest stages directly constrain and enable the choices available in later ones. That means that, in practice, if designers encounter a development challenge at a later stage, they should consider reviewing and fine-tuning earlier stages. What follows is a brief orientation to each chapter and its contribution to the overall architecture.

Chapter 0: The Discovery Phase

Preparing for institution-specific approaches requires establishing the pre-design conditions that determine whether the framework will be adopted superficially or embedded systemically. It confronts what the framework identifies as the “Subject Matter Expert (SME) trap”, the widespread but mistaken assumption that disciplinary expertise is a sufficient qualification for curriculum architecture. The chapter introduces the 10 Principles of Learning Design as the strategic spine of the entire monograph (Atkinson, 2026), and argues that before a single learning outcome is drafted, design teams must orient themselves to institutional mission, regulatory boundaries, and the root causes of any curriculum problem they are attempting to solve. Drawing on systems-thinking approaches, the chapter makes the case that preparation is not a delay to the design process; it is its foundation.

Chapter 1: The Primacy of the Learner

Establishing Student Profiles and Personal Epistemologies is arguably the most consequential decision in learning design. Understanding who the students actually are. Rather than designing for a statistical average that corresponds to no real individual, the 8-SLDF mandates the construction of outlier personas that represent the full diversity of the cohort, including students facing circumstantial barriers, those with low prior academic literacy, and those whose cultural context shapes their relationship with knowledge itself. The chapter provides a detailed analysis of the POISE framework, examining how student beliefs about the pace, ownership, innateness, simplicity, and exactness of knowledge mediate the learning process (Atkinson, 2014). This work is grounded in the foundational scholarship on personal epistemology (Hofer, 2008; Hofer & Pintrich, 1997). It also addresses the neurodiversity filter as a Stage 1 requirement, noting that as many as 1 in 7 applicants now identify as neurodivergent, designing for cognitive variation is no longer an optional enhancement but a professional standard (Coghill, 2025).

Chapter 2: Professional and Institutional Context

The second stage of the framework requires designers to look outward before they look inward. An effective curriculum cannot be built in isolation from the professional reality graduates will enter, and this chapter examines the mechanisms by which that reality is identified and translated into design requirements. It explores how graduate destination data, employer consultation, and professional body benchmarks must be integrated into the design process from the outset, ensuring that the programme is oriented toward what Atkinson (2018) describes as the student’s “Day One” in professional practice. The chapter situates this within the broader context of the evolving nature of work, including the implications of generative AI for graduate employability (Rispler et al., 2025), and examines how institutional constraints, from quality assurance frameworks to the requirements of the Framework for Higher Education Qualifications, shape the parameters within which designers must operate.

Chapter 3: Strategic Media Choices

Designing for digital equity and accessibility requires that technology selection is a core design decision, not a logistical afterthought. The chapter develops the framework’s argument that media use must never be “incidental” but must instead constitute a pedagogically justified “blend” that reflects both the professional tools graduates will encounter and the diverse circumstances of the student cohort (Atkinson, 2018). It introduces the Five Pillars of Media Selection, students, staff, professional needs, content resources, and institutional infrastructure, and examines each in depth. Critically, the chapter foregrounds the equity implications of media choice, drawing on JISC (2025) data indicating that 60% of UK students face Wi-Fi connectivity issues and 37% lack access to suitable devices, and extending this analysis to what it terms the “AI Premium Gap”: the risk that the cost of premium AI tools creates a new tier of competitive advantage for students with greater financial means (Brookings Institution, 2025). Accessibility is treated not as a compliance requirement but as a default design condition, with Universal Design for Learning (UDL) principles integrated throughout (Rose et al., 2014). This stage is not about the educational technologies deployed in the design of learning and teaching activities (Stage 6); it is about preparing a design landscape in order to design for future learners’ needs.

Chapter 4: Designing Effective Intended Learning Outcomes (ILOs)

This stage is the pivot point of the entire framework, the moment when the orientational work of the preceding stages is translated into formal technical blueprints that drive assessment and activity design. Many designers will be familiar with this as their ‘starting point’. The 8-SLDF argues that this can be easily undermined if the previous stages have been ignored. The chapter argues that ILOs are not administrative formalities but constitute what the framework describes as the “design DNA” of a course (Atkinson, 2018). An explanation of the consistent use of the term ‘Intended’ to distinguish it from ‘Unintended’ is provided briefly. It provides a detailed account of the 8-SLDF’s multi-domain approach to outcome design, requiring that outcomes be identified across five distinct domains: cognitive, affective, psychomotor, interpersonal, and metacognitive (Atkinson, 2015, 2024). This challenges the persistent over-reliance on Bloom’s cognitive domain in traditional higher education assessment and reflects the broader argument that graduates must be assessed on what they can do across the full range of professional and intellectual competencies. The chapter grounds this in the foundational scholarship of constructive alignment (Biggs & Tang, 2007) and addresses the emerging requirement to revise ILOs in response to the professional reality of human-AI collaboration (Rispler et al., 2025).

Chapter 5: Developing a Meaningful Assessment Strategy

Moving from testing to evidence requires a shift in the mindset of design teams. Assessment, properly understood, is the formal mechanism through which a student evidences their attainment of a defined learning outcome, and this definition has significant consequences for how assessment tasks are designed, sequenced, and evaluated. The chapter completes the “design triad” established across Stages 4, 5, and 6, ensuring that every ILO has a corresponding evidence-gathering task and that the tasks themselves are authentically aligned with professional reality. It examines strategies for managing assessment load through multi-domain integration, the architecture of rubrics as transparency tools for both students and assessors, and the emerging paradigm of ipsative assessment as a mechanism for measuring individual learning gain against Stage 1 baselines rather than against an external norm (Hughes, 2017). The chapter also directly addresses the sector’s most pressing assessment challenge: moving from adversarial AI detection toward what it terms “Post-Plagiarism” design frameworks that treat human-AI collaboration as a legitimate and assessable graduate skill (Rispler et al., 2025).

Chapter 6: Designing Engaging Learning Opportunities

Sequencing the experience of learning activities not as delivery mechanisms for content, but as strategic rehearsals for the evidence required in assessment, is the core of this chapter. An examination of the technical process of sequencing ILOs into session-level unassessed objectives, managing notional student hours in accordance with credit expectations, and ensuring that the learner journey is sufficiently predictable to support both student agency and institutional workload planning. Sections are devoted to the 9S Model, an reinterpretation of Gagné et al.’s (1992) nine steps of instruction, and the original ACCORD Model, both of which provide a structured but flexible template for session design. The chapter also introduces the DiAL-e (Digital Artefacts for Learner Engagement) matrix as a tool for selecting learning patterns based on pedagogical intent rather than content convenience (Burden & Atkinson, 2009), and addresses the integration of human-AI collaborative teaching as a means of reducing extraneous cognitive load whilst enhancing the quality of complex scenario-based learning (Lin & Chen, 2025).

Chapter 7: Feedback Throughout

This stage aims to cultivate the learning dialogue through feedback, treating it as a continuous conversation integral to the learning process rather than an administrative duty triggered by submission. The chapter provides a detailed account of the TACTICS framework, seven criteria by which feedback design can be evaluated and improved: Timely, Amount, Clear, Tone, Informs Teaching, Constructive, and Specific, developed in dialogue with the foundational scholarship of Nicol and Macfarlane-Dick (2006) on formative assessment and self-regulated learning. It examines collaborative and social feedback strategies, including peer feedback architecture and cohort-level responses, as mechanisms for distributing the feedback responsibility beyond the individual tutor and fostering a community of learning. The chapter closes with a considered examination of the role of AI in feedback design, arguing that automated and AI-assisted feedback tools must be carefully governed to preserve the human tone and supportive orientation that the framework identifies as essential to student empowerment.

Chapter 8: Evaluation

Closing the Quality Loop completes the design cycle by establishing evaluation as a mechanism for measuring the success of the design choices made across all preceding stages, not as a retrospective audit of student satisfaction, but as a forward-looking instrument for curriculum renewal. The chapter introduces Small Group Instructional Diagnostics (SGID) as the framework’s primary internal evaluative tool, examining the structure and sequencing of its four diagnostic questions and the critical role of the neutral facilitating colleague in generating honest, learning-focused testimony (Seldin, 1999). It develops the framework’s concept of learning gain as “distance travelled”, illustrated through the contrasting cases of the experienced practitioner and the novice learner, whose high and low grades, respectively, may invert when measured against their individual starting points. The chapter also addresses the significant methodological challenge posed by the widespread adoption of generative AI to traditional metrics of student performance and attainment (Stephenson & Armstrong, 2026).

Chapter 9: Institutional Capability and the Future of Learning Design

Serving as the synthesis chapter, this chapter steps back from the individual stages to examine the 8-SLDF as a strategic architecture for institutional transformation, aiming to move institutions towards strategic agility. It argues that the framework’s value extends well beyond the individual module or course level: when adopted systematically, it enables institutions to move from static, periodically reviewed programmes toward what can be described as a model of “perpetual prototyping”, continuous curriculum renewal driven by Stage 8 evaluation data rather than administrative review cycles. The chapter examines the professionalisation of learning design as a scholarly practice in the tradition of Boyer’s notion of Scholarship (1997) and, in particular, Shulman’s Scholarship of Teaching and Learning (2000). It also outlines the strategic implications of modularisation and micro-credentials for institutional agility, as well as the ethical and practical dimensions of designing for the “invisible programmed lens” of artificial intelligence. It closes with a call for institutions to invest in institutional and departmental learning design capability as a sustainable infrastructure for student success, rather than treating curriculum quality as the product of individual academic heroism.

Chapter 10: Conclusion

Yet to be written!

Taken together, these chapters constitute a complete architecture for learning design: from the discovery of institutional readiness, through the rigorous engineering of outcomes, activities, and assessments, to the systemic evaluation that ensures the design remains responsive to the students it serves. The framework does not offer a checklist. It offers a professional discipline, one that, when embedded at the institutional level, has the capacity to transform the quality, equity, and agility of higher education at scale.

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References

Atkinson, S. P. (2014). Rethinking personal tutoring systems: The need to build on a foundation of epistemological beliefs. BPP University College.

Atkinson, S. P. (2026). 10 principles of learning design: A strategic architecture for institutional agility and future making (1st edn). Sijen Education.

Boyer, E. L. (1997). Scholarship Reconsidered: Priorities of the Professoriate (1 edition). Jossey-Bass.

Burden, K., & Atkinson, S. P. (2009). Personalising teaching and learning with digital resources: DiAL-e Framework case studies. In J. O’Donoghue (Ed.), Technology Supported Environment for Personalised Learning: Methods and Case Studies (pp. 91–108). IGI Global.

Coghill, J. (2025). Research Guides: Neurodivergence: Infographic and Research on Neurodivergence. East Carolina University Libraries. https://libguides.ecu.edu/c.php?g=914006&p=11174589

Gagné, R. M., Briggs, L. J., & Wager, W. W. (1992). Principles of Instructional Design. Harcourt Brace Jovanovich College Publishers.

Hofer, B. K. (2008). Personal Epistemology and Culture. In M. S. Khine (Ed.), Knowing, Knowledge and Beliefs (pp. 3–22). Springer. https://doi.org/10.1007/978-1-4020-6596-5_1

Hofer, B. K., & Pintrich, P. R. (1997). The Development of Epistemological Theories: Beliefs about Knowledge and Knowing and Their Relation to Learning. Review of Educational Research, 67(1), 88–140.

Hughes, G. (2017). Ipsative Assessment and Personal Learning Gain: Exploring International Case Studies. Palgrave.

Lin, C.-P., & Chen, P.-C. (2025). Human-AI collaborative teaching for entrepreneurship learning in vocational higher education. Innovations in Education and Teaching International, 0(0), 1–17. https://doi.org/10.1080/14703297.2025.2563761

Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199. https://doi.org/10.1080/03075070600572090

Rispler, C., Mashiach-Eizenberg, M., & Yakov, G. (2025). Understanding students’ perceptions of generative AI: Implications for pedagogy and graduate employability. Journal of Teaching and Learning for Graduate Employability, 16(1), 145–170. https://doi.org/10.21153/jtlge2025vol16no1art2084

Seldin, P. (1999). Changing Practices in Evaluating Teaching: A Practical Guide to Improved Faculty Performance and Promotion/Tenure Decisions. Wiley.

Shulman, L. S. (2000). From Minsk to Pinsk: Why a scholarship of teaching & learning? The Journal of Scholarship of Teaching and Learning, 1(1), 48–53.

Stephenson, R., & Armstrong, C. (2026). Student Generative Artificial Intelligence Survey 2026 (No. 199). Higher Education Policy Institute. https://www.hepi.ac.uk/reports/student-generative-ai-survey-2026/

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Addressing the “Academic Autonomy” Tension

The integration of structured learning design often encounters resistance rooted in the perceived threat to academic autonomy. Until the last decade, higher education has treated teaching as a “private practice”, a closed-door activity where the individual academic holds absolute sovereignty over the curriculum and its delivery (Ramsden, 2003). However, the complexity of modern, multi-modal delivery requires a shift from this isolated model toward a collaborative, transparent approach.

The 8-SLDF addresses this tension by decoupling pedagogical logic from subject-matter expertise. It suggests that while the academic remains the master of the “what,” the framework provides the rigorous “how” to ensure student success. This prevents the standardisation from becoming homogenisation, instead providing a scaffold that supports diverse disciplinary identities within a consistent institutional quality framework.

The transition from the traditional lecturer to the Learning Architect represents a fundamental shift in professional identity. In the 8-SLDF model, the academic no longer works in a vacuum. Instead, they contribute or lead a multi-disciplinary team, acting as the visionary who uses the eight stages, from Student Personas (Stage 1) to Evaluation (Stage 8), as a blueprint for construction.

This “Learning Architect” model moves teaching away from performance and toward design-led practice. By adopting the 8-SLDF, the design process becomes visible and shareable. This transparency does not diminish the academic’s role; rather, it elevates it: the academic is no longer just a content provider but a co-designer of complex intellectual environments. This collaborative transparency ensures that the learning journey is robust, accessible, and aligned with modern pedagogical standards.

Rather than viewing the 8-SLDF, or indeed any design framework, as a bureaucratic constraint, it should be framed as a vehicle for the Scholarship of Teaching and Learning (SoTL) (Boyer, 1997). The framework’s emphasis on documentation and evidence-based decision-making aligns perfectly with the requirements of peer-reviewed scholarship. When an academic utilises the 8-SLDF, they are engaging in a documented, iterative scholarship process. Each stage of the framework provides a data point for reflection and improvement. By adopting this structured methodology, design becomes a scholarly output. The 8-SLDF provides the “Scholarship of Design” allowing academics to demonstrate the impact of their teaching interventions in a format that is recognisable for promotion and professional recognition. This elevates learning design from an administrative task to a rigorous, documented form of academic excellence.

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Deepening the “Neuro-discerning” Narrative

The traditional higher education model has historically treated neurodivergence as a deficit to be accommodated through individualised adjustments (Hamilton & Petty, 2023). However, the 8-SLDF facilitates a paradigm shift toward ‘Inclusion by Default’. This approach recognises the neuro-discerning learner, a student who possesses high meta-cognitive awareness of their cognitive processing needs and actively selects environments that offer flexibility, clarity, and multi-modal engagement. In the 8-SLDF, inclusion is not an elective “add-on” performed at the end of development; it is an analytical requirement of Stage 1 (Student Personas). By identifying the cohort’s diverse cognitive profiles at the outset, the Learning Architect ensures that the subsequent iterative design loop between Learning Outcomes (Stage 4), Assessment Design (Stage 5), Learning Activities (Stage 6), and Feedback (Stage 7) are both aligned and inherently accessible.

The neuro-discerning learner expects more than just a PDF transcript of a lecture. They require a learning environment designed with Universal Design for Learning (UDL) principles (Meyer et al., 2014) baked into the foundational logic. This may be considered an aspirational outcome, but it is unacceptable to move the challenge into the “too hard to even try” basket. Within the 8-SLDF, evidence of this becomes most apparent in the design of Learning Activities (Stage 6). By providing “cognitive scaffolds” (Sweller, 1988), such as clear signposting, modular content delivery, and varied sensory inputs, that reduce extraneous cognitive load. When design is neuro-discerning, it benefits the entire student body, applying the “curb-cut effect” to the digital and physical classroom. The curb-cut effect refers to accessibility campaigns that lower curb ramps to improve accessibility (Blackwell, 2016)

The strategic landscape of higher education has moved beyond the moral case for inclusion to a strategic and legal imperative. Institutions that fail to embed neuro-inclusive design into their core quality assurance processes face significant risks to student retention, satisfaction, and mental health outcomes (Hamilton & Petty, 2023; McDowall & Kiseleva, 2024). The 8-SLDF positions neuro-inclusion as a non-negotiable standard of programme and course design. A course cannot be considered high-quality if it requires retrospective modifications for neurodivergent students. Instead, the framework mandates that designs offer “equivalence by design,” allowing students to demonstrate mastery through various modes without compromising academic rigour, and that they be integrated into the curriculum architecture rather than outsourced to central disability services.

Neuro-inclusive design is a benchmark of institutional excellence. Adopting the 8-SLDF allows universities to move from a reactive, individualised model to a proactive, systemic model. This systematic approach ensures that the learning environment is “born accessible,” fulfilling the institutional duty of care while enhancing the learning experience for the increasingly diverse global student population.

The Global and Cultural Context

Learning design is never a culturally neutral act (Steele et al., 2024). The 8-SLDF acknowledges that fundamental educational constructs are deeply influenced by cultural context. While some Western models prioritise individualised ownership and measurable exactness, other global traditions value collective wisdom and emergent, fluid learning journeys. The 8-SLDF achieves global transferability by avoiding prescriptive content, focusing instead on a process-oriented methodology. The framework (Stage 1 — Student Profiles) mandates an analysis of the cultural landscape, allowing for the integration of appropriate indigenous pedagogies. By embedding cultural reciprocity into the design logic, the framework ensures that global delivery is not merely a translation of Western materials, but a genuine re-authoring that respects local ontologies.

The “digital divide” has evolved into a complex “AI divide” (Siddiqi, 2026). As institutional reliance on generative AI increases, there is a fundamental risk that students experiencing digital poverty, lacking high-speed internet access or the financial means to access premium AI tools, will be structurally disadvantaged. The 8-SLDF addresses these inequities as a strategic priority rather than an afterthought. Designers are required to ensure “low-bandwidth parity,” ensuring that core learning materials remain accessible to those in areas of digital poverty without necessitating high-cost data usage (Stage 6 — Learning Activities). There is also an aim to incorporate “AI-literacy scaffolding,” providing institutional access to tools and training to ensure that the AI divide does not become an unbridgeable gap in academic achievement (Varsik & Vosberg, 2024).

By positioning equity as a foundational challenge, the 8-SLDF encourages institutions to design for the most vulnerable learner first. This “Equity-First” approach ensures that innovation does not come at the expense of inclusion, reinforcing the framework’s role as a learning-design tool for social justice in a globalised higher education sector.

Institutional Agility & Memory

In a volatile and evolving educational landscape, the primary risk to educational quality is not the adoption of new technology, but the loss of institutional memory (Parise et al., 2006). Traditional models of teaching, which rely heavily on the tacit knowledge of individual academics, are vulnerable to knowledge vacuums when staff transition to other roles or institutions. The 8-SLDF mitigates this by serving as an institutional spine, a robust, documented framework that ensures pedagogical intent is preserved, regardless of personnel changes. The 8-SLDF transforms a course from a personal “black box” into a transparent institutional asset. By mandating rigorous documentation throughout the design process, the framework creates a clear pedagogical trail. This ensures that a new course lead can understand not just what is being taught, but the strategic why behind every assessment and activity. This structural continuity is essential for future-proofing. When the design logic is externalised through the eight stages, the institution moves from a reliance on heroic individuals to a culture of collective expertise. This spine provides the stability necessary for courses, and their constituent elements, to be scaled, adapted, or handed over without a degradation in student experience or academic standards.

A common critique of structured design is the perceived workload it imposes on academics. However, the 8-SLDF argues for a high Return on Investment (ROI) through the front-loading of effort. By investing in each stage during the initial build, institutions significantly reduce the reactive workload. The ROI is realised in three primary areas:

• Reduced Troubleshooting: Clear alignment between outcomes and activities reduces the volume of student enquiries.

• Automated Efficiencies: Well-designed resources enable self-directed learning, freeing staff to focus on high-value interventions.

• Scalability: A course built on the 8-SLDF can, when designed for the purpose, be delivered to 50 or 500 students with minimal incremental increase in design effort.

The 8-SLDF process concludes not with a finished product but with perpetual prototyping, acknowledging that in the current digital-rich environment, static curricula are quickly obsolete. Instead of periodic, high-stakes re-validations, the framework promotes an agile, iterative approach. Intelligence gathered in Stage 8 (Evaluation) ensures the curriculum remains responsive to emerging industry trends and student data. This mindset of continuous improvement ensures that the institution remains agile, capable of pivoting its delivery in response to the rapid socio-technical shifts that define the current landscape.

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References

Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871

Biggs, J., & Tang, C. (2007). Teaching for Quality Learning at University: What the Student does (3rd edn). Open University Press.

Blackwell, A. G. (2016). The Curb-Cut Effect. Stanford Social Innovation Review, 15, 2833. https://doi.org/10.48558/YVMS-CC96

Boyer, E. L. (1997). Scholarship Reconsidered: Priorities of the Professoriate (1 edition). Jossey-Bass.

Hamilton, L. G., & Petty, S. (2023). Compassionate pedagogy for neurodiversity in higher education: A conceptual analysis. Frontiers in Psychology, 14, 1093290. https://doi.org/10.3389/fpsyg.2023.1093290

Hutchings, P., & Shulman, L. S. (1999). The Scholarship of Teaching: New Elaborations, New Developments. Change: The Magazine of Higher Learning, 31(5), 10–15. https://doi.org/10.1080/00091389909604218

McDowall, A., & Kiseleva, M. (2024). A rapid review of supports for neurodivergent students in higher education. Implications for research and practice. Neurodiversity, 2, 27546330241291769. https://doi.org/10.1177/27546330241291769

Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. CAST Professional Publishing, an imprint of CAST, Inc.

Parise, S., Cross, R., & Davenport, T. H. (2006). Strategies for Preventing a Knowledge-Loss Crisis. MIT Sloan Management Review. https://sloanreview.mit.edu/article/strategies-for-preventing-a-knowledgeloss-crisis/

Ramsden, P. (2003). Learning to teach in higher education (2nd ed). RoutledgeFalmer.

Siddiqi, M. (2026). New Digital Divide in the Age of AI: Equity and Access in Higher Education. New Directions for Community Colleges, cc.70051. https://doi.org/10.1002/cc.70051

Steele, C., Gower, G., & Bogachenko, T. (2024). Creating and enacting culturally responsive assessment for First Nations students in higher education settings. Australian Journal of Education, 68(2), 84–102. https://doi.org/10.1177/00049441241258496

Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Varsik, S., & Vosberg, L. (2024). The potential impact of Artificial Intelligence on equity and inclusion in education (OECD Artificial Intelligence Papers No. 23; OECD Artificial Intelligence Papers, Vol. 23). OECD. https://doi.org/10.1787/15df715b-en

The Historical Roots of the Shift: From Transmission to Construction

The claim that higher education is undergoing a fundamental shift from content delivery to intentional learning design is strongly supported by converging streams of scholarly evidence. The intellectual foundations of this shift can be traced to a long-standing critique of the “transmissive” model of teaching, in which the instructor’s primary function is to transfer information to passive recipients. This transmissivist theory of learning describes an instructional approach in which the direct transmission of information from expert to novice dominates the learning process, with the instructor at the centre and students as passive recipients. By contrast, active learning is grounded in constructivist theory, which holds that humans learn by actively using new information and experiences to modify their existing models of how the world works (Freeman et al., 2014).

This constructivist challenge to transmission-based pedagogy gained formal traction through Biggs’ landmark framework of constructive alignment. Biggs’ (1996) concept of “constructive alignment” represents a marriage of constructivist learning theory and the instructional design literature, with constructivism serving as a framework to guide decision-making at all stages of instructional design: in deriving curriculum objectives in terms of cognitive performance, in deciding teaching and learning activities, and in assessing and reporting student performance. More recently, Biggs’ framework, along with a range of other interpretations of outcomes-based approaches, has been drawn together under the umbrella term of Outcomes-Based Education (OBE), which positions constructive alignment as a qualitative tool whose success in practice is predicated on implementation by skilled, professional educators (Loughlin et al., 2021).

The parallel framework of backward design, developed by Wiggins and McTighe, similarly crystallised this transition. Their “Understanding by Design” is a systematic approach to developing courses that begins with course goals or outcomes and works backwards to the design of learning experiences (Wiggins & McTighe, 2005). Together, these frameworks signal a decisive conceptual reorientation: rather than asking what content should be taught, educators are now asked to begin with what students should be able to do, think, and demonstrate by the end of a course or programme.

The Role of Learning Design as a Discipline

The institutionalisation of this shift is perhaps most visible in the emergence and professionalisation of learning design as a distinct discipline within higher education. The role of learning designers is most strongly associated with higher education, where it has evolved alongside shifts in pedagogy, technology, and policy. In this broader view, design is understood as the process of imagining possible futures for learning that do not yet exist and articulating intentional pathways to achieve them. This framing is explicitly compatible with critical pedagogical approaches and values educator agency (Kickbusch et al., 2025).

Critically, the core expertise of learning designers lies not in mastering individual tools, but in applying design principles to support meaningful learning and collaborating with educators to develop innovative, research-informed approaches to teaching and learning. Learning designers are fundamentally engaged in the creative and intentional design of learning experiences, irrespective of the digital environments that shape their work (Kickbusch et al., 2025). This professional evolution is reflected in the practical demands placed on higher education institutions more broadly. Technological advancements mean that institutions can offer students more blended and distance-learning opportunities, and a growing number of students are seeking more flexible formats for pursuing courses and credentials. Instructional designers have consequently been described as “the glue that holds together technology, content, and pedagogical best practice” in higher education institutions (Pollard & Kumar, 2022).

The Shift Toward Student-Centred and Active Learning Environments

A substantial and growing body of empirical evidence supports the superiority of active, student-centred learning over traditional content delivery. A meta-analysis of 104 studies across the humanities and social sciences found that student performance on assessments was higher by 0.489 standard deviations under active instruction, providing sound scientific evidence for the overall superiority of active instruction over passive instruction in higher education (Kozanitis & Nenciovici, 2023). These findings replicate earlier results from STEM fields, indicating the breadth of the effect across disciplines (Freeman et al., 2014).

Landmark research from Harvard reinforced this with a striking finding about student perceptions versus actual learning. Although students felt they learned more through traditional lectures, they actually learned more in classrooms that employed active-learning strategies (Deslauriers et al., 2019). This perception gap itself carries important implications for learning design: the shift requires not only pedagogical redesign but also deliberate communication with students about the purpose and process of active learning.

The transition from a teaching to a learning paradigm in higher education has been discussed in the literature worldwide for decades. It concerns a shift from teacher-centred learning, where teachers transmit knowledge, to student-centred learning, which favours deep learning through student discovery and knowledge construction (Barr & Tagg, 1995). Research further confirms that this shift produces durable results: student-centred teaching strategies result in learning outcomes including comprehension, long-term recall, problem-solving abilities, and positive attitudes toward the subject that persist over time compared to traditional teacher-centred strategies, which are more effective only for short-term recall (Shah et al., 2024).

The empirical findings on constructive alignment further reinforce this point. Students in more constructively aligned courses are more likely to adopt deep learning approaches and less likely to use surface learning approaches in their study of a particular course (Wang et al., 2013). This suggests that the design of the learning structure itself, not merely the quality of content, is a significant determinant of learning quality.

COVID-19 as a Catalyst: From Disruption to Intentional Redesign

While the scholarly shift from content delivery to learning design had been underway for decades, the COVID-19 pandemic dramatically accelerated its urgency and visibility. The 2019-2022 pandemic involved a rapid pedagogical shift from traditional to online class sessions, from in-person to virtual instruction, and from seminars to webinars, ushering in an era of radical technological transformation and accelerated digitalisation across the worldwide higher education system. As universities found themselves compelled to rethink and redesign their educational offerings, COVID-19’s disruptive effects created not only fertile opportunities to transform higher education institutions but also surfaced significant challenges in that process (García-Morales et al., 2021).

Crucially, scholars noted that emergency remote teaching exposed the inadequacy of simply moving lectures online without pedagogical redesign. Dumulescu et al. (2021) argued that the effectiveness of university teaching in the post-digital era is strongly connected to the ability to create cognitively transferable learning experiences and emotionally safe learning environments, while promoting an active, autonomy-focused approach to self-regulated learning. To best prepare students for the rising complexity of digitalisation, faculty members and instructional designers must implement new pedagogical approaches and practices to support student learning. The pandemic thus served as a forcing function, compelling institutions to confront the gap between content delivery and meaningful learning design (Chan et al., 2022).

The mobility paradigm literature further documents the spatial and experiential dimensions of this disruption. Chattaraj and Vijayaraghavan (2021) argued that the implications of multiple shifts in learning spaces induced by COVID-19 are highly pertinent and can significantly shape the way pedagogues and researchers engage with various modes of learning: physical, online, and hybrid, and the future of higher education as a whole as it continues to be shaped by technology-enabled learning.

Technology, Personalisation, and the Future of Learning Design

The ongoing integration of digital technologies (and increasingly, artificial intelligence) is both extending the possibilities of intentional learning design and intensifying the demand for it. Educational platforms are using sophisticated algorithms to analyse students’ learning approaches, preferences, and performance data, thereby enabling the delivery of customised content and resources, enhancing student engagement, and promoting deeper understanding of the subject matter (Evangeline, 2024). This personalised approach is only possible when learning is deliberately designed around outcomes, not content coverage. As technology continues to evolve, EdTech platforms are using advanced data analytics and AI to tailor learning pathways that address specific skill gaps, ensuring students can navigate their educational journey at their own pace. These trends towards increased personalisation and customisation within platforms warrant careful attention from educational leadership and design teams.

The role of generative AI represents the most recent and perhaps most consequential dimension of this shift. O’Dea (2024) identified both the opportunities and challenges that generative AI presents for higher education learning and teaching, noting that it reshapes how students engage with knowledge and how educators design learning. Rather than displacing learning design, AI appears to amplify its importance: the design of the human learning experience must become more intentional precisely because the content layer can increasingly be automated or augmented. This volume will explore the impact of AI in learning design in its penultimate chapter.

Navigating the Shift: The Role of Teachers as Learning Architects

Underpinning the shift to intentional learning design is a reconceptualisation of the educator’s role. Afriani et al. (2025) described this evolution as moving toward the role of a learning architect, someone who curates, orchestrates, and adapts digital resources to support diverse learners and complex learning processes. This framing distinguishes teaching through technology, which entails a more integrated, intentional, and transformative use of tools to enhance learning design, from merely teaching with technology, which supplements traditional practices with digital tools (Afriani et al., 2025).

A paradigm shift in the power dynamics of education is therefore underway, with autonomy and agency shifting progressively toward students, and the evolving role of academics highlighting the need for a more facilitative and guiding design rather than traditional didactic approaches. Research findings support a departure from the traditional emphasis on covering vast amounts of content toward fostering deep understanding and mastery of concepts, and call for universities to establish dedicated spaces for professional learning centred on these pedagogical principles (Thomas et al., 2025).

Beetham and Sharpe (2020) argued that digital pedagogy requires teachers to take on roles as learning designers, content curators, and facilitators of collaborative meaning-making. This repositioning of the educator as an architect and facilitator of intentional learning experiences, rather than a transmitter of knowledge, may be the most fundamental expression of the paradigm shift currently underway in higher education.

Conclusion

Taken together, the evidence across these six dimensions, theoretical frameworks, the professionalisation of learning design, empirical outcomes research, the pandemic disruption, technological advancement, and the reframing of educator identity, converges on a clear conclusion. The landscape of higher education is indeed undergoing a fundamental shift from a traditional model of content delivery to a sophisticated paradigm of intentional learning design. This shift is not merely technological or logistical; it is epistemological and pedagogical, representing a reconceptualisation of what teaching is for, what learning looks like, and what higher education institutions are ultimately responsible for delivering.

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References

Afriani, G., Sa’diyah, S., Krisnawati, N., Husnita, L., & Komariah, N. (2025). Teachers as Digital Facilitators: Navigating the Shift from Content Delivery to Learning Design. Global Education Journal, 3(2), 335–341. https://doi.org/10.59525/gej.v3i2.674

Barr, R. B., & Tagg, J. (1995). From Teaching to Learning—A New Paradigm For Undergraduate Education. Change: The Magazine of Higher Learning, 27(6), 12–26. https://doi.org/10.1080/00091383.1995.10544672

Beetham, H., & Sharpe, R. (Eds). (2020). Rethinking pedagogy for a digital age: Principles and practices of design (Third edition). Routledge, Taylor & Francis Group. https://doi.org/10.4324/9781351252805

Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871

Chan, R. Y., Bista, K., & Allen, R. M. (Eds). (2022). Online teaching and learning in higher education during Covid-19: International perspectives and experiences. Routledge.

Chattaraj, D., & Vijayaraghavan, A. P. (2021). The mobility paradigm in higher education: A phenomenological study on the shift in learning space. Smart Learning Environments, 8(1), 15. https://doi.org/10.1186/s40561-021-00162-x

Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257. https://doi.org/10.1073/pnas.1821936116

Dumulescu, D., Pop-Păcurar, I., & Necula, C. V. (2021). Learning Design for Future Higher Education – Insights From the Time of COVID-19. Frontiers in Psychology, 12, 647948. https://doi.org/10.3389/fpsyg.2021.647948

Evangeline, J. (2024). Adapting to Change: The Top Higher Education Trends for 2024. The Higher Education Review. https://www.thehighereducationreview.com/editors-column/adapting-to-change-the-top-higher-education-trends-for-2024-nid-4622.html

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111

García-Morales, V. J., Garrido-Moreno, A., & Martín-Rojas, R. (2021). The Transformation of Higher Education After the COVID Disruption: Emerging Challenges in an Online Learning Scenario. Frontiers in Psychology, 12, 616059. https://doi.org/10.3389/fpsyg.2021.616059

Kickbusch, S., Kelly, N., & Huijser, H. (2025). Framing the core expertise of learning designers through strong concepts. Higher Education. https://doi.org/10.1007/s10734-025-01514-z

Kozanitis, A., & Nenciovici, L. (2023). Effect of active learning versus traditional lecturing on the learning achievement of college students in humanities and social sciences: A meta-analysis. Higher Education, 86(6), 1377–1394. https://doi.org/10.1007/s10734-022-00977-8

Loughlin, C., Lygo-Baker, S., & Lindberg-Sand, Å. (2021). Reclaiming constructive alignment. European Journal of Higher Education, 11(2), 119–136. https://doi.org/10.1080/21568235.2020.1816197

O’Dea, X. (2024). Generative AI: Is it a paradigm shift for higher education? Studies in Higher Education, 49(5), 811–816. https://doi.org/10.1080/03075079.2024.2332944

Pollard, R., & Kumar, S. (2022). Instructional Designers in Higher Education: Roles, Challenges, and Supports. The Journal of Applied Instructional Design. https://doi.org/10.59668/354.5896

Shah, I. H., Au Yong Lyn, A., Morlet, G. M. A., & Rageth, L. (2024). The Impact of Student-Centered Teaching Strategies on Educational Attainment in Econometrics: Evidence From the UK. Sage Open, 14(2), 21582440241240844. https://doi.org/10.1177/21582440241240844

Thomas, M. B., Muscat, A., Zuccolo, A., Luguetti, C. N., & Watt, A. (2025). Navigating Pedagogical Innovation in Higher Education: Education Academics’Experiences with Active and Inquiry-Based Learning in Intensive Teaching. Innovative Higher Education, 50(6), 1917–1943. https://doi.org/10.1007/s10755-025-09807-y

Wang, X., Su, Y., Cheung, S., Wong, E., & Kwong, T. (2013). An exploration of Biggs’ constructive alignment in course design and its impact on students’ learning approaches. Assessment & Evaluation in Higher Education, 38(4), 477–491. https://doi.org/10.1080/02602938.2012.658018

Wiggins, G., & McTighe, J. (2005). Understanding by Design. ASCD.

The core of the 8SLDF is Constructive Alignment, a concept where Intended Learning Outcomes (ILOs), assessment strategies, and teaching activities are inextricably linked. The framework moves beyond the traditional focus on cognitive (intellectual) skills to incorporate five distinct domains of learning: Cognitive, Affective, Metacognitive, Psychomotor, and Interpersonal. By utilising tools such as student personas and the POISE epistemological model, the framework ensures that courses are transparent, culturally aware, and “futurist,” preparing students for careers and technologies that may not yet exist.

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Frameworks vs Principles, Models and Patterns

We must remind ourselves what a Framework is and is not. Learning Frameworks operate between evolved principles and the socio-cultural context in which the designers operate, and the models, based on educational theory, that enable the design encountered by learners.

See the table below for a brief overview.

As I stated elsewhere, the web is awash with sites, often from university learning development units, that merge all of these conceptual levels and approaches under the banner of ‘instructional design models’. In learning design, these terms are often used interchangeably, which can lead to “conceptual fog.” However, they actually represent a hierarchy of abstraction, moving from why we teach to exactly how we build a specific interaction.

Here is how these concepts differ in a functional, pedagogical context:

• Philosophy: Philosophies are high-level belief systems about how humans learn and the purpose of education. They provide the ethical and theoretical foundation for everything else. (such as Social-Constructivism)

• Principles: Fundamental, evidence-based rules that hold across most contexts. They bridge the gap between abstract philosophy and concrete design. (Such as Atkinson’s 10 Principles of Learning Design)

• Framework: A framework is a structural support system. It provides a set of stages or a “skeleton” that organises the design process or the curriculum structure. (Such as the 8SLDF or ADDIE)

• Models: Models are simplified representations of a process or a specific instructional strategy. While frameworks focus on the design process, models often focus on the structure of the instruction itself. (Such as Gagné’s or the 9S)

• Patterns: Patterns are the most granular and practical. A pattern is a repeatable solution to a commonly occurring problem in a specific context. (Such as those shared in the DiAL-e)

The Structure of 8SLDF

Based on the foundation of the 10 Principles of Learning Design, the 8-SLDF is designed to ensure the courseware it develops is aligned, transparent and appropriate for context.

1. Constructively Aligned: Clearly defined ILOs must dictate what is assessed and how teaching is conducted.

2. Transparent: Students should receive a clear map of the learning journey, written in accessible language, explaining not just what they will do, but why they are doing it.

3. Contextually Aware: Designers must acknowledge the specific socio-political and cultural perspectives from which they teach, rather than assuming their orientation is universal. They must also ensure learning is purposeful.

There are 8 distinct stages in the 8SLDF. They must be considered sequentially, but each stage should be rechecked against the preceding stages. This is an inbuilt quality assurance approach.

Stage 0: Orientation

Before design begins, the team must establish the project’s scope. This involves answering foundational questions regarding business cases, competitor analysis, validation processes, and institutional constraints (e.g., credit weightings and timetabling).

Stage 1: Student Personas and Orientations

Designers must move beyond stereotyping to create “flesh-and-blood” personas. This stage utilises two primary models:

1. Student Inclusion Model: This model examines four categories of student variables:

• Circumstantial: Age, disability, employment, location, and IT access

• Educational: Entry qualifications, prior experience, and learning preferences

• Cultural: Language abilities, personal values, and country of origin

• Dispositional: Confidence, motivation, and gender/sexuality

2. The POISE Epistemological Model: This model explores how students believe knowledge is acquired. This is critical for international contexts where cultural views on authority and truth may differ. Each of the five dimensions (derived from scholarship) has a binary concept at its core worth exploring with each new cohort.

• Pace: Quick or not at all (Learning is either instant or impossible)

• Ownership: Authority or Reason (Knowledge is handed down by authority vs arrived at by reason)

• Innateness: Innate or Acquired (The ability to learn is fixed at birth vs developed)

• Simplicity: Simple or Complex (Knowledge consists of isolated facts vs complex networks)

• Exactness: Certain or Tentative (There is always a right answer, vs knowledge is provisional)

Stage 2: Learning Contexts

Designers must act as “futurists,” scanning the horizon for changes in their discipline. This involves reviewing:

• Institutional Context: Academic regulations and strategic objectives.

• National Quality Assurance: Credit frameworks and subject benchmarks.

• Professional Accreditation: Competency frameworks and ethical standards.

• Scholarship Agenda: Academic literature and “Grey Literature” (e.g., software white papers and professional blogs).

Stage 3: Media Contexts

Media choice should be driven by the needs of the student and the requirements of the profession, rather than institutional default settings.

• Staff Capability: Faculty must model the media skills they expect graduates to possess.

• Accessibility: All digital resources (PDFs, PowerPoints, Videos) must be screen-readable, captioned, and copyright-compliant.

• The “Walled Garden”: Designers must decide which tools belong inside the institutional Virtual Learning Environment (VLE) and which should leverage the broader internet.

Stage 4: Intended Learning Outcomes (ILOs)

ILOs describe what a student can do at the end of a course. The 8-SLDF advocates strict adherence to the Single Verb-Subject-Context structure. The framework rejects the “narrow obsession” with cognitive skills, insisting on a multi-domain approach using The Five Domains of Learning (CAMPI):

• Cognitive: Intellectual skills and knowledge structures (Intellectual Skills)

• Affective: Emotional perception, values, and professional ethics (Professional Values)

• Metacognitive: Understanding how one thinks and relates to the discipline (Self-Awareness)

• Psychomotor: Manual, practical, or dexterity skills, including software (Practical Skills)

• Interpersonal: Communication, Conflict resolution, Cross-Cultural and collaboration (Communication Skills)

Well-structured ILOs must start with a single active verb, followed by the subject, and finally the context.

Stage 5: Designing Assessment

Assessment is often the foundation of the student experience. The 8-SLDF promotes several key assessment qualities:

• Authentic: Closely mirrors tasks found in the professional world.

• Durable: Designed to be used over multiple cycles without needing total revision.

• Scaffolded: Assessments build on one another to create a pathway toward increased confidence.

• Criterion-Referenced: Marks are based on predefined quality statements (rubrics) rather than comparisons to other students.

Stage 6: Learning Opportunities

Learning activities should be “rehearsals” for assessment. This stage focuses on mapping session-level Objectives (which are not assessed) to the course-level ILOs (which are). This is the stage at which the Models and Patterns chosen come into play.

Stage 7: Feedback

Feedback should be viewed as a continuous process rather than an administrative post-mortem. The framework uses the TACTICS acronym: Timely, Amount (focused), Clear, Tone (supportive), Informs Teaching, Constructive, and Specific.

Stage 8: Evaluation

Evaluation measures the performance of the course and the teacher, distinct from the assessment of the student. Two important concepts dealt with in this stage are!

• In-Class Evaluation: Conducted roughly one-third of the way through a module to allow for “minor adjustments” to the current cohort.

• Learning Gain: Measuring the “distance travelled” by a student from their starting point to completion, acknowledging that a student starting with low prior knowledge may show more “gain” than an expert, even if their final grade is lower.

Course Design and Artificial Intelligence

As of 2026, the 8-SLDF views AI as a “neutral colleague” or sense-checking instrument. Key insights include:

• Judgement Gap: Current AI tools cannot make the reflective, considered judgments required for holistic design.

• Risk of Fallacies: AI-generated designs often perpetuate existing educational fallacies because they are trained on legacy data.

• Human-Centric Design: There is no substitute for a design team taking a fresh approach aligned with the specific needs of future students. AI should be used to support the team, not replace the design process.

In the next few weeks, we will explore each stage in some depth and react to some of these key statements:

“All learning designers are essentially futurists... You need to be thinking about a future that might be three or four years ahead.”

“Learning design requires a holistic viewpoint. Academics are understandably led by their knowledge and expertise and often teach retrospectively, trying to replicate their own learning pathway.”

“You should never write an outcome that is not assessed and should never assess anything that is not stated as an outcome.”

“Feedback is a normal human communication operation. It is not an administrative duty, only to engage in when faced with a pile of scripts.”

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The Core Concept in this approach aims to move away from seeing courses as static, disposable entities and instead treats them as dynamic ecosystems.

• Reuse: This involves the strategic repurposing of existing learning objects, units, modules, or entire course structures across different programmes. It leverages interoperability, the ability of educational content to function across various platforms and contexts.

• Renewal: The iterative process of updating and refreshing content, pedagogies, and assessments. Rather than a total overhaul, renewal focuses on “evergreening” materials to ensure they remain relevant to industry standards and student needs.

The pressure to innovate while maintaining quality has made these strategies essential for modern institutions:

While the benefits are clear, several systemic hurdles often complicate the process:

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1. Academic Autonomy: Faculty may feel that reusing content infringes on their academic freedom or unique teaching style.

2. Intellectual Property (IP): Distinguishing between institutional ownership and individual creation can create legal and motivational barriers.

3. Contextual Drift: Content designed for a specific level or discipline may lose its effectiveness when moved to a different context without careful re-contextualization.

4. Technical Interoperability: Disparate Learning Management Systems (LMS) or outdated file formats can hinder resource sharing.

Successful reuse is rarely about copy-pasting. It requires a granularity mindset, in which course components are designed as discrete, searchable, and tagged units (often called Learning Objects).

To effectively manage reuse and renewal, institutions are adopting a Curriculum Lifecycle Management (CLM) approach, which involves regular “health checks” for courses, where data from student feedback and labour market trends trigger specific renewal actions rather than waiting for a periodic five-year review.

Here are three sub-principles to inform learning design teams to enable reuse and renewal.

We ensure that each assessable ILO is mapped appropriately. Mapping Intended Learning Outcomes (ILOs) to Graduate Profile Outcomes (GPOs) and establishing a hierarchy of non-assessed learning objectives enables the redeployment of existing resources across courses and programmes. Outcome-Based Education (OBE) and the concept of Constructive Alignment are at work here. By deconstructing a programme and its course constituents into a granular hierarchy, institutions create a ‘modular’ architecture. This modularity is what enables the strategic reuse and renewal of educational resources.

Three tenets of this sub-principle are:

The Role of Mapping and Hierarchy: Mapping ensures horizontal and vertical alignment. When design teams establish a hierarchy of non-assessed objectives beneath assessable ILOs, they create a map of the specific knowledge and skills required (Biggs & Tang, 2007). This allows designers to identify ‘shared’ components across different disciplines.

Facilitating Resource Redeployment: When resources (videos, readings, simulations) are tagged to specific, granular objectives rather than a specific course title, they can become more effective Learning Objects. Research suggests that this metadata-driven approach allows a single high-quality resource to serve multiple courses that share the same foundational enabling objectives (Polsani, 2003).

Enhancing Renewal: A mapped system makes ‘renewal’ more surgical. If a GPO changes due to industry shifts, designers can trace exactly which ILOs and resources need updating without having to dismantle the entire programme (Uchiyama & Radin, 2009).

The second sub-principle is a mechanistic principle, a necessary step for identification and retrieval.

We design learning that is meta-tagged and recoverable. A necessary, technically valid and foundational principle of Learning Design when applied to the management of digital learning assets. In academic contexts, this refers to the transition from “monolithic” course design, in which content is confined to a single syllabus, to a Learning Object (LO) model.

Three tenets of this sub-principle are:

Meta-tagging and Granularity: For learning to be recoverable, it must be broken down into discrete units. Meta-tagging involves assigning structured data (keywords, difficulty level, copyright, and linked ILOs) to these units. This is governed by international standards like IEEE LOM (Learning Object Metadata) and SCORM (Yassine et al., 2016).

The “Recoverability” Factor: In this context, “recoverable” means discoverable. Without standardised tagging, high-quality resources become “dark data”, content that exists on a server but cannot be found or reused by other faculty members because it is not indexed against specific outcomes or topics (Wiley, 2002).

Strategic Value for Renewal: A meta-tagged library allows for “just-in-time” curriculum renewal. If a specific industry regulation changes, a learning designer can search the metadata to “recover” every module across the entire institution that touches on that regulation, ensuring a comprehensive update (Downes, 2001).

The final sub-principle addresses the need for careful curation of reusing others’ resources.

We link intelligently to selected curated resources. We work collectively to meet the need for continuous content updates and maintenance, acknowledging that our designs are never 100% complete. The ambition here is to address the contemporary shift from “static” course design toward Agile Curriculum Development and Curriculum-as-a-Service (CaaS). It moves away from the “Not Invented Here” syndrome (the desire to build everything internally) so prevalent in academia. It acknowledges the half-life of knowledge, the reality that information in many fields becomes obsolete faster than traditional review cycles can keep up with.

This principle rests on these three elements.

Intelligent Linking and Curation: Instead of creating bespoke content for every topic, “intelligent linking” utilises high-quality Open Educational Resources (OER) and industry-standard documentation. This is a recognised strategy for maintaining academic rigour while reducing the development burden. Curation is viewed as a scholarly act of pedagogical filtering (Siemens, 2005). Subject Matter Experts or academic contributors to courses should assemble, annotate, classify and tag existing resources rather than write new narratives.

Collective Maintenance (The “Wiki” Model): Seeing maintenance as a shared institutional responsibility rather than a solo endeavour. This aligns with “Communities of Practice” (CoP) theory, where faculty collaborate to keep a shared resource pool “fresh” rather than working in silos (Wenger, 1999). Clearly, this only works when there is a common design framework and an acknowledgement of any existing learning patterns. However, it can mean that ongoing, consistent updates to courseware are possible.

The “Beta” Mindset (Never 100% Complete): Acknowledging that design is never finished is a core tenet of Iterative Design. In software and now in education, this is often called “Permanent Beta.” It enables continuous improvement based on real-time student data and evolving industry requirements (Llorente Cejudo, 2013).

Together, these three sub-principles describe a Modular, Metadata-Driven, and Agile curriculum framework.

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Downes, S. (2001). Learning Objects: Resources For Distance Education Worldwide. The International Review of Research in Open and Distributed Learning, 2(1). https://doi.org/10.19173/irrodl.v2i1.32

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Uchiyama, K. P., & Radin, J. L. (2009). Curriculum Mapping in Higher Education: A Vehicle for Collaboration. Innovative Higher Education, 33(4), 271–280. https://doi.org/10.1007/s10755-008-9078-8

Wenger, E. (1999). Communities of practice: Learning, meaning, and identity. Cambridge University Press.

Wiley, D. A. (Ed.). (2002). The instructional use of learning objects (1. ed). Agency for Instructional Technology Association for Educational Communications & Technology.

Yassine, S., Kadry, S., & Sicilia, M. A. (2016). Learning Analytics and Learning Objects Repositories: Overview and Future Directions. In Learning, Design, and Technology (pp. 1–29). Springer, Cham. https://doi.org/10.1007/978-3-319-17727-4_13-1

Our programme and course designs should be created to enable appropriate levels of data to be captured during and after delivery. This requires some engineering. We need to begin by distinguishing among three fields of educational data analysis by their primary focus, the techniques they employ, and the level at which they intervene in the educational environment. These are contested definitions, but these are the ones I work with.

Educational Data Mining (EDM): Influenced by information science and machine learning, EDM identifies predictive student behaviours through large datasets, often inter-institutional. It utilises classical statistical analysis and neural networks to help institutions plan and alter course designs or assessments at the programme level.

Academic Analytics (AA): This field focuses on organisational efficiencies and business data within an institutional context. Its key objectives include improving student retention, faculty effectiveness, and institutional delivery through techniques like analysing enrollment patterns and space usage.

Learning Analytics (LA): LA centres on supporting the individual learner experience. It aims to optimise social engagement and individual learning trajectories using specialised methods such as network, discourse, and semantic analysis. Unlike the other fields, its scope is specifically on individualised data, including social variables and contextual conditions.

The following table compares their key characteristics:

Understanding these three fields of educational data analysis allows us to explore the three sub-principles that inform learning designers’ practice, primarily in learning analytics. Here are the three sub-principles that explore the practical implications:

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This sub-principle underpins the motives for designers and learners.

We structure learning within optimally defined and responsive pathways, allowing learners to support their choices to advance quickly through courseware or revisit earlier studies, where academic regulations allow. The literature sometimes refers to Adaptive Learning Pathways (ALP) and Flexible Learning Pathways (FLP). These pathways emphasise student agency, allowing learners to control their pace, either by progressing quickly through work demonstrated as mastered or by revisiting earlier work, with support from real-time data and institutional policy.

This sub-principle relies on four pillars:

Responsive and Optimal Pathways: Modern pedagogical frameworks use ‘macro-sequencing’ to determine the order of units of study based on learner performance. Designers should ensure the pathway is optimally defined by maintaining an appropriate level of challenge for each individual (Reigeluth, 2007).

Speed and Mastery: Adaptive systems allow students to bypass content they have already mastered, facilitating faster progression (Cavanagh et al., 2020). Research indicates that when students are empowered with self-regulated learning (SRL) strategies, they manage their time more effectively and achieve higher academic outcomes (Theobald, 2021).

Remediation and Revisiting: Effective pathways including micro-sequencing for remediation when performance criteria are not met, providing targeted support to correct misconceptions before the student moves forward (Bhardwaj et al., 2025).

Academic Regulations and Policy: The successful implementation of these pathways often depends on institutional policies that recognise credit accumulation, transfer, and diverse modes of study delivery (Müller et al., 2023). Designers need to influence policies.

This sub-principle raises the issues of design.

We establish touchpoints for supportive intervention, structuring the learning journey to trace learners’ engagement. These touchpoints respect learners’ ownership of their data. The challenge for the design team is to balance proactive support with data privacy and student agency.

This sub-principle raises three critical challenges affecting contemporary digital education in particular:

Supportive Intervention: Using “touch-points” (often triggered by specific learner behaviours) is a hallmark of Just-in-Time or Proactive Intervention models. These are designed to provide help before a learner disengages or fails. Research highlights that structured touchpoints, derived from engagement data, can significantly improve retention, provided these interventions are supportive rather than punitive to maintain a positive student-teacher relationship (de Freitas et al., 2015).

Structuring the Journey: Learning designers must map an ‘optimal’ path to make engagement visible and measurable. The flexibility of that path depends on how formal and rigid academic regulations are within any given institution. However, tracing engagement requires creating meaningful touchpoints that provide context to the data, rather than just collecting clicks in a vacuum (Macfadyen et al., 2020).

Data Ownership & Ethics: This addresses the “Privacy Paradox” in education. Prinsloo and Slade (2016) argue that learners should not be passive subjects of surveillance but active collaborators with ownership and agency over how their data is used to support their learning journey. For learning analytics to be effective, they must be transparent and respect learners’ rights over their digital footprints.

This third and final sub-principle reinforces the reasons for learning designs to consider these issues.

We design courses that can incorporate emerging online learning practices. We do this by anticipating the introduction of adaptive learning technologies and supportive analytics that guide the learner to success. Designing courses to anticipate emergent technologies is a proactive strategy for improving learning outcomes and student retention. There is ample research to confirm that integrating adaptive learning technologies and supportive analytics into course design allows for:

Personalisation: Dynamically adjusting content and pacing based on real-time learner data (Gevorgyan, 2024).

Predictive Support: Identifying “at-risk” students through engagement patterns and providing targeted interventions before failure occurs (Bird, 2023).

Self-Regulated Learning: Empowering learners to manage their own progress through data-driven feedback loops requires course facilitators and designers to attend to their learners’ digital literacy needs (Yaseen et al., 2025).

Well-designed adaptive learning policies, tools and procedures promise to significantly increase academic performance by tailoring instruction to individual needs. In a 2024 meta-analysis, du Plooy and colleagues reported performance gains in 59% of cases and greater learner engagement in 36%. (du Plooy et al., 2024). The inter-relationship between learning analytics and adaptive systems creates a new learning design paradigm in which data is not merely collected but used to optimise the learning environment itself (Atkinson, 2015).

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Atkinson, S. P. (2015). Adaptive Learning and Learning Analytics: A new design paradigm. BPP University

Bhardwaj, V., Zhang, S., Tan, Y. Q., & Pandey, V. (2025). Redefining learning: Student-centered strategies for academic and personal growth. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1518602

Bird, K. (2023). Predictive Analytics in Higher Education: The Promises and Challenges of Using Machine Learning to Improve Student Success. The AIR Professional File, Fall 2023. https://doi.org/10.34315/apf1612023

Cavanagh, T., Chen, B., Lahcen, R. A. M., & Paradiso, J. (2020). Constructing a Design Framework and Pedagogical Approach for Adaptive Learning in Higher Education: A Practitioner’s Perspective. The International Review of Research in Open and Distributed Learning, 21(1), 172–196. https://doi.org/10.19173/irrodl.v21i1.4557

de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I., & Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6), 1175–1188. https://doi.org/10.1111/bjet.12212

du Plooy, E., Casteleijn, D., & Franzsen, D. (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, 10(21), e39630. https://doi.org/10.1016/j.heliyon.2024.e39630

Gevorgyan, S. (2024). The Use of Adaptive Learning Technologies in e-Learning for Inclusive Education: A Systematic Review. E-Learning Innovations Journal, 2(1), 90–107. https://doi.org/10.57125/ELIJ.2024.03.25.05

Macfadyen, L. P., Lockyer, L., & Rienties, B. (2020). Learning Design and Learning Analytics: Snapshot 2020. Journal of Learning Analytics, 7(3), 6–12. https://doi.org/10.18608/jla.2020.73.2

Müller, C., Mildenberger, T., & Steingruber, D. (2023). Learning effectiveness of a flexible learning study programme in a blended learning design: Why are some courses more effective than others? International Journal of Educational Technology in Higher Education, 20(1), 10. https://doi.org/10.1186/s41239-022-00379-x

Prinsloo, P., & Slade, S. (2016). Student Vulnerability, Agency and Learning Analytics: An Exploration. Journal of Learning Analytics, 3(1), 159–182. https://doi.org/10.18608/jla.2016.31.10

Reigeluth, C. M. (2007). Order, First Step to Mastery: An Introduction to Sequencing in Instructional Design. In F. E. Ritter, J. Nerb, E. Lehtinen, & T. O’Shea (Eds), In Order to Learn: How the sequence of topics influences learning (p. 0). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195178845.003.0002

Theobald, M. (2021). Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, 101976. https://doi.org/10.1016/j.cedpsych.2021.101976

Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A.-A. A. (2025). The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy. Sustainability, 17(3), 1133. https://doi.org/10.3390/su17031133

Here are three sub-principles that unpack this Principle:

We design in light of the changing nature of work, shifting patterns of employment and the evolving needs of professions. Our designs are informed by scholarly review, ongoing consultancy with professional representatives, and learners’ evaluation of their learning experiences. Recognising that academics and learning designers may not have studied within a discipline for many years means that, by definition, their experience of learning a particular subject is historically situated. That is not to say that their foundational knowledge and the way a subject or discipline was taught is irrelevant, but it does mean that each learning design team should see any programme or course design with its future students in mind and recognise the ‘Half-Life’ of knowledge. Design teams need to have a sound strategy for horizon scanning, consult professional bodies on the state of a sector or industry, and be well-versed in both academic and grey literature.

The required shift is from a retrospective design (as taught) to a prospective design (aligned with future professional needs). Designers need to address the “competence gap” that occurs when academic curricula fail to keep pace with the rapid evolution of industry and society.

The core of the sub-principle is validated by four theoretical pillars:

The “Half-Life” of Knowledge: The assertion that practitioner experience becomes “obsolete” over time is supported by the concept of skill decay. In many modern fields, particularly those involving technology, the half-life of a learned skill is estimated at approximately 5 years (McGowan & Shipley, 2020). This necessitates a design approach that prioritises current “practice-based evidence” over historical academic tradition.

Horizon Scanning in Curriculum Design: The need for a sound horizon-scanning strategy is a recognised necessity in Strategic Foresight for education. According to the OECD (2020), educational institutions must move from reactive planning to “prospecting”, actively identifying “weak signals” of change in the professional sector to ensure graduates remain relevant.

The Role of Grey Literature and Industry Consultation: Relying solely on peer-reviewed academic journals can lead to a lag in curriculum relevance, as the publication cycle often takes years. Validating designs through “grey literature” (industry reports, white papers, and government policy) and ongoing consultancy ensures that the curriculum reflects the “bleeding edge” of professional shifts (Susskind & Susskind, 2017).

Student-Centric Future Orientation: Shifting focus from “previous adherents” to “future students” aligns with User-Centred Design (UCD) principles applied to pedagogy. This involves creating “learner personas” based on future work scenarios rather than historical student profiles (Goodyear, 2015). See stage 2 of the 8SLDF on Student Personas.

The second sub-principle explored further the need for future thinking.

We anticipate the future in our designs, including the impact of technological and broader societal changes on the nature of work within a particular discipline. Building a robust horizon-scanning procedure at the outset of any program or course design is the only way to ensure course designs are not out of date on the very day they are launched to students. Carefully designing learning and teaching activities to ensure that skills and abilities are future-proof is a significant challenge, but not an impossible one. There is an argument that the majority of university courses should include learning activities that directly address the potential impact of technological and broader societal changes on that individual’s future. Learning design teams should adopt a proactive, “future-back” approach to curriculum development, moving away from static, historically-driven models toward dynamic horizon scanning and industry-aligned design.

Here are four supporting pillars for this sub-principle:

Obsolescence of Prior Experience: The assertion that an academic’s personal experience of learning a subject may be historically situated is a polite way of saying “obsolete”. is supported by the concept of the “half-life of skills.” In many technical and professional fields, the half-life of a learned skill is now estimated at as little as 5 years (McGowan & Shipley, 2020). This necessitates a shift from teaching as we were taught to a model of co-construction with current practitioners.

Horizon Scanning and Future-Proofing: Education programmes must move beyond reacting to change and instead adopt “prospecting” to anticipate multiple future scenarios, ensuring that current curricula do not become “legacy systems” upon launch (OECD, 2020).

The “Future” as Core Curriculum: My argument that courses should directly address the impact of societal and technological change aligns with Future-Oriented Chemistry and similar movements in other disciplines. This is often termed Futures Literacy, the capability that allows people to better understand the role of the future in what they see and do today (Miller, 2019).

Anticipating Technological Impact: Drawing on Susskind’s work, the evolution of professions is no longer linear. Susskind and Susskind (2017) argue that traditional professions are being dismantled by increasingly capable systems, necessitating a design focus on “para-professional” tasks and human-AI collaboration rather than solely on traditional foundational knowledge, ensuring the focus remains on broad competencies rather than fleeting technical skills.

The third sub-principle further reinforces the need for a focus on the technology perspective.

We design for the future by anticipating the changing needs of students and stakeholders, as well as technological advancements. We do this by actively engaging with current practice-based evidence in research and the global market. Future thinking can feel quite alien to many learning designers, but it is a skill that can be developed and is best practised with others. The fundamental idea is a creative one: designed teams should project their graduates' working lives 10 or 20 years into the future. Not so much that one needs to build a futuristic, high-tech course suitable for delivery in 20 years’ time, but to lay a pathway towards the future that does not lead to obsolescence. The required mindset for design teams is to adopt a “long-view” of education, shifting the focus from immediate graduation to long-term career sustainability. It frames future thinking not as a predictive science, but as a creative, collaborative discipline essential for responsible learning design.

Here are four supporting pillars for this sub-principle:

Anticipating Needs through Practice-Based Evidence: Engaging with both research and the global market aligns with the Evidence-Based Design (EBD) movement. This approach ensures that future thinking is not mere speculation but is grounded in a practice where designers use current trends to map future skill requirements.

The 10–20-Year Horizon: Projecting 20 years ahead is a core tenet of Strategic Foresight in Education. While traditional planning cycles are 3–5 years, “Futures Literacy” requires imagining longer horizons to identify weak signals of change that will eventually reshape a profession (Miller, 2019). This prevents path dependency, where designers simply iterate on what already exists.

Laying Pathways against Obsolescence: The goal of not building a high-tech course for today but a pathway for the future is supported by the concept of Transversal Skills, as explored by the International Commission on the Futures of Education (2021). By focusing on learning how to learn and cognitive flexibility, design teams provide students with the foundational “meta-skills” required to navigate a future curriculum (Dede & Richards, 2020)

Future Thinking as a Collaborative Skill: That this is a skill, best practised with others, is validated by the Social Constructivist view of foresight. Collaborative “visioning” exercises among diverse stakeholders (academics, industry, students) reduce individual bias and lead to more robust, “anti-fragile” educational models (Fullan & Quinn, 2016).

Curriculum design is an act of future-making.

Dede, C., & Richards, J. (Eds). (2020). The 60-Year Curriculum: New Models for Lifelong Learning in the Digital Economy. Routledge. https://doi.org/10.4324/9781003013617

Fullan, M., & Quinn, J. (2016). Coherence: The right drivers in action for schools, districts, and systems. Corwin, a SAGE Company.

Goodyear, P. (2015). Teaching as design. HERDSA Review of Higher Education, 2, 27–50.

International Commission on the Futures of Education. (2021). Reimagining our futures together: A new social contract for education. UNESCO. https://doi.org/10.54675/ASRB4722

McGowan, H., & Shipley, C. (2020). The adaptation advantage: Let go, learn fast, and thrive in the future of work. Wiley.

Miller, R. (2019). Transforming the Future: Anticipation in the 21st Century (R. Miller, Ed.). Taylor & Francis Routledge [Imprint].

Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016

OECD. (2020). Back to the Future of Education: Four OECD Scenarios for Schooling. Educational Research and Innovation. https://doi.org/10.1787/178ef527-en

Spicer, A. (2020). Organizational Culture and COVID-19. Journal of Management Studies, 57(8), 1737–1740. https://doi.org/10.1111/joms.12625

Susskind, R., & Susskind, D. (2017). The Future of the Professions: How Technology Will Transform the Work of Human Experts (Reprint edition). OUP Oxford.

Lifelong learning is best conceived as the ongoing, self-motivated pursuit of knowledge for personal or professional growth, aimed at enhancing an individual’s employability, social inclusion, and personal development. Within the workforce, many licensed professions mandate continuous professional development as a prerequisite for maintaining licensure (or certification), underscoring the necessity of evolving alongside one’s field. To support these efforts, dedicated lifelong learning institutes and informal learning communities exist globally, providing the structured and social frameworks necessary to facilitate continuous education throughout a person’s life. The quality of the educational provision varies enormously.

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Lifelong learning and learner autonomy are two sides of the same coin. While lifelong learning describes the span (or duration) of education (from “cradle to grave”), learner autonomy describes the individual’s capacity and agency to drive that process.

The relationship between these two concepts is essentially one of necessity and enablement:

• Autonomy is a Prerequisite: Lifelong learning is defined as “voluntary and self-motivated.” Without learner autonomy, the ability to take charge of one’s own learning, a person cannot sustain education once they leave the formal, teacher-led classroom.

• The Shift in Responsibility: In traditional education, the institution is responsible for the curriculum (Formal Education). In lifelong learning, the individual becomes the “architect” of their own knowledge (Informal Learning). This requires the autonomous skills of goal-setting, resource identification, and self-assessment.

• The Goal of Empowerment: Many modern educators argue that the primary goal of formal education should be to develop learner autonomy, specifically to equip students to remain lifelong learners in an ever-changing job market.

The following table illustrates how specific autonomous traits manifest in a lifelong learning context:

Learner autonomy isn’t just a “nice-to-have” trait; it is the engine that keeps the lifelong learning process moving. Without the internal drive and the “how-to-learn” skillset, the pursuit of knowledge often stops the moment an award is handed out.

Here are three sub-principles to support this Principle:

We allow the learner to define their own route, fully informed of the professional and academic requirements for their validated learning. We do not restrict the learner to our planned pathway. Learning is structured so that learners can review early learning and teaching activities and determine their own competence (Atkinson, 2011), thereby empowering them to decide whether to take foundational topics or move forward in their learning.

The aim here is to align with established educational theories regarding learner autonomy, self-directed learning (SDL), and competence-based education. It describes a shift from “planned pathways” (teacher-centred) to “defined routes” (learner-centred).

The core of the sub-principle is validated by several key theoretical pillars:

• Agency and Route Definition: Allowing learners to define their own route is the practical application of learner agency. Benson (2013) defines learner autonomy as the capacity to take control of one’s own learning, which specifically includes the ability to determine the direction and sequence of study.

• Informed Decision-Making: The concept of being fully informed of professional and academic requirements corresponds with the concept of scaffolding autonomy. Reinders (2010) argues that for autonomy to be effective, instructors must provide a framework of independent learning skills and clear standards so that the learner’s choices are grounded in professional reality rather than a lack of direction.

• The Role of Reflection and Competence: Reviewing early learning to determine one’s own competence is a metacognitive process. This aligns with Self-Determination Theory (SDT), in which the need for competence is a primary psychological need that drives intrinsic motivation (Ryan & Deci, 2017). By allowing learners to skip foundational topics they already master, the aim is something that looks more like a heutagogical approach, self-determined learning where the learner is the central agent in their own development (Blaschke, 2012).

We acknowledge the social and cultural context of our learners (Deci & Ryan, 2000) and support them in orienting themselves as autonomous learners, embracing their future as lifelong learners, and engaging with and sharing with their community. The aim here is to highlight the intersection of Self-Determination Theory (SDT) and sociocultural theory, and to allow indigenous ontologies and epistemologies to play their part. By acknowledging the social and cultural context, we are applying a culturally responsive pedagogy that recognises learning is not an isolated cognitive act but one embedded in the learner’s identity and community relationships.

The core of this second sub-principle is supported by several key theoretical pillars:

• Social and Cultural Context (SDT): While Deci and Ryan (2000) are primarily known for the internal psychological needs of autonomy, competence, and relatedness, they explicitly state that these needs are universal, but their expression is culturally mediated. Supporting a learner’s “relatedness” involves acknowledging their social origins and cultural values to foster intrinsic motivation (Ryan & Deci, 2017).

• The Learner and Community: Community can be defined in various ways. It could be your neighbourhood, your work colleagues, or your socio-cultural, religious and ethnic community. The important thing is acknowledging that learners share an identity with others. In the context of Māori pedagogy (Kaupapa Māori), the learner is not an individual unit but part of a collective. Macfarlane et al. (Macfarlane et al., 2007) emphasise that, for Māori learners, success is intrinsically linked to whanaungatanga (relationship-building) and family involvement. Engaging the whānau transforms the educational journey from a private endeavour into a collective contribution to the community’s future.

• Orienting Toward the Future: The transition from a “student” to an “autonomous lifelong learner” requires what Biesta (2015) calls subjectification, the process of becoming an independent agent. By supporting this orientation, designers are helping learners build the “capacity to aspire” within their own cultural framework (Appadurai, 2004).

We encourage the learner to personalise their learning experience by drawing on their professional and social context in their study and assessment. Personalisation and customisation have been two ambitious targets for course designers for decades. The principles of Andragogy (adult learning theory) and Authentic Assessment both argue that learning is most effective when it is situated within the learner’s lived experience. By integrating professional and social contexts into the curriculum and assessment, designers aim to foster deep learning rather than surface-level memorisation.

The third and final sub-principle is validated by several key theoretical pillars:

• Contextualised Learning: Personalising the experience by drawing on professional contexts aligns with Malcolm Knowles’ assumption that adult learners are task-centred and problem-centred (1984). They are most motivated to learn when the subject matter has immediate relevance to their job or personal life.

• Authentic Assessment: Incorporating a learner’s social and professional reality into assessments is a hallmark of “authentic assessment.” For an assessment to be authentic, it must reflect the “professional practice” and the social contexts in which the skills will eventually be applied. This increases the “ecological validity” of the learning (Gulikers et al., 2004).

• The Social Constructivist View: Drawing on the social context, acknowledges that knowledge is co-constructed within a community. By encouraging learners to bring their own environments into their studies, designers can facilitate situated learning, in which learning occurs through participation in “communities of practice” (Lave & Wenger, 1991).

Appadurai, A. (2004). The Capacity to Aspire: Culture and the Terms of Recognition. In V. Roa & M. Walton (Eds), Culture and Public Action (pp. 59–84). Standford University Press. https://gsdrc.org/document-library/the-capacity-to-aspire-culture-and-the-terms-of-recognition/

Atkinson, S. P. (2011). Embodied and Embedded Theory in Practice: The Student-Owned Learning-Engagement (SOLE) Model. The International Review of Research in Open and Distance Learning, 12(2), 1–18.

Benson, P. (2013). Teaching and Researching: Autonomy in Language Learning (2nd edn). Routledge. https://doi.org/10.4324/9781315833767

Biesta, G. (2015). What is Education For? On Good Education, Teacher Judgement, and Educational Professionalism. European Journal of Education, 50(1), 75–87. https://doi.org/10.1111/ejed.12109

Blaschke, L. M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-determined learning. The International Review of Research in Open and Distributed Learning, 13(1), 56–71. https://doi.org/10.19173/irrodl.v13i1.1076

Deci, E. L., & Ryan, R. M. (2000). The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Gulikers, J. T. M., Bastiaens, T. J., & Kirschner, P. A. (2004). A five-dimensional framework for authentic assessment. Educational Technology Research and Development, 52(3), 67–86. https://doi.org/10.1007/BF02504676

Knowles, M. S. (1984). The Adult Learner: A Neglected Species (3rd edn). (Original work published Gulf Publishing)

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Macfarlane, A., Glynn, T., Cavanagh, T., & Bateman, S. (2007). Creating Culturally-Safe Schools for Māori Students. The Australian Journal of Indigenous Education, 36(1), 65–76. https://doi.org/10.1017/S1326011100004439

Reinders, H. (2010). Towards a Classroom Pedagogy for Learner Autonomy: A Framework of Independent Language Learning Skills. Australian Journal of Teacher Education, 35(5). https://doi.org/10.14221/ajte.2010v35n5.4

Ryan, R. M., & Deci, E. L. (Eds). (2017). Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. Guilford Press. https://doi.org/10.1521/978.14625/28806

Applied learning is defined by several key elements that distinguish it from traditional rote learning:

• Real-World Context: Problems and projects mirror actual challenges found in professional or civic life.

• Active Engagement: The learner is a participant, not just an observer. This often involves building, creating, or troubleshooting.

• Reflection: A critical component where the learner evaluates what worked, what didn’t, and how the theory shifted when put into practice.

• Transferable Skills: It emphasises “soft skills” like communication, teamwork, and critical thinking alongside technical knowledge.

Here are three sub-principles that unpack this for learning designers.

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We support learners in their professional and vocational advancement and their desire for social enhancement. The reality is that many University students still embark on a multi-year degree programme with no clear career pathway. They may choose a degree because it is regarded as their strongest subject at school, or simply because they find it fun or interesting. Other learners clearly identify the benefits of successfully completing and getting credit for a specific programme or course. It is also not unusual for students to change their motivation as their studies progress. Our learning designs cannot assume a homogeneous set of motivations and must be cognisant of the wide range of choices students make and the reasons behind them (Ryan & Deci, 2017).

In practical terms, this means that as designers, we need to ensure we are continuously horizon scanning for changing trajectories within our disciplines and making appropriate modifications to the curriculum. This does not mean a rewrite of course or programme outcomes if these were written properly in the first instance (Wiggins & McTighe, 2005).

We encourage learners to be informed, free-thinking individuals and enable them to contribute to global society. I am an internationalist, so my interpretation of this sub-principle may differ slightly from yours. I am an advocate of Global Citizenship Education (GCE) and Social Responsibility (Rhoads & Szelényi, 2011). You may choose to change the framing from global to national, even regional or local, depending on your context. The key point is that graduates from our programmes should be able to make a beneficial contribution beyond their own self-interest. This is based on a philosophy that the modern university remains a “Public Good”. It also implies that the relationship between institution and learner should be more transformational than transactional.

In our designs, we should aim to foster critical pedagogies that enable learners to become critical thinkers rather than passive recipients of knowledge within a defined context (Freire, 1996). This often represents itself as a form of Glocalisation, thinking globally, acting locally. This is an important concept for designers who work on programmes with a specific local or regional focus, such as community nursing, regional development, or urban planning.

We champion clearly articulated outcomes, in the form of concise and precise Graduate Profile Outcomes (GPOs) and enhance their development through well-articulated Intended Learning Outcomes (ILOs), often broadening the learning journey to encompass a wide range of competencies designed to reflect a broad range of personal and professional skills (Atkinson, 2015). These outcomes must be constructively aligned (Biggs & Tang, 2007).

The language may vary depending on your institution’s style and regulations. For GPOs, we sometimes see Graduate Attributes (Barrie, 2006), and the distinction between ILOs and “Unintended Learning Outcomes” is important to me, but not to many designers. I draw a distinction between Intended and Unintended Learning Outcomes because I favour broader, portfolio-based assessments that encourage individuals to identify learning beyond the curriculum (Cambridge, 2010). It is important to note that the Unintended Learning Outcomes are not mere accidents; they constitute Emergent Outcomes that can be actively supported alongside a managed curriculum (Osberg & Biesta, 2008).

Atkinson, S. P. (2015). Graduate Competencies, Employability and Educational Taxonomies: Critique of Intended Learning Outcomes. Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education, 10(2), 154–177.

Barrie, S. C. (2006). Understanding What We Mean by the Generic Attributes of Graduates. Higher Education, 51(2), 215–241. https://doi.org/10.1007/s10734-004-6384-7

Biggs, J., & Tang, C. (2007). Teaching for Quality Learning at University: What the Student does (3rd edn). Open University Press.

Cambridge, D. (2010). Eportfolios for Lifelong Learning and Assessment. John Wiley & Sons, Incorporated.

Freire, P. (1996). Pedagogy of the Oppressed (M. B. Ramos, Trans.; 2Rev Ed). Penguin.

Osberg, D., & Biesta, G. (2008). The emergent curriculum: Navigating a complex course between unguided learning and planned enculturation. Journal of Curriculum Studies, 40(3), 313–328. https://doi.org/10.1080/00220270701610746

Rhoads, R. A., & Szelényi, K. (2011). Global citizenship and the university: Advancing social life and relations in an interdependent world. Stanford University Press.

Ryan, R. M., & Deci, E. L. (Eds). (2017). Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness. Guilford Press. https://doi.org/10.1521/978.14625/28806

Wiggins, G., & McTighe, J. (2005). Understanding by Design. ASCD.

Here are three sub-principles to illustrate this.

We design for the contemporary learner, aware of the changing patterns in digital and information literacies amongst our learners, and distinguish between digital capabilities across disciplines and levels of study. The contemporary learner is most likely ahead of us in our default settings. They are potentially more comfortable with digital applications. But we cannot assume this to be true. We cannot design learning that assumes a significant level of digital literacy (Kirschner & De Bruyckere, 2017).

As learning designers, we should be wary of assuming comfort equals competence. While a student might be “ahead” in terms of social media navigation or UI intuition, they often lack functional digital literacy for academia, such as:

• Evaluating the credibility of AI-generated sources.

• Data management and privacy ethics.

• Navigating complex, non-intuitive Learning Management Systems (LMS).

If a course design assumes high-level digital skills, the “hidden curriculum” risks punishing students from ‘low-digital access’ contexts, lower socioeconomic backgrounds or those returning to education after a hiatus. Scaffolding digital tasks ensures that the grade reflects the student’s understanding of the subject rather than their ability to troubleshoot software (Castañeda & Williamson, 2021).

A Computer Science student’s digital needs are fundamentally different from a Fine Arts or Nursing student’s. This can be conceived of as two different axes: vertical and horizontal literacy (JISC, 2021).

• Vertical Literacy: Progressing from basic digital tasks in Year 1 to specialised professional software in Year 3.

• Horizontal Literacy: Understanding how digital tools specifically apply to their chosen field.

The question designers need to be asking is, while we believe our learners can out-TikTok us in their sleep, can they cite a database? We need to strike a balance between respecting the learner’s lived experience and upholding institutional responsibility.

We design for digital versatility, acknowledging that learners expect to access learning at any time and in any place, increasingly using mobile devices to support their learning (MacDonald & Creanor, 2017). Our emphasis on digital design means we strive for digital innovation in learning and creating courseware that is learner-focused, engaging, and personalised.

While the first sub-principle focused on equity and scaffolding, this one focuses on delivery and user experience. There has been a trend towards “m-learning” (mobile learning) and “u-learning” (ubiquitous learning), but these carry some significant pedagogical risks that savvy designers will immediately spot (Bravo-Adasme & Cataldo, 2022).

Modern learners are often “time-poor”, balancing work, caregiving, and commuting. Designing learning for the “edges of the day” (the 15-minute bus ride) is a pragmatic response to learner demographics. This encourages designers to move away from 60-minute recorded lectures (which fail on mobile devices) toward microlearning (5-minute videos, interactive flashcards, or scannable text). There is a healthy body of research around Cognitive Load Theory (CLT) (Sweller, 2023).

Most designers have moved beyond the “PDF graveyard” (just uploading documents to an LMS), but we often worry that “innovation” is code for “expensive tech for the sake of tech.” In an academic context, innovation is only valid if it improves learning outcomes, not just “engagement” metrics.

Claiming to create personalised courseware is a challenge. This means designing for adaptive release or AI-driven pathways, where a learner who expresses a conceptual misunderstanding (failing a quiz) receives different material than one who passes with flying colours. it. This is currently an incredibly labour-intensive process (although systems and platforms are making it easier) (Zizikova et al., 2021).

The second sub-principle needs to be tempered with the realism of the first sub-principle. If we design for “innovation” and “mobile-first” without the “scaffolding” and “literacy awareness”, we risk creating a beautiful, high-tech course that half of our learners can’t actually use effectively.

We adopt appropriate and available tools that highlight the newest pedagogies in technology-enhanced learning. I appreciate that in the world of learning design, there is a constant tension between innovation (using the latest tech) and efficacy (using what actually works). There is an underlying concept through which this principle should be interpreted: we are, as designers, “Pedagogy-First” rather than “Technology-First”. Pedagogy should drive the tool selection (Photopoulos & Triantis, 2022).

Institutional leadership, and at times the IT department, appear to display some “techno-solutionism” (Morozov, 2014), the belief that buying the newest tool will automatically result in better learning. The reality is that a “new” pedagogy (like AI-integrated Socratic questioning) is only as good as the learning science behind it.

Focusing on available technologies acknowledges the reality of university budgets and IT security protocols. You can’t design for a VR lab if the university doesn’t own one. It signals to the designers that you aren’t asking them to build “castles in the sky,” but to work within the institutional ecosystem.

The phrase “newest pedagogies” might trigger scepticism from some of you. You have seen many “revolutions” come and go (MOOCs, Second Life, gamification badges) that didn’t always deliver on their promises. However, the focus here is on pedagogies and not the technologies themselves. Evaluating new technologies and evaluating their impact on learners is a field of research worth tracking.

In summary, as learning designers, we should aim to adopt appropriate and available tools to implement evidence-based pedagogies in technology-enhanced learning, ensuring our methods evolve alongside digital advancements.

Bravo-Adasme, N., & Cataldo, A. (2022). Understanding techno-distress and its influence on educational communities: A two-wave study with multiple data samples. Technology in Society, 70, 102045. https://doi.org/10.1016/j.techsoc.2022.102045

Castañeda, L., & Williamson, B. (2021). Assembling New Toolboxes of Methods and Theories for Innovative Critical Research on Educational Technology. Journal of New Approaches in Educational Research, 10(1), 1–14. https://doi.org/10.7821/naer.2021.1.703

JISC. (2021). Individual digital capabilities. JISC. JISC. https://digitalcapability.jisc.ac.uk/what-is-digital-capability/individual-digital-capabilities/

Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. https://doi.org/10.1016/j.tate.2017.06.001

MacDonald, J., & Creanor, L. (2017). Learning with Online and Mobile Technologies: A Student Survival Guide. Routledge Ltd.

Morozov, E. (2014). To save everything, click here: The folly of technological solutionism (Paperback 1. publ). PublicAffairs.

Müller, C. (2025). Digital Learning Design: Designing Effective Online and Blended Learning. Springer Nature. https://doi.org/10.1007/978-3-031-89045-1

Photopoulos, P., & Triantis, D. (2022). Think Twice: First for Tech, Then for Ed. SN Computer Science, 4(2), 123. https://doi.org/10.1007/s42979-022-01538-7

Sweller, J. (2023). Cognitive load theory. In International Encyclopedia of Education (pp. 127–134). Elsevier. https://doi.org/10.1016/B978-0-12-818630-5.14020-5

Zizikova, S. I., Shikhovtsov, Y. V., & Matasova, I. L. (2021). Transformation Of Education In The Context Of Digitalization. European Proceedings of Social and Behavioural Sciences, Global Challenges and Prospects of The Modern Economic Development. https://doi.org/10.15405/epsbs.2021.04.02.128

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We design for context, not cohort, unless there is a specific accreditation requirement. This statement makes the most sense in remote-learning contexts where bringing students together in the same physical space is impossible. However, I believe it also makes sense in most online synchronous learning contexts, where forcing students to collaborate when there is no assessed outcome that merits it usually meets resistance. Situated learning does not automatically imply socially constructed learning (Vygotsky, 1978). Situated learning emphasises where and how knowledge is used, while social constructivism emphasises who creates the knowledge and through what interpersonal processes. The fetishisation of collaborative learning has done enormous damage to some learners’ progress. Making your student participate in group activities because they are ‘in vogue’ without a discernible objective directly mapped to an outcome can be negative (Kirschner et al., 2006). The emphasis should be on designing for authentic contexts.

We structure learning so that individuals experience it as and when they need it. A context-focused approach puts the onus on us to design learning that is directly relevant to the learner in their own setting, social and/or professional, encouraging them to leverage a wide range of professional networks and social engagements to support their learning journey (Luckin, 2010). This means encouraging students, where appropriate, to engage with family, friends and colleagues, as well as or as an alternative to their fellow students. If the intention is to broaden perspectives and widen the pool of knowledge and experience, how much better is it for students to gain insight from individuals other than their fellow students?

Designing demand-driven learning is a challenge. A student does not always know what they need at any given point in time. Designing learning objectives with incremental progression, using carefully crafted taxonomies across relevant domains, allows students to navigate a variety of optional pathways. This scaffolded autonomy relies on three aspects of educational psychology: students’ metacognitive limitations, instructional design (using taxonomies), and course architecture. Students often lack the ability to recognise their own knowledge gaps (Kruger & Dunning, 1999), and without carefully designed pathways, many student sexpeerience cognitive paralysis.

We design for emerging patterns of learning facilitation. We design for as many different learning patterns as possible so as not to overly restrict students to ‘our way of learning’. This means we are open to challenges from our students and colleagues about how best to learn and, therefore, teach specific skills and attributes. Setting an Intended Learning Outcome and acknowledging that individuals might find different pathways to reach it is the way to go. Making those pathways transparent as part of the evidence presented for assessment will enrich the course and its design modifications going forward. The aspiration should be for learning designers to create courses that follow the three tenets of Universal Design for Learning (Rose & Meyer, 2002)in providing students with:

Multiple Means of Engagement: Tapping into different learner interests and motivations.

Multiple Means of Representation: Giving learners various ways to acquire information.

Multiple Means of Action and Expression: Allowing students to demonstrate what they know through different “pathways.”

References

Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75. https://doi.org/10.1207/s15326985ep4102_1

Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. https://doi.org/10.1037/0022-3514.77.6.1121

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.

Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred ecologies. Routledge.

Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for supervision and curriculum development.

Seely-Brown, J., Collins, A., & Duguid, P. (1989). Situated Cognition and the Culture of Learning. Educational Researcher, 18(1), 32–42. https://doi.org/10.2307/1176008

Törmänen, T., Ketonen, E., Lehtoaho, E., Turunen, M., Zabolotna, K., Shubina, T., & Järvenoja, H. (2025). Situational Motivation in Academic Learning: A Systematic Review. Educational Psychology Review, 37(2), 56. https://doi.org/10.1007/s10648-025-10036-0

Vygotsky, L. S. (1978). Mind in Society: Development of Higher Psychological Processes (New edition). Harvard University Press.

The second of ten Principles for Learning Design is that we design to harness authoritative voices. This may appear if it is stated as its opposite, we certainly design to harness the voices of the ignorant! However, it is worth restating that in higher education in the Occidental world, we operate in a post-enlightenment, rational context in which those with evidence and wisdom, rather than thoughts and urges, are deemed worthy of emulation. There is a caveat to this opening statement, though. The days when you went to university, and you got the course based on the book of the lecturer who declared themselves as the authority have largely gone. A plurality of voices, opinions, conjectures and evidence is ordinarily present in most higher education courses today, although not always labelled as such.

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Here are three supporting clauses for this principle, which I think design teams should discuss.

We work with academics and subject matter experts to ensure that our learning materials are current, accurate, and future-focused (Holmberg, 1983)

The way we establish who has an authoritative voice has changed in recent decades. Historically, the monograph held sway over reading lists, supplemented by peer-reviewed journal articles. This was at least in part because the notion of editorial oversight and peer review served as a quality check on the evidence presented. Increasingly, acknowledged experts are creating their own blogs and publishing on Substack or Conversation. Long-form specialised articles and even documentaries are increasingly being cited by journal authors. The challenge remains to identify whether a voice, regardless of the platform, is informed and authoritative.

We listen to multiple voices and hear what the learner needs, paying attention to learners’ evaluative comments and stakeholder expectations.

Plurality in the voices that students are exposed to is a tenet of quality learning in most higher education programmes. This polyvocality is deemed necessary from an epistemological perspective, reinforcing the notion that ‘Western’ thought has developed through this exchange of views and counterviews. As learning designers, we must also respond to the tenor or tone of the authoritative voice. Some may be passionately infused social commentary, which, while it may not negate a point being made, may be unacceptable to a student depending on their socio-cultural background. Balancing the expectations of the stakeholders in any programme, be they industry, discipline specialists, or leading academics, with what we know will ‘get through’ to students is a fine art.

We ensure courseware is designed to be appropriately facilitated, without forcing the learner to engage unless outcomes explicitly require it. We enable, although do not always require, an effective learning-teaching conversation to occur, fulfilling the personal connection and need for support that many (Open Distance Flexible Learning - ODFL) learners require (Laurillard, 2002)

This sub-principle was written in the context of a national distance-learning provider, but I believe it is essential across all higher-education contexts and modes of delivery. The difference between reading a book (or listening to a podcast or watching a documentary) and a designed course lies in the interaction built around that book or resource. Historically, one might have enrolled at a university to gain access to the library or to have an authoritative volume read from the lectern (‘lecturer’), but the ubiquity of knowledge makes that motivation no longer valid. For the last 75 years or more, the primary motivation has not been access but facilitation. The lecturer’s role shifted towards that of a tutor, less to profess the truth than to walk the student through it and its contradictions. Online learning has posed fresh challenges for this facilitation, spawning numerous virtual learning environments and digital discourse tools.

The point about not forcing students to engage is in deference to the growing recognition of students’ autonomy. Forcing a nursing student to participate in an online discussion over a week, when they might live at home with both parents who work in healthcare, seems unfair. Better to have the student present their evidence from their real-world encounters and then explore any contradictions.

So, while we still want to design with authoritative voices, we should acknowledge that these are more diverse than they were when we were undergraduates, and that there is value in authentic worldviews, too, that have inherent authority.

Holmberg, B. (1983) “Guided Didactic Conversation in Distance Education,” in Distance Education: International Perspectives, ed. David Sewart, Desmond Keegan, and Börje Holmberg (London: Croom Helm, 1983), 114–22

Laurillard, D (2002) Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies, 2nd ed, Routledge Falmer

We design for learning that reflects our specific cultural context. This means being conscious of the diverse epistemological approaches reflected in the Indigenous Peoples of our lands, as well as the broader context of multiculturalism. For example, here in Aotearoa New Zealand, where I live, we design with the understanding that Māori have a distinct worldview that informs how they see everything (Henry & Pene, 2001). We may not change the actual knowledge being facilitated, but the manner in which it is shared is subtly different. In already diverse multicultural contexts, like the United Kingdom, my experience is that we ‘just teach’ and expect students from diverse backgrounds to adjust. If we are conscious of our own cultural perspectives and signal them to our students as clearly as possible, they are already one step closer to reconciling their own worldview with ours.

We ensure culturally responsive designs. Choosing culturally appropriate learning materials is an essential first step. The examples you use in the classroom or online, the scenarios you pose, and the activities you design must be culturally aware. I remember being told of the experience (possibly apocryphal) of the Open University UK developing a maths course for the Arab OU and using an example of barrels of beer being rolled up an incline to calculate velocity. Not a good choice. Ideally, we should seek to draw examples and scenarios from the real-life experiences of our own students. When I was at the LSE coaching new tutors, we designed a small activity in which groups of students, who naturally grouped together by country of origin, selected a brand name from their own country and used it to answer a number of questions. This cultural autonomy makes for powerful learning.

We make the cultural perspectives present in the courseware evident. While it is not always possible to interpret learning experiences through multiple cultural lenses, designers should make the lens used evident and encourage learners to reflect on their own perspectives and those of others. This does not mean every learning artefact or teaching session requires an elaborate explanation. But it does mean that designers need to think about it. Look at the reading list for your course. How many different nationalities and ethnicities, genders and time periods, are represented in the voices you are presenting as authentic and authoritative? In some disciplines, the prominence of what is unfavourably referred to as the ‘Dead White Men’ may be entirely logical. In other cases, taking the time to seek out different cultural perspectives on the knowledge at hand provides a much richer learning experience for your students (Wen, 2011).

Finally point. I have an exercise derived from a project called POISE, which stands for Personal Orientation to the International Student Experience (Atkinson, 2014). It is designed for faculty to discuss five questions with their diverse student groups, so that students themselves recognise the diversity in epistemologies in their own cohort. You could use this with your design team, too.

Atkinson, S. P. (2014). Rethinking personal tutoring systems: The need to build on a foundation of epistemological beliefs. BPP University College.

Henry, E., & Pene, H. (2001). Kaupapa Maori: Locating Indigenous Ontology, Epistemology and Methodology in the Academy. Organization, 8(2), 234–242. https://doi.org/10.1177/1350508401082009

Wen, H. (2011). Continuity of Heart-mind and Things-events: A Systematic Reconstruction of Neo-Confucian Epistemology. Asian Philosophy, 21(3), 269–290. https://doi.org/10.1080/09552367.2011.597926

17 March 2026