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Vibe-coding and co-creation: human-AI collaboration in higher education

by Andrew Williams

The debate around artificial intelligence (AI) in higher education has largely focused on students. Are they using ChatGPT in assessments? How do we maintain academic integrity? How should teachers and curricula adapt?

Much less attention has been given to academics working with generative AI (GenAI) as a creative collaborator or co-pilot, rather than merely attempting to mitigate its impact. The potential for productive and effective AI collaboration deserves greater consideration because it challenges longstanding assumptions about expertise, creativity and educational design.

Ever since the release of ChatGPT in November 2022, I have been fascinated by how intuitive the interaction with GenAI tools can be and the ease with which ideas can be explored through ordinary conversation. GenAI is a sophisticated technology that allows users to test possibilities, refine outputs, and rapidly turn preliminary concepts into usable material.

My background is in biological sciences, with no grounding or expertise in computer science, software engineering or coding. For me, this technology has helped me overcome technical barriers and allowed me to explore new ways to teach and to develop bespoke educational resources. I envisioned a learning experience for my undergraduate medical science students (studying human genetics and evolution) that moved beyond static textbook diagrams and dry explanations to a more interactive and visually stimulating format. Realising that vision required a simulation that let students tweak parameters, observe real‑time population dynamics, and grasp complex evolutionary ideas.

Traditionally, such a project would rely on grants, external software developers, or months of self‑taught coding. For most of us, the gap between an innovative pedagogical idea and a digital reality was confined by technical expertise. Today, through iterative collaboration with GenAI, natural language can function as a design interface, allowing educators to transform ideas into working educational tools.

I was also intrigued by the ever improving, and often publicised, capabilities of GenAI tools as coding agents. With no coding experience myself, I decided to try my hand at ‘vibe-coding’.

Natural language as a design interface

‘Vibe-coding’ is an emerging term within technology communities that describes a process where functional intent is communicated via natural language prompts, allowing AI to generate and refine code. This allows the user to focus on what the tool should do rather than how it must be coded. Essentially, ‘vibe-coding’ involves directing the AI to write and co-develop code aligned with your vision and pedagogical objectives. In other words – no code, no problem!

My recent research describes an iterative prompting strategy and the outcomes of a human-AI collaboration to create an interactive simulation that models certain aspects of evolution (Williams, 2026a). Instead of learning JavaScript or working with software developers, I co-created an application with ChatGPT using only natural language prompting. The result was a browser-based simulation capable of supporting inquiry-based learning and formative assessment in undergraduate education.

Rather than blindly generating content based on loosely defined instructions, the human-AI collaboration involved a structured iterative prompting strategy, careful testing of AI-generated output and re-prompting until the desired aims of the project were met. This enabled the creation of an interactive simulation in which students could investigate biological systems, manipulate variables, export datasets and construct evidence-based explanations (figure 1). This allowed students to move beyond consuming information, to generating and interpreting data themselves in real-time.

Figure 1. Snapshot of ‘Survival Island’ – an interactive simulation.

The Illusion of automated expertise

There is a prevailing narrative that AI democratises expertise, in that it allows anyone to do anything more quickly and with less effort. My experience of ‘vibe-coding’ suggested something different. The  credibility of the final simulation depended on actively embedding disciplinary expertise directly into the co-design process.

While the AI had removed the technical barrier (the coding), it had increased the importance of my academic expertise and oversight. The AI could generate a thousand lines of code in seconds, but it couldn’t tell me if the code represented a plausible scientific simulation. My epistemic judgement was required to verify the AI-generated output. The final student-ready simulation required multiple rounds of iteration, testing and re-prompting, until the collaboration achieved a credible and biologically grounded application suitable for teaching.

Rather than replacing my expertise, ChatGPT provided capabilities I do not possess: writing HTML; JavaScript; debugging code. I contributed disciplinary knowledge, pedagogical intent and continuous evaluation. Every feature reflected educational decisions rather than technical ones. The simulation succeeded because the educational need was clearly defined before AI generated the code.

Higher education has often treated GenAI as an automated assistant or as a problem to be mitigated. Increasingly, it may be more productive to think of AI as a collaborator whose contributions depend upon human judgement. Expertise remains indispensable, as humans ultimately remain responsible for judging whether outputs are educationally, scientifically and ethically sound.

Beyond the prompt: the AI literacy gap

Universities have understandably invested considerable effort in developing student AI literacy. This project led me to a broader appreciation of the need to promote teacher AI literacy across higher education, a critical area often overlooked.

AI literacy for teachers is not just about knowing how to write a good prompt or choosing the best online GenAI tool. Rather, it is a multi-dimensional competency that considers foundational knowledge (prompting, tool use), ethical, emotional and technical proficiencies. For educators to co-create teaching resources in collaboration with AI, for example through vibe-coding, more than technical curiosity is required.

The iterative ‘vibe-coding’ process I followed mirrors the ‘critical evaluation’, ‘epistemic’ and ‘innovation’ dimensions of an integrated AI literacy framework for higher education teachers (Williams, 2026b). This recently published conceptual framework comprises eight core AI literacy dimensions, which you can access in full using the link.

In the current context, the important  AI literacy dimensions include:

  • Critical evaluation: the ability to treat the AI model as a “black box” and rigorously verify outputs against authoritative sources.
  • Epistemic authority: a clear understanding of the probabilistic nature of GenAI and the domain-specific expertise to judge AI output, thereby retaining human agency during interactions with AI.
  • Innovation mindset: the willingness to move from being a consumer of tools to a co-creator of bespoke learning environments, including an understanding of how to collaborate effectively with GenAI.

These are becoming essential academic capabilities rather than optional technical skills. This transition presents considerable challenges, as academic practice has historically been predicated on either individual resource creation or the outsourcing of technical development to third parties. GenAI now enables academics to become directors of increasingly sophisticated creative processes. They define the educational challenge, AI performs the technical implementation, and academics evaluate and verify the final outcome.

As technical production becomes easier with GenAI, educational judgement becomes increasingly important. Our value is no longer found in the technical labour of production, but in our ability to define the educational challenge, critique the iterations, and curate the final outcome. The “vibe” we are coding is our pedagogical intent.

Key takeaways

  • AI is a tool, not a replacement.
  • Human-AI collaboration enables pedagogical creativity.
  • ‘Vibe-coding’ uses AI’s coding capabilities but demands epistemic oversight.
  • Teacher AI literacy is multi‑dimensional.
  • Institutional support is required to build that AI literacy.

GenAI is redefining the landscape of higher education, not through incremental improvements to traditional teaching materials, but by enabling entirely new educational content to be created. However, despite the promises of this new technological revolution, we still need robust evidence about student learning gains, engagement, accessibility and the sustainability of AI-generated educational resources.

It is easy to provide AI tools for HE educators to use. The challenge is to give educators the technical, critical and ethical literacy required to use them effectively.

Andrew Williams is a Professor (Teaching) in the Faculty of Medical Sciences at UCL. He teaches immunology and cell and molecular biology to undergraduate and postgraduate students. His research aims to explore the impact of artificial intelligence (AI) on assessment and learning in higher education and the ways in which we can promote student and staff AI literacy.


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Learning analytics won’t close awarding gaps. Better teaching will.

by David Mather

Universities have invested heavily in understanding awarding gaps. We know more than ever about differential outcomes between student groups. We have sophisticated learning analytics platforms, increasingly accurate predictive models and growing confidence in our ability to identify students who may disengage, withdraw or underperform. This is important work, as it has helped institutions move away from reactive support towards earlier and more informed intervention.

Despite this, awarding gaps remain stubbornly persistent. Perhaps that is because we have spent the last decade using data to identify students rather than improve teaching. If we have become better at identifying students who are likely to struggle, why have we made less progress in changing the outcomes we are trying to improve?

Perhaps the answer lies in the purpose we have given our data. Learning analytics has become one of higher education’s greatest success stories. Universities now know more about their students than at any point in the sector’s history. Attendance, engagement with virtual learning environments, assessment patterns and a range of demographic indicators provide increasingly sophisticated pictures of student behaviour (Romero & Ventura, 2024). Most institutions now use this information to answer a single question: who is most likely to struggle?

It is an important question, but I am no longer convinced it is the most educational one.

Learning analytics has become remarkably good at supporting institutional decision making. It helps us identify educational risk, target student support and improve continuation. However, prediction is not pedagogy. Identifying students who may struggle is not the same as understanding how teaching might change their trajectory. Awarding gaps are not created by dashboards. They emerge through complex interactions between curriculum, assessment, expectations, recognition, belonging and students’ experiences of learning. They will not be closed by increasingly sophisticated prediction alone. Therefore, the assertion of this article is simple: better teaching closes awarding gaps. The role of data should be to improve teaching rather than improve prediction.

The distinction that I have presented became clear to me long before I joined higher education. My career began in further education, where I taught and led in sixth form colleges. Like many colleagues working in that sector, I became accustomed to using ALPS, a value added system that compares students’ outcomes with those expected from their prior attainment (ALPS Education, 2025). The numbers themselves were rarely the most interesting part of the conversation – the pedagogic conversations they created were.

Course teams did not gather simply to identify students who might miss their target grades. They asked why one assessment had produced stronger outcomes than another. They debated whether curriculum sequencing was helping students build confidence. They compared approaches to feedback and questioned whether different teaching strategies had influenced progress. Data became a catalyst for professional dialogue. It encouraged teachers to think critically about pedagogy rather than simply monitor performance.

One observation emerged repeatedly. Students at either end of the attainment spectrum were visible. Those achieving well continued to receive stretch and challenge. Those facing significant barriers rightly attracted additional support. However, those in the middle of those two poles often received much less attention. These were students who attended lessons, completed assignments and achieved respectable grades. They were progressing, but they had begun to plateau. Their potential remained largely hidden because nothing within the data suggested they required intervention.  I have become increasingly convinced that higher education contains the same group.

Earlier this year I described those between the two poles as the silent middle (Mather, 2026a). My argument then focused on belonging. Universities have become increasingly effective at identifying students who excel and students who experience significant challenge. Those in between often remain unnoticed because they continue to progress through their programmes without attracting concern. Progress, however, should never be mistaken for fulfilled potential. My earlier articles have led me to a conclusion that the silent middle is not simply a group of overlooked students – they reveal something more fundamental about the questions we ask of our data.

Learning analytics tells us who is likely to struggle. It tells us much less about where teaching is most likely to make the greatest educational difference. Those are not the same question. One helps universities manage institutional risk. The other has the potential to improve educational practice. The distinction is significant because universities cannot close awarding gaps through prediction alone. They will close them by improving teaching, strengthening relationships and creating learning environments in which more students can succeed. Learning analytics has an important role to play in that ambition, but only if we stop asking it to tell us merely who needs support and start asking what it can teach us about teaching itself. The question is not whether universities should continue to invest in learning analytics (they should). The question is whether we have become too comfortable allowing learning analytics to define the educational problem.

For the past decade, learning analytics has largely been framed as an exercise in prediction. Institutions seek to identify students who may withdraw, fail or disengage so that support can be targeted before those outcomes become inevitable (Romero & Ventura, 2024). This represents an advance over retrospective approaches that identified concerns only after students had already fallen behind. Early identification and timely-bound support are crucial. The sector should not lose either.

However, prediction is not an educational strategy. Prediction tells us which students are most likely to experience difficulty. It tells us very little about what educators should do differently. A dashboard cannot redesign a curriculum, improve formative feedback, strengthen relationships between students and staff or create cultivate belonging. All of those are fundamentally pedagogical acts.

Awarding gaps emerge through pedagogy as much as they emerge through student characteristics. Curriculum design, assessment practices, expectations, feedback, representation and relationships all influence whether students flourish or merely progress. All of this means that learning analytics should be judged not simply by its ability to predict outcomes, but by its ability to improve teaching.

The distinction between prediction and teaching quality has profound implications for educational leadership. Every university leader understands that resources are finite. Personal tutors, learning developers, academic skills teams and lecturers cannot provide intensive support to every student throughout the academic year. Decisions therefore have to be made about where educational effort should be concentrated. Those decisions are often informed by indicators of risk. Students who stop attending, miss assessments or disengage understandably receive attention. That is appropriate and necessary, but I wonder whether we have overlooked another question: where is teaching most likely to change a student’s trajectory?

That question shifts the purpose of learning analytics completely. Instead of asking who is most likely to fail, we begin asking where educational opportunity exists. Instead of identifying institutional risk, we begin identifying pedagogical opportunity. Those are not competing priorities – they are different ways of thinking about educational gain.

My experience in further education suggests that this distinction is crucial. ALPS data did not conclude conversations about attainment – it started them. Teachers debated why one class had responded differently from another, why particular assessment approaches appeared more effective and which students seemed to benefit most from timely feedback or increased challenge. Data did not replace professional judgement – it sharpened it. Higher education has an opportunity to claim that mindset.

Freire (1970/2005) argued that education is transformative because it encourages dialogue, reflection and action rather than passive acceptance. His critique was directed at pedagogy, yet it also offers a useful way of thinking about educational data. Learning analytics should not become another form of educational banking in which institutions accumulate information without fundamentally changing practice. Data has educational value only when it changes the conversations educators have about teaching.

Addressing data for the purpose of informing pedagogy and practice should intersect with the conversation about awarding gaps. Universities have invested considerable effort in understanding differential outcomes across student groups. That work has transformed institutional awareness of structural inequalities and rightly challenged assumptions that degree classifications simply reflect individual effort or ability. Research has consistently demonstrated that awarding gaps emerge through complex interactions between curriculum, assessment, belonging, institutional culture and wider social structures rather than any single deficit within students themselves (Mountford-Zimdars et al, 2015). Addressing those gaps therefore requires sustained institutional commitment.

The institutional commitment I refer to is where the silent middle re-enters the conversation. Those in the silent middle are rarely identified as requiring support because they continue to engage, submit work and progress through their programmes (Mather, 2026a). They do not generate alerts because they are doing precisely what institutional systems expect them to do. Many achieve marks in the middle classifications throughout their degrees. They are neither failing nor flourishing – they simply continue. My question is: do our data encourage us to notice them? 

I suspect they do not. As such, the consequence is that some of the students with the greatest capacity for educational gain receive the least pedagogical attention. Universities rightly identify students whose continuation is at risk. We devote much less effort to identifying students whose attainment could be transformed through relatively modest changes to teaching, feedback or academic relationships.

Educational gain is not distributed evenly across a student population. Some learners require substantial pastoral support simply to remain engaged, whilst others may experience significant academic development following one timely conversation, carefully designed formative feedback or an increased sense of belonging within their discipline. If learning analytics cannot help us distinguish between those different forms of educational opportunity, then we are asking increasingly sophisticated systems to answer significantly limited questions. Perhaps the challenge facing higher education is not to build better dashboards – perhaps it is to ask more educational questions of the data we already possess.

The argument I have made in this article is not that universities should invest less in learning analytics. Institutions should continue to develop their analytical capability because understanding students’ experiences is crucial if they are to be enhanced. However, my concern is that we have become so focused on prediction that we have paid insufficient attention to pedagogy. Learning analytics has become exceptionally good at telling us which students are most likely to struggle. It has been much less successful in helping us understand how teaching itself should change. If universities genuinely want to reduce awarding gaps, I contend that we should ask different questions of our data.

First, what do the data tell us about our teaching rather than our students? Much of our current use of learning analytics encourages us to examine student behaviour. Attendance, engagement and submission patterns become indicators of individual risk. Those measures have value, but they tell us little about the effectiveness of our curriculum, assessment design or feedback practices. Every dashboard should prompt course teams to ask whether the learning environment itself requires attention before concluding that the student does.

Second, where is the greatest opportunity for educational gain? Universities have become highly effective at identifying educational risk. We should become equally interested in identifying educational opportunity. Some students require substantial pastoral intervention to remain engaged. Others may transform their academic performance following timely feedback, clearer expectations or a stronger sense of belonging. Educational gain is not distributed evenly across a student population. Learning analytics should help us understand where relatively modest pedagogical changes are likely to have the greatest impact.

Third, who have we stopped noticing because they continue to pass? The silent middle is not simply a descriptive label. It represents a challenge to institutional assumptions about success (Mather, 2026a). Students who consistently achieve marks in the middle classifications rarely appear within institutional dashboards because they generate little concern. Their continued progression can disguise unrealised potential. Universities that are serious about improving outcomes should become just as interested in students who have plateaued as those whose continuation is at risk.

Fourth, what should we teach differently tomorrow because of what the data tell us today? This is the most important question of all. Learning analytics should not conclude a conversation, it should start one. If programme teams leave meetings with a list of students to contact but no discussion about teaching, assessment or curriculum, then the data has served an administrative purpose rather than an educational one. Professional judgement remains the most powerful tool available to educators. Analytics should strengthen that judgement, not replace it.

Finally, how will we know whether our teaching has changed outcomes? Universities devote considerable resources to measuring continuation, progression and degree classifications. Those metrics are important, but they should not become proxies for educational quality. We should also ask whether students have become more confident learners, whether they participate more actively, whether they experience a greater sense of belonging (on their terms) and whether teaching has enabled them to exceed the trajectories that seemed likely at the beginning of their studies. Those are educational outcomes. They deserve to be measured with the same seriousness as institutional performance indicators.

None of the questions I have posed requires higher education to adopt a system such as ALPS. Schools, sixth form colleges and universities pursue different purposes, educate different learners and operate within different frameworks. The lesson is not the system itself – it is the educational philosophy that underpins it. Freire (1970/2005) argued that education becomes transformative through dialogue, reflection and action. The same principle should apply to learning analytics. Data should not become another mechanism for classifying students or managing institutional performance – it should encourage educators to reflect critically on their practice, challenge assumptions about learning and act in ways that improve students’ educational experiences.

My own work over the past year has explored transition, belonging, good teaching and the silent middle (Mather, 2026a, 2026b). This article has helped me realise that these are not separate conversations; they point towards the same conclusion: students succeed because teaching matters. Relationships matter, recognition matters, belonging matters and curriculum matters. Learning analytics only become educationally significant when they help us improve all of those things.

David Mather is Associate Head of School (Students) and Senior Lecturer in Educational Leadership and Management at the University of Portsmouth. His research explores identity, recognition, and transition across educational and professional contexts, with a particular interest in improving educational practice and the student experience. He is the founder of We See You, an initiative supporting Armed Forces, veteran, and Blue Light communities in higher education.


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Sidestepping: my experience as a female Black tutor

by Olajumoke Orebamjo

I have been teaching for over 16 years (with the last 10 years in the tertiary sector) and I have had the pleasure and sometimes, unfortunately, the displeasure of interacting with individuals from diverse backgrounds, races and perspectives. Lately, my role, amongst others, entails one-to-one supervision sessions. This is a role like many other university roles which is repetitive and sometimes mundane in nature. Nevertheless, I particularly enjoy working with mature adult learners as I find my interactions with them intellectually stimulating. The sessions often deviate from the topic of focus to other issues that are not necessarily relevant, but what is gained from these interactions is not just a fulfilment of the aims of the meeting but also a general sense of wellbeing that is cerebral in nature.

The ‘dance’

Because most students of colour have had little or no interaction with a successful individual from a minority ethnic group, what often ensues is what I like to call ‘side-stepping’, as we initially engage in a mental dance around each other, trying to determine each other’s thoughts, and oscillating between ‘prey’ and ‘predator’. This is a natural reaction of defended subjects; ever vigilant and ready to ward off potential threats. We spend some time on this preamble before one of us goes on the attack, which would usually be the student, who would ask the question I’ve heard countless times: ‘how did you get this job?’. There is the assumption that I could only have attained this position by questionable means. The perception of the student is that I’m ‘culturally suspect’ (Orebamjo, 2024) and a possible stumbling block to their academic success. I have even been ascribed the moniker ‘oreo’ – black on the outside but white on the inside – by students who felt the need to express their disappointment that I was not Black enough for their liking or that I ‘act white’ (Orebamjo, 2024). 

The students’ negative reactions never come as a surprise as I have become accustomed to this form of ‘friendly fire’ (Philip, Rocha and Olivares-Pasillas, 2017). It was a recurring phenomenon I endured while delivering the top up degree programme in health and social care in a London-based university. My attempt to mitigate the academic challenges of the mature students, who were all from minority ethnic groups, was met with fierce opposition from the students. In their view, my actions, as a Black tutor, not only exposed their inadequacies, it simulated the unrealistic, unfair and discriminatory practices of a hegemonic system (Orebamjo, 2024).  The students’ thinking was that my being Black meant I would have a better understanding of their lived experiences.

It is therefore no wonder that any encounter with students of colour automatically triggers the ‘caution’, ‘get ready to attack’ and ‘attack!’ or ‘stand down’ (in that order) signal within me. I spontaneously assume a defensive persona, with a corresponding reaction in the student.  Each encounter is the same, commencing with psychological dance; the student undulating between delight (of sharing the commonality of ‘minority’), suspicion (that judgement is looming) and disappointment (that no hoodwinking can take place). I’m also mentally prancing; assured of the semblance of authority I believe I possess, wary of the fact that a ‘deadly’ attack may occur at any moment, while at the same time, trying to convey to the student that ‘you are in a safe place’.

It is what it is

As a Black woman, I am aware of all these defensive tactics from global majority students and my experiences mirror those of colleagues from minority ethnic groups. The reactions of this profile of students are taken for granted and are ‘to be expected’. I do however tread carefully in these interactions because I do not want to fulfil the students’ negative expectations and so spend more time than necessary salving their sense of self-worth in a futile attempt to dispel all negative perceptions they may have about me. It’s like I’m saying, ‘hey I’m one of you so don’t judge me too harshly!’. Eventually though, I resign myself to thinking, ‘it is what it is’.

The racial tension between Blacks and Whites is a common occurrence that is often presumed. Hence it is difficult, if not impossible, to explain these experiences to my White colleagues as these actions and reactions are born of the simple reality of an ’other’ interacting with another ‘other’ within a highly hierarchical higher education arena. Each one is engaged in a constant mental negotiation with the dominant values that pose a threat to their individuality and self-worth, whilst attempting to justify their membership of a seemingly hostile establishment that has no appreciation of their individuality (Tormey, 2021).

Constant reflection, together with extensive engagement with literature on mature learners from minority ethnic groups in higher education, has given me in-depth knowledge and understanding of the educational challenges of this erstwhile marginalised group of students and so I am well equipped to manage the students’ attitudes and emotional baggage. Of greatest value is my engagement with intersectionality (Crenshaw, 1989), which has given me an awareness of how social identities such as race, class, social economic status and gender intersect and overlap to result in complex experiences of disadvantage or privilege. Many students of colour would have experienced multifaceted oppression resulting in defensive attitudes, which they end up bringing into their learning environment (Orebamjo, 2024). To therefore come face to face with a Black individual with some level of authority – especially in a university that has a demographic footprint of almost 100% White – is reason enough for the student to call in the ‘defence calvary’.

And so, the dance continues!

Dr Olajumoke (Jumie) Orebamjo is a lecturer in Practice Development: Health and Social Care and Paramedic Practice at University of Cumbria where she oversees undergraduate and graduate research projects. She’s also a Senior Fellow of the Higher Education Academy and a committed academic with over 12 years of experience teaching and supporting students to overcome academic challenges by developing agency. Proven record of designing and effecting teaching and learning methods that develop students’ skills particularly in metacognition.


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Teaching in higher education: Connected practice for changing times?

by Karen Gravett and Simon Lygo-Baker

Why does teaching matter, and how might we understand what it means to teach in higher education, in contemporary times? This blog introduces our new book Reconceptualising teaching in higher education, published by Routledge. The book is created from our own reading, research, ideas, and practice as two academics working in the field of higher education, where we have been teaching for over twenty years in universities in the UK, America and Australia. It was inspired by our thoughts and discussions surrounding what it means to teach and the joys, pleasures and challenges that accompany our role.

At present there are many questions regarding higher education, its purpose and possible futures. For teachers too, questions remain regarding the necessity and shape of teachers’ contributions in a marketised sector mediated by artificial intelligence and pinched by precarity. And yet, this book is underpinned by our continued belief that teaching matters. We believe that meaningful teaching matters for our students, for our own development and experiences as educators, and for the futures of universities themselves. We argue that teaching provides opportunities for meaningful learning which matters for personal growth and for the development of knowledge. We believe that meaningful learning matters for the creation of new opportunities and possibilities. As bell hooks (1994) explains, fundamentally, education is about ‘the practice of freedom’. In a world where perhaps the experiences we have, the products we purchase, and the information we consume may not always seem meaningful, we believe that the connections that happen when we learn and when we teach have a power that should be harnessed and celebrated. Education matters, because not only does it open doors, but it allows us to recognise them and frame them for ourselves, offering the opportunity to challenge and evolve.

The book is designed for anyone seeking to develop their role as teachers in contemporary universities. This includes new teachers as well as those of us who still have questions and are still keen to develop and respond to our changing times. Specifically, it asks us to rethink our role and the directions we typically follow and suggests the need to disrupt these and to rethink our role as teachers, to take a different path, talk to someone new, or see things a different way. Viewing higher education from new positions can help us to reimagine our role and discover or reclaim the pleasure of teaching. 

To do this, our book challenges the traditional view of teaching as an individual act. Instead, it frames teaching as a relational and situated practice, built on connections with others. Secondly, we explore teaching as an affirmative and emotional endeavour that can inspire others and lead to joyful and generative moments of connection. Lastly, the book positions teaching as a critical practice, where educators are encouraged to embrace uncertainty, question assumptions, and let their approaches evolve. These ideas are all interwoven with practical insights into contemporary areas of practice, including assessment, learning spaces, feedback, digital education, artificial intelligence, learning design, belonging and inclusion, to develop ethical and relational pedagogic approaches. Specifically, the last chapter examines a wide range of key issues, for example feedback frustrations or student engagement, in order to examine how as a reader you might be able to develop approaches and ideas that work for you in responding to some of these challenges. We hope that readers will find the book useful and look forward to continuing conversations around what it means to teach in changing times.

References

Gravett, K and Lygo-Baker, S (2026) Reconceptualising teaching in higher education: Connected practice for changing times Routledge

hooks, b (1994) Teaching to transgress: Education as the practice of freedom Routledge

Dr Karen Gravett is Associate Professor of Higher Education, and Head of the Surrey Institute of Education at the University of Surrey, UK, where her research focuses on the theory-practice of higher education. She is Executive Editor for the journal Teaching in Higher Education, and a member of the editorial board for Learning, Media and Technology. Karen’s latest books are: Gravett, K and Lygo-Baker, S (2026). Reconceptualising teaching in higher education: Connected practice for changing times, Gravett, K (2025) Critical Practice in Higher Education, and Gravett, K (2023) Relational Pedagogies: Connections and Mattering in Higher Education.

Simon Lygo-Baker works as a Senior Lecturer in Clinical Education at King’s College London and has previously worked in the University of Wisconsin, USA and the University of Surrey, UK. He has previously worked on developing curricula with refugees, asylum seekers and other socially excluded groups, as well as working for a number of years in academic development.


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From ad hoc to constructive: the ABC levels of GenAI integration in business education

by Qianqian Chai and Xue Zhou

Introduction – the challenge of GenAI integration in business education

Since the release of ChatGPT, Generative Artificial Intelligence (GenAI) has rapidly entered higher education. Business schools, with their strong ties to industry and emphasis on applied skills, provide a particularly important setting for examining GenAI’s role in curriculum design. Yet, while adoption has expanded quickly, the educational outcomes of GenAI integration have not been consistent (Kurtz et al, 2024). Across cases, educators identified both benefits and risks, including engagement, skills development, and overreliance. This unevenness suggests that rather than reflecting a single trajectory of adoption, early practice appears to involve different approaches to integration. The central issue is not whether GenAI is used, but how different approaches shape outcomes.

This blog draws on our recent study of GenAI integration in business modules at a UK Russell Group university (Zhou et al, 2026). Through a qualitative analysis of 17 educator cases across 24 modules, we examined how GenAI was incorporated into curriculum design, and how different approaches were associated with distinct benefits and challenges. Using the lens of constructive alignment (Biggs, 1996), we identified three patterns of integration: ad hoc, blended, and constructive, which together form the ABC levels for understanding how GenAI is integrated into curriculum practice. We use these levels to explore why some approaches appear more educationally effective than others. In particular, this blog will offer research-informed insights into how GenAI can be integrated more effectively and sustainably in business higher education. While the cases are drawn from business education, the patterns identified and principles of constructive integration have wider relevance across disciplines where GenAI is increasingly embedded in curriculum design.

ABC levels of GenAI integration in the business curriculum

Our analysis identified three levels of GenAI integration: ad hoc, blended, and constructive. Table 1 outlines these distinctions across key dimensions.

Table 1 ABC levels of GenAI curriculum integration

Constructive integration represents a qualitatively different approach, grounded in constructive alignment, where intended learning outcomes, teaching activities, and assessment are deliberately designed to develop and evaluate students’ ability to use GenAI critically and effectively. At this level, GenAI is not an optional or supporting tool, but an integral component of disciplinary learning, with a clear pedagogical purpose and coherent role across the curriculum.

By contrast, ad hoc integration is characterised by occasional and isolated use, where GenAI is introduced as an optional or experimental tool without being planned into the broader curriculum design. Blended integration moves beyond this by incorporating GenAI into selected learning activities or tasks, giving it a more purposeful pedagogical role, but its use remains only partially embedded. Both approaches therefore fall short of the coherence and strategic alignment that define constructive integration.

The distinction between these patterns is therefore not simply a matter of more or less GenAI use, but of how GenAI is positioned within the curriculum: as an experiment, as a support, or as a capability to be deliberately developed. Although developed from business education contexts, this typology offers a lens that can be applied more broadly to understand how GenAI is positioned within different disciplinary curricula.

Why constructive integration matters

Across the cases, GenAI integration generated benefits and challenges across students, educators, and institutions. At the student level, reported benefits included stronger engagement, confidence, and employability-related skills, while the main risks centred on overreliance, inequality, ethical concerns, and ineffective outputs. For educators, benefits included efficiency gains, professional learning, and improved teaching performance, but these were accompanied by increased workload and the need to redesign activities and assessments. At the programme level, GenAI enhanced curriculum relevance but raised concerns about academic standards.

Figure 1 shows that these benefits and challenges were not distributed evenly across the three patterns of integration. Constructive integration displayed the strongest and broadest benefits, while ad hoc and blended approaches showed narrower gains alongside more exposed challenges. In other words, the issue is not whether GenAI brings value or risk, but how curriculum design shapes the balance between them.

Figure 1 Trade-offs of GenAI integration: challenges (red) vs benefits (green)

What makes constructive integration different is not the removal of challenge, but the stronger presence of educational value. In the study, constructively integrated cases were linked more clearly to student engagement, capability development, employability, and curriculum relevance because GenAI was embedded through aligned outcomes, activities, and assessment, rather than added on as a tool or support. Importantly, these cases also showed stronger educator development, including pedagogical reflection and confidence, despite workload pressures. This suggests constructive integration enhances both student outcomes and educator learning by embedding AI within coherent curriculum design.

How constructive integration is achieved

Table 2 presents examples from the modules in this study, showing how GenAI was constructively integrated into existing pedagogical strategies without requiring curriculum redesign.

Table 2 Constructive GenAI Integration into Existing Pedagogical Strategies

Taken together, the cases suggest several practical principles for integrating GenAI more coherently within the curriculum. These principles are not specific to business education, but reflect broader curriculum design considerations that can be adapted across disciplines with different pedagogical traditions.

  • Integration builds on existing pedagogical strategies: GenAI should be embedded within approaches already familiar to the discipline, such as project-based or simulation-based learning, without requiring curriculum redesign (Chugh et al, 2023).
  • Sharpen the role of GenAI by disciplinary purpose: In different contexts, GenAI supported strategic analysis, research and synthesis, reflective thinking, or data interpretation. Its value depends on alignment with module aims (Zhou & Milecka-Forrest, 2021).
  • Make AI use purposeful through assessment and evaluative tasks: In stronger cases, GenAI was connected to tasks that required students to interpret, justify, compare, or critique AI-supported outputs, rather than simply using AI to complete tasks (Biggs & Tang, 2010).
  • Support deeper student engagement through scaffolding: Structured guidance, such as prompting strategies, comparison activities, and reflective tasks, enabled more critical and purposeful use (Cukurova & Miao, 2024).

Overall, constructive integration is less about introducing new tools than about redesigning existing curriculum elements so that GenAI is meaningfully aligned with disciplinary learning.

Conclusion

The ABC levels developed in our study show that GenAI integration in business education does not follow a single trajectory but ranges from ad hoc and blended use to constructive integration. The key difference lies in approach: constructive integration embeds GenAI through aligned outcomes, activities, assessment, and scaffolding. The challenges observed across GenAI integration practices suggest an urgent shift from ad hoc GenAI integration toward strategic and constructive integration in business education. In this way, higher education can support students’ employability and capability development, strengthen educators’ professional and pedagogical confidence, and enable institutions to sustain coherent, future-facing curricula.

Dr Qianqian Chai is a Lecturer in Business and Management at Queen Mary University of London and Chair of the AI in Education Innovation Sub-committee in the School of the Arts. Her research focuses on AI in higher education, including curriculum design, academic integrity, and policy. q.chai@qmul.ac.uk

Professor Xue Zhou is a Professor in AI in Business Education and Dean of AI at the University of Leicester. Her research interests include digital literacy, digital technology adoption, cross-cultural adjustment, and online professionalism. xue.zhou@le.ac.uk


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In defence of SoTL: anchoring educational evaluation and educational research

by Liz Austen

By the end of 2025 I had attended three HE related conferences: Euro SoTL, the Wonkhe Festival of HE and the SRHE Annual Conference. I presented on similar topics at all three events; what evidence do we generate to help us understand and act to enhance student experiences and outcomes in higher education? During the Wonkhe panel and my SRHE session, I defined two approaches at the disposal of HE practitioners:

Higher Education Evaluation: an approach which helps to understand and explore what works and doesn’t work in a given context and is of value to stakeholders. The aim of evaluation is to generate actionable evidence-informed learning, which encourages, informs and supports continuous improvement of process and impact (Evaluation Collective 2025)

Higher Education Research: to extend knowledge and understanding in all areas of educational activity and from a wide range of perspectives, including those of learners, educators, policymakers and the public (adapted from BERA, 2024)

At the Wonkhe panel, Clare Loughlin-Chow (CEO of SRHE) helpfully outlined the higher education research topics that were most prevalent in the SRHE journals. Omar Khan (CEO of TASO) then outlined the scope and priories of TASO, an affiliate member of the government’s What Works Network which focuses on higher education evaluation. My conceptual discussion of evidence generation brought the two together.

At EuroSoTL earlier in the year, my colleagues and I outlined our new institutional approach to the Scholarship of Teaching and Learning:

Scholarship of Teaching and Learning (SoTL): to improve student learning through engagement in the existing knowledge of teaching and learning, developing contextual ideas and innovation in practice, reflecting on practice, applying methodological rigour, working in partnership with students, and sharing of scholarship publicly (adapted from Felton (2013))

When I attended SRHE in December 2025, SoTL appeared in only one session I attended and some of this discussion focused on the challenges of bringing SoTL into spaces for educational research. My hand in the air comment – that criticism of SoTL by educational researchers was an example of ‘academic snobbery’ – certainly raised a few eyebrows. This blog post considers the relationship between these three approaches and whether, for the good of our students, it’s time for some reconciliation.

Educational evaluation, SoTL and educational research

Educational research in higher education has developed over the last 60 years. Interestingly, research into teaching and learning is cited as the most theorised by this type of research (Tight, 2012). Higher education evaluation, sometimes considered as applied research, was recently propelled by the Office for Students’ agenda to ‘evaluate, evaluate, evaluate (Office for Students, 2022). SoTL has developed alongside HE research and evaluation, emerging from Boyer’s work in 1990.

The aims of each endeavour are distinct, tied by the notion of ‘enquiry’. Research seeks to build new knowledge, and evaluation seeks to provide judgment on a contextual problem. SoTL has a narrower focus on teaching and learning than the broader scope of research and evaluation but incorporates prior knowledge and contextual problem solving through focused enquiry (Gray, 2025). SoTL builds on the foundation of social sciences methodology and can integrate disciplinary methodology into practitioner’s teaching and learning enquiry (Riddell, 2026). Educational evaluation often asks questions of the effectiveness of interventions, but in some teaching and learning spaces, the evaluative language of ‘intervention’ isn’t appropriate (Austen (2025) in Austen and McCaig (2025)). Exploring what works through SoTL enquiry aligns better. Often the bridging term ‘pedagogic research’ is used as integral to SoTL (close to practice) but distinct from educational research (broader anticipated impact). Our chosen SoTL definition uses neither research nor evaluation terminology, but has component parts – knowledge, innovation, method, dissemination – that are central to all.

The essential agents in educational research, evaluation and SoTL are the same – individual students (as partners, as participants and as voice givers), individual staff (academic and professional services), institutional groups or clusters, collaborating HEIs, and third space organisations. Reasons for enquiry are also similar and include sector expectations and shared learning, the desire for institutional enhancement and impact, personal development and career progression. Or as Ashwin & Trigwell (in Evans et al, 2021) note:  to inform a wider audience; to inform a group within context; to inform oneself. All research, evaluation and SoTL agents must navigate the practical and ethical considerations of ‘insider’ enquiry if they are exploring their own practices or within their institutional contexts (BERA, 2018; Barnett & Camfield, 2016).

Output pathways are also interconnected. The SoTL staircase (Beckingham, 2023) recognised the variety of outputs encouraged by SoTL and includes those traditionally aligned with research and evaluation (reports and journal articles). Research outputs may be guided by REF criteria, and evaluation outputs by readership. The conclusions in research articles frequently state that more research needed, and evaluation reports often sit unread in metaphorical desk draws. In comparison, SoTL practitioners benefit from publications which are close to practice, quicker to publish, and more likely to influence change.

Both educational evaluation and educational research are inherently theoretical, grounded by educational or pedagogic theory or a theory of change. SoTL is more action focused, less theoretical than research yet can be more exploratory than evaluation. In 2011, Kanuka questioned SoTL’s credibility due to the lack of theoretical underpinning or reference to existing scholarship. At times, I suggest that educational research can be positioned too far in the opposite direction. The presentations at SRHE were heavily theoretical and sometimes I was left thinking ‘so how would this work actually improve the learning experiences of students’? In contrast, the breadth of SoTL includes both theory and action, albeit in more pragmatic ways.

There are values and specific skill sets of educational researchers and evaluators (and often epistemological disagreements occur between the two). This commitment to identity can be excluding and may help to understand why SoTL has been challenged. Canning & Masika (2022) caution us on the ‘threat to serious scholarship’ posed by SoTL, which they believe risks devaluing research into higher education learning and teaching. Their criticism of ‘anything goes’ I would frame as an important approach to inclusion. Their criticism of the ‘watered-down version of teaching and learning research’ I frame as SoTL’s recognition of the developmental, particularly in building staff confidence. Where confusion over definitions and scope still occur, I question whether institutional SoTL has been well grounded or well led.

Conclusion

There is clearly a divide between higher education research and SoTL. There are few recent SRHE blog posts which reference SoTL at all and one that does advises against flag-in-the-sand nomenclature (Sheridan, 2019). Having spent a lot of time in these circles, I believe higher education evaluators are more agnostic, but I include them in this discussion as they bring a new dynamic to this debate.

In this blog I have identified the ways in which research, evaluation and SoTL have their own agendas and yet have much in common. I argue that SoTL emerges as a grounding anchor between higher education research and higher education evaluation. SoTL borrows from both. SoTL feeds into both. SoTL is more than both (Potter, 2025). SoTL’s inherent value is the ability to build a community which improves student experiences and outcomes in an enquiry led and timely way.

For more details on the approach to SoTL at Sheffield Hallam University see: https://lta.shu.ac.uk/scholarship

Reference

Riddell, J (2026) ‘Hope circuits in practice: how the scholarship of teaching and learning fuels pedagogical courage and systemic change’ Guest Lecture, Sheffield Hallam University

Liz Austen is Professor of Higher Education Evaluation and Associate Dean Learning, Teaching and Student Success at Sheffield Hallam University. She has worked as an independent Evaluation Consultant on HE sector contracts and is a regular keynote speaker on all things evaluation in HE. Her focus is on evidence informed practice across the student lifecycle. Liz also leads a cross sector HE network called the Evaluation Collective.


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The missing middle ground between research-led and practice-led education

by Saeed Talebi and Nick Morton

A peer reviewer recently challenged our pedagogical approach. We had described embedding an industry-led research project on Digital Twin development into our built environment curriculum as ‘research-informed teaching’. The reviewer disagreed: this was ‘practice-led rather than research-informed,’ they argued, because students weren’t producing research outputs themselves.

The comment revealed a conceptual confusion we suspect is widespread in higher education. We often assume that if students aren’t producing original research, then any industry-focused teaching must simply be vocational training with academic window-dressing. This leaves practice-facing disciplines in an awkward position: industry engagement is essential to what we do, but it risks being dismissed as less scholarly. There is, however, a middle ground.

Healey and Jenkins’ (2009) model offers a useful way through this confusion. They identify four modes of engaging undergraduates with research: research-led (learning about current scholarship), research-oriented (learning research methods), research-based (undertaking inquiry), and research-tutored (engaging in research discussions). These are mapped across two dimensions: whether students are positioned as audience or participants, and whether the emphasis falls on research content or processes. The model’s key insight is that students can be meaningfully engaged with research even when they aren’t producing research outputs themselves. The question isn’t simply whether students are ‘doing research’, it’s whether they’re positioned as passive recipients of established knowledge or as active participants in scholarly inquiry.

Practice-led teaching operates on different logic, though that logic has a closer relationship to applied research than is sometimes acknowledged. Its primary aim is developing professional competence through authentic engagement with messy problems and competing stakeholder priorities. The distinction isn’t whether industry is involved – it can be present in both approaches. The distinction lies in how students are positioned in relation to knowledge. In practice-led education, knowledge tends to be treated as relatively settled. In research-informed education, knowledge is contested, evolving, and open to question. An opportunity arises when these approaches coincide without conscious design, and a risk emerges when they collapse into one another. Research-informed teaching can become performative, referencing staff publications without changing how students learn. Practice-led teaching can slip into employability theatre, where live briefs are added without interrogating what knowledge students are actually developing.

As Professor Hanifa Shah OBE recently argued in Times Higher Education, STEAM education at its best equips students to “move fluidly between analytical and imaginative modes of thinking“, asking critical questions, considering ethical implications, and bringing meaning to innovation. This is precisely the disposition that research-informed teaching seeks to develop. In STEAM disciplines, including architecture, built environment, computing and engineering, emerging technologies create spaces where research and practice intersect meaningfully. Digital Twins and real-time monitoring tools, for example, allow students to work with live systems while engaging critically with the assumptions and ethics embedded within them. Students aren’t merely applying research after the fact, nor mimicking professional routines. They’re learning to question how data is generated, how models simplify reality, and how decisions are shaped by both evidence and judgement. Practice becomes a site of inquiry.

There’s an institutional dimension here too. Across the sector, promotion frameworks, workload models, and teaching quality metrics often reward research visibility and industry engagement without asking how either is translated pedagogically. Academics are encouraged to ‘bring research into teaching’ and ‘embed employability’, yet rarely supported in doing the difficult design work that meaningful integration requires. Recent discussions within the sector have highlighted how delivery models shape the possibilities for integrating academic and workplace learning. These are sector-wide conversations, and they reflect shared challenges around diverse learner cohorts, blended delivery, and the risk of compliance overtaking genuine learning. As a result, many innovative practices remain dependent on individual effort rather than structural support.

None of this means practice-led and research-informed approaches are mutually exclusive. The most effective curricula often blend elements of both. But blending deliberately is quite different from conflating accidentally.

When designing industry-engaged teaching, it’s worth asking honest questions. Are students positioned as inquirers or executors? Are they engaging with contested knowledge or settled practice? Does assessment reward critical reflection or merely competent performance? Is the industry project a vehicle for scholarly inquiry, or is scholarly framing a veneer over vocational training?

The answers won’t always be clear-cut, and that’s fine. But asking the questions helps us design with intention rather than stumbling into confusion – and helps us articulate what we’re doing when a peer reviewer, a sceptical colleague, or a university committee asks us to justify our approach.

Dr Saeed Talebi is an Associate Professor in the Department of Architecture and Built Environment at Birmingham City University and a Senior Fellow of the Higher Education Academy (SFHEA). He has held a number of T&L leadership roles, including Departmental Lead, Course Leader, and Academic Lead for Teaching Excellence and Student Experience. He has a keen interest in pedagogy in higher education, with particular interest in research-informed teaching and the integration of emerging technologies and practice-led projects into built environment curricula to enhance student outcomes and experience. He has also led the delivery of large STEAM research projects.

Professor Nick Morton is the Academic Director of Partnerships and STEAM at Birmingham City University. A Principal Fellow of the Higher Education Academy (PFHEA), he was awarded a National Teaching Fellowship in recognition of his track record in curriculum development. He has held a number of senior leadership roles at BCU, including Associate Dean for Teaching Education and Student Experience, overseeing Computing, Engineering and the Built Environment. He was elected Vice-Chair of the Council of Heads of the Built Environment (CHOBE) in 2012 and is a Fellow of the Royal Institution of Chartered Surveyors (FRICS).


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Judgement under pressure: generative AI and the emotional labour of learning

by Joanne Irving-Walton

What AI absorbs and why that matters

Most debates about generative AI in higher education fixate on what it produces: essays, summaries, answers, paraphrases. I find myself increasingly interested in something else – what it absorbs. Over the past year, as conversations about AI have threaded through seminars and tutorials, a pattern has gradually become visible. In those discussions, students rarely begin with content production; instead, they talk about how it helps them get started and steadies them enough to keep going. They use it when the blank page paralyses, when feedback stings and when uncertainty feels exposing. One student described asking AI to “make it feel possible”. Another spoke of feeding tutor comments into the system so they could be “explained more kindly”. A third reflected, almost apologetically, “I don’t want it to do my work… I just need something to push against before I say it out loud and risk looking stupid”.

In each case, AI is not replacing thinking. It is absorbing part of the emotional labour involved in it, and as that labour is redistributed, the texture of judgement shifts. Academic judgement does not tend to emerge from comfort. It develops in the stretch between knowing and not knowing, when confidence dips, stakes feel heightened, and your sense of competence is quietly tested (Barnett, 2007). Staying in that stretch long enough for thinking to clarify demands more than intellectual effort; it requires emotional steadiness, time, space and the capacity to tolerate uncertainty without rushing to resolution (Biesta, 2013). Traditionally, that steadying work has been shared across learning relationships: tutors reframing feedback, peers normalising confusion, supervisors encouraging persistence through doubt. Generative AI now occupies part of that terrain.

I do not think this is inherently a problem. For some students, it is transformative. It marks a shift in where the labour of learning takes place and that change deserves examination rather than alarm.

Four modes of engagement and emotional labour

When students talk about how they use AI, their practices tend to cluster into four overlapping orientations. These are not moral categories so much as shifts in where emotional and cognitive labour is undertaken.

Instrumental engagement appears when students use AI to summarise readings, refine phrasing or impose structure. Here the friction lies in form-making and shaping thought into something communicable. The judgement at stake is procedural: what is proportionate or efficient in this context?

Dialogic engagement emerges when students test interpretations or rehearse arguments. AI becomes a low-stakes sounding board, absorbing some of the vulnerability of articulating something half-formed. The question beneath it is interpretive: what does this mean, and how far do I trust my reading and myself?

Metacognitive engagement is evident when students ask AI to critique their reasoning or compare approaches. What is absorbed here is evaluative tension and the discomfort of examining one’s own argument. The judgement in play is comparative and strategic: which option is stronger, and why? And then there is affective-regulatory engagement. Here, AI absorbs the anxiety that precedes judgement itself. It breaks tasks into steps, softens feedback, lowers the threshold for beginning, offers reassurance before submission and quietens the internal ruminations and rehearsals of everything that might go wrong. This is not peripheral to learning. It is increasingly central.

Figure: Where the labour of learning now lives

Accessibility, safety and the risk of smoothing too much

For many students, particularly those navigating anxiety, executive dysfunction, neurodivergence or heavy external commitments, this emotional buffering is not indulgence but access (Rose & Meyer, 2002). Breaking tasks into steps or privately rehearsing ideas before speaking can widen participation rather than diminish it.

We should not romanticise struggle. Nor should we imagine that institutional structures have ever been able to hold every student perfectly. For some learners, AI offers another place to rehearse thinking, one that sits alongside, rather than replaces, human dialogue.

But there is a tension here. If AI consistently absorbs the strain of uncertainty before ideas encounter resistance, if feedback is softened before it unsettles, if structure replaces the slow work of wrestling thought into form, then something quieter begins to shift. Much of this work happens privately, in browser tabs and late-night prompts, in spaces students do not always feel comfortable admitting to. That makes it harder for us to see what is being strengthened and what may be thinning. The danger is not comfort, but the quiet disappearance of formative strain.

By formative strain, I do not mean suffering for its own sake, nor simply the “desirable difficulties” described in cognitive load theory (Bjork & Bjork, 2011) or the stretching associated with a Vygotskian zone of proximal development (Vygotsky, 1978). I am referring to the lived experience of remaining with ambiguity, critique and partial understanding long enough for judgement to consolidate; the emotional as well as cognitive work of staying with a problem. If that work is always pre-processed, it may narrow the rehearsal space where judgement forms.

Scaffold or substitute

Much depends on whether AI remains a scaffold or begins to function as a substitute. Used as scaffold, it lowers the emotional threshold just enough for deeper engagement, absorbing anxiety without displacing judgement. Used as substitute, it reduces not only strain but evaluation itself; the work of deciding and committing shifts elsewhere. The distinction lies less in the tool than in how it is woven into the learning environment.

Individual awareness and institutional responsibility

It would be easy, and unfair, to frame this as a matter of individual discernment. Students already carry a great deal. But nor is this simply a matter of institutional correction. We are all navigating new terrain in real time, without a settled script.

If we are serious about judgement formation, then responsibility is shared — and it is evolving. This is less about detection or prohibition than about openness. AI engagement is happening whether we discuss it or not. The question is whether we bring it into the light. That might mean inviting students to reflect on how they used AI in a task, not as confession, but as analysis. It might mean modelling, in our own teaching, what it looks like to question or refine an AI response rather than accept it wholesale. It certainly means acknowledging the emotional labour of learning openly (Newton, 2014), recognising that starting can be harder than finishing and that this, too, is part of learning.

At a structural level, we also need some candour. Systems built on speed, metrics and visible output inevitably amplify the appeal of friction-reducing tools. If polish is rewarded more consistently than process, we should not be surprised when students bypass the stretch between uncertainty and articulation. Cultivating discernment, then, is not a matter of allocating blame. It is a collective project of making the shifting terrain of AI use visible, discussable and educative.

Where the emotional work now lives

Generative AI has not diminished the importance of human judgement. If anything, it has made visible how emotionally mediated that judgement has always been (Immordino-Yang & Damasio, 2007). The interior work of learning – the hesitation, the rehearsal, the private negotiation of uncertainty – has never been fully observable. It has always unfolded, at least in part, elsewhere.

What AI changes is not the existence of that interior space, but its texture. Some of that labour now takes place in dialogue with a system that can stabilise, extend or subtly redirect thinking. That creates an opportunity: we are at a juncture where the emotional dimensions of learning can be surfaced and examined more deliberately than before.

It also carries risk. Students can disappear down an AI rabbit hole just as easily as they once disappeared into rumination. The question is not whether the interior work exists, but how it is shaped and whether it ultimately strengthens judgement or thins it.

References

Barnett, R (2007) A will to learn: Being a student in an age of uncertainty Open University Press

Biesta, GJJ (2013) The beautiful risk of education Paradigm Publishers

Bjork, EL & Bjork, RA (2011) ‘Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning’ in MA Gernsbacher, RW Pew, LM Hough & JR Pomerantz (eds), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64) Worth Publishers

Newton, DP (2014) Thinking with feeling: Fostering productive thought in the classroom Routledge

Vygotsky, LS (1978) Mind in society: the development of higher psychological processes Harvard University Press Rose, DH & Meyer, A (2002) Teaching every student in the digital age: universal design for learning ASCD

Joanne Irving-Walton is a Principal Lecturer at Teesside University, working across learning and teaching and international partnerships. She is particularly interested in how academic judgement and professional identity develop through the emotional realities of higher education.


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Can folk pedagogies help us understand the limited impact of research on higher education?

by Alex Buckley

The SRHE conference is a great place to see our field in all its glory. From the sessions I attended in December 2025, one thing that was abundantly clear was the desire of so many HE researchers to change the world. A distinctive feature of contemporary HE research – reflecting the social sciences more broadly – is the focus on political and ethical issues, with avowedly political and ethical intentions. The improvement of society is often the explicit end, rather than the more humble improvement of our own part of the education system.

Despite this desire to make a difference, higher education research has for many years been held up as an area where the impact of those working in the field is not what it could be. As George Keller said in 1985, “hardly anyone in higher education pays attention to the research and scholarship about higher education”,

Asking the right questions?

There hasn’t been a lot of work on the gap between research and practice in HE – though there is a fair amount in the schools sector from which we can extrapolate, to a greater or lesser extent – but one issue that has received some attention is the fundamental one: are researchers actually asking the right questions?

Vivianne Robinson is a researcher who has laid a substantial amount of blame at the feet of researchers, who “have little to offer by way of alternative solutions, when the problems they have been studying are not those of the practitioner” (Robinson 1993). I have recently used Robinson’s model of Problem-Based Methodology to explore whether research about exams in higher education does engage sufficiently with the challenges that teachers take themselves to face. The results were not encouraging.

One of the more straightforward of Robinson’s criteria for impactful research is that researchers should be addressing teachers’ beliefs, and correcting them where they are erroneous. That’s important, but what if those beliefs are hard to shift? We all have stubborn hunches about how higher education works: good ways of motivating students, how to write feedback that will make students pay attention, how to clearly communicate complex ideas. What if there are teacher beliefs that are deeply embedded, so deeply that we don’t always know we have them, but that aren’t helping us and need to change?

One idea that has been explored in the school sector, but has largely passed us by, is the concept of ‘folk pedagogies’. This idea was developed in the 1990s as an extension of the more famous concept of ‘folk psychologies’: the tacit theories that we all have that allow us to make sense of people’s behaviour. For Jerome Bruner, a natural next step from folk psychologies was the idea that we have intuitive theories about how people learn.

“Watch any mother, any teacher, even any babysitter with a child and you’ll be struck by how much of what they do is steered by notions of ‘what children’s mind are like and how to help them learn,’ even though they may not be able to verbalise their pedagogical principles.” Bruner (1996)

There has been some research in the school sector about the implications of this idea, particularly in terms of how much difference research makes to educational practice. Folk pedagogies have two features that will make them a factor in the impact of education research: they interfere with the uptake of new research-based ideas and approaches, and they are stubborn. On the first point, the idea is that new ideas about higher education will have to replace the old if they are to influence teachers; and on the second, evidence suggests that even where trainee teachers have ostensibly internalised more scientific theories of learning, the folk pedagogies come creeping back.

In the case of higher education, what might these commonsense, intuitive theories look like? They might just be very general ideas about how people learn, applied to the particular context of higher education. Bruner identifies a range of broad folk pedagogical views, such as one which sees ‘children as knowers’, with a focus on the gathering and organising of facts. Perhaps one kind of folk psychology of higher education would be the application of that idea specifically to students in universities rather than other sectors: a focus on the selection, organisation and retention of propositional knowledge within degree programmes. Perhaps there are also specific intuitive theories about higher education that influence teachers’ practices. Perhaps there is a folk intuition that university students should not be spoon-fed – that they must take responsibility for their own learning and seek to develop their own views. Perhaps there is a folk intuition that students should encounter challenging views that encourage them to question their own certainties. In the absence of research, we can only speculate (and introspect).

Respecting the ‘folk’

The idea that teachers have deep intuitions about how students learn, that those intuitions can prevent them from acting on more evidence-based beliefs, and that those intuitions are hard to shake; none of those ideas are particularly earth-shattering. They are probably common sense among those researching and enhancing higher education. The value of the idea of ‘folk pedagogies’ lies instead in the way that it encourages us to take those intuitions seriously, both as an object of study and a powerful barrier to change.

Rather than dismissing intuitions about higher education – as ignorant beliefs and hide-bound traditions – we can study them. What are they? Where do they come from? How do they change? The idea of folk pedagogies is not pejorative. There’s no shame in having intuitions about how learning works. As with folk psychological theories, they are necessary parts of how we navigate the world, and something we can’t do without. There is also deep wisdom to be found in those intuitions, even if they are sometimes misleading. Research goes wrong by departing from common sense, at least as much as the other way around.

Acknowledging the existence of folk theories of higher education can help improve the impact of our research in all sorts of ways. We can research them, to understand why teachers and students (and others) do what they do, and the conditions in which deep intuitions can change. It can help us understand where – and why – research has departed so far from common sense as to be of little practical relevance.

It can also help us understand the scale of the challenge. In much of what we do, we’re seeking to modify what university teachers do, which very often means changing how they think. The reality is that we aren’t usually changing superficial, specific beliefs, at least not where the improvements we’re seeking are substantive. We’re changing deep beliefs picked up over a lifetime. Our model of improvement may then need to fit the old adage: if you’re not making progress at a snail’s pace, you’re not making progress. That’s a bit different from annual quality enhancement cycles or short-term strategic initiatives. We can change the world, but it will take time.

References

Bruner, J. (1996). The culture of education. Harvard

Robinson, V. M. J. (1993). Problem-Based Methodology: Research for the Improvement of Practice. Pergamon Press

Dr Alex Buckley is an Associate Professor in the Learning & Teaching Academy at Heriot-Watt University, Scotland. His research is focused on conceptual aspects of research and practice in assessment and feedback.


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What is a poem doing in a literature review?

by Nguyen Phuong Le, Kathleen Pithouse-Morgan and Thang Long Nguyen

If the phrase ‘write a poem’ makes your stomach do a tiny backflip, you are in good company. The three of us came to poetry from very different places. Kathleen has been working with poetry in teaching and research for many years, across different countries and contexts. Phuong first encountered poetic inquiry while working with Kathleen as a research assistant, learning her way into the field as a newcomer. Long joined as a critical reader of this blog, bringing curiosity from outside poetry‑based research.

Those different starting points matter. None of us came to this work believing poetry was an obvious or easy fit for literature reviewing.

In our conversations, workshops, and conference sessions, we have seen friends, postgraduate students, supervisors, lecturers, and experienced researchers worry that they are ‘not creative’. Some worry their English is not ‘good enough’. Others feel uneasy because poetry sounds personal, exposing, and even childish, in a higher education context.

Our starting point is simple: using a small, low-stakes poetic process to think with literature, stay engaged, and find your way into scholarly conversation. When you do this with another person, the process can feel even more doable. You cannot get this wrong, because the point is not to produce a ‘professional’ poem.

Why poetry in a literature review, seriously?

You don’t have to write poems to review literature. Most reviews are written in conventional academic prose. But if you are doing qualitative research, you may already know that knowledge is not only built through tidy argument. It is also built through attention, resonance, discomfort, contradiction, and voice.

Literature reviews can become a performance of mastery: you read fast, extract key points, categorise, critique, cite, and move on. Although these steps seem straightforward, the focus on moving quickly and efficiently may mean we miss what texts invite us to feel, picture, and connect with. The emotional texture of reading disappears, along with much of what makes qualitative work matter: empathy, imagination, and relational engagement.

Poetry calls for slower digestion. It invites you to ask, ‘What stays with me?’. It offers a way to respond before you feel ready to produce polished academic claims. That response can later feed your analytic writing, without needing to look like academic writing at the start.

What do we mean by “collaborative feedback poetry”?

Kathleen and Phuong’s article, ‘Reimagining qualitative literature reviewing through collaborative feedback poetry’ (Pithouse-Morgan & Le, 2025), introduces the term collaborative feedback poetry to describe a literature-reviewing strategy in which people respond to academic texts through short poems and exchange poetic responses with one another.

In such a strategy, collaboration matters. Many researchers struggle not only with the literature and writing, but also with the loneliness of the process. Working alongside someone else shifts the emotional climate. You are no longer trying to “prove” that you understand. You are noticing, articulating, and learning together.

Feedback matters as much as the poem. In academic settings, feedback often points out what is missing, what is weak, and what needs to be fixed. In collaborative feedback poetry, the focus is not on correction but on extension. The poem becomes a doorway, inviting you to walk further into the text rather than retreat from it.

“But I’m not a poet!”

That’s the point.

In the first few minutes of Kathleen’s collaborative feedback poetry sessions, the atmosphere is often tense. People apologise before they write. They say they are not creative, have never written a poem, or worry that their English is not good enough.

What changes things is permission: Permission to know, from the start, that there is no way to get this wrong.

Permission to be simple.

Permission to be incomplete.

Permission to use a home language.

When that permission feels real, participants begin to read, talk, and act differently. The literature starts to feel less like a wall and more like a space they can enter – through poetry, in whatever form it takes.

Phuong has seen these hesitations surface in conference conversations and informal chats with colleagues in Vietnam. After presentations on poetry as a literature‑reviewing practice, people are often interested but quiet. Later, they admit their worry about whether there is a ‘right’ kind of poem, or that writing poetry in a second or third language will expose them as less than capable.

That hesitancy matters. So instead of defending poetry in abstract terms, we slow down and walk through a small example.

Here is one example, a short haiku:

Creative Arts Professors’ Concerns

Pandemic’s harsh fall,

professors’ struggles echo,

incomplete sonnets.

(First published in Pithouse-Morgan & Le, 2025)

Phuong wrote this haiku in response to two papers by creative arts educators in higher education: Holmgren (2018) and Meskin and van der Walt (2022). Holmgren’s paper, written before the COVID-19 pandemic, explores musical interpretation through philosophic poetic inquiry and autoethnodrama. Meskin and van der Walt’s paper, written during the pandemic, uses poetic inquiry and reciprocal found poetry to reflect on disruptions to educator-artists’ academic and creative lives.

Rather than summarising either paper, Phuong read them together and asked: ‘What feeling carries across both texts?’ The answer was interruption – teaching and creative work that could not fully unfold. This is where ‘incomplete sonnets’ came from.

The poem does not replace the literature review. Instead, it marks what stayed with the reader after reading closely. This is not (just) an artistic move, but an act of attention and relation.

When we introduce this process, we usually ask a few simple questions, such as ‘What stayed with you after reading?’ ‘Which words carry that feeling?’ ‘What happens when you space those words out on a page?’ And ‘What occurs when another person reads and responds to your poem?’

When we introduce this process, we ask readers to notice what remains with them after reading. Kathleen’s poem Growing Beyond came from that noticing: reading across texts about doctoral students’ poetic inquiry (Chan, 2003; Kang et al, 2022) and attending to what stayed with her. In their poetry, Chan and Kang et al wrote about what it felt like to be doctoral students, including experiences of isolation, marginalisation, and internal struggle. Their work highlights the restorative, reflective, and critical possibilities of poetic inquiry in higher education. The poem opens with an impulse Kathleen recognised in their writing:

A sudden compulsion,

a yearning to express,

to write poetry.

                (First published in Pithouse-Morgan & Le, 2025)

Why the collaborative element carries weight

Higher education research can be intensely individualised. Even when we are part of a student cohort or a research centre, as students or academics, we often read and write alone before submitting work for evaluation or review. Collaborative feedback poetry encourages a different kind of scholarly space. The goal is not to show you are clever, but to practise staying with ideas and emotions in the supportive presence of another.

That matters for students and academics at different levels, and for supervisors and educators trying to teach literature reviewing without turning it into a fear-fest. It also matters for multilingual writers, who are too often made to feel that academic voice counts only when it sounds like confident English.

Collaboration does not remove difficulty; it changes what difficulty feels like. You are not stranded in it. You are accompanied. To us, this companionship feels more welcoming than working alone, not least because, like many of you, we are also trying to find and express our voices within the wider literature.

A takeaway for you

If you want to try this, keep it small. Choose one article. Give yourself ten minutes to jot down words that come to mind as you read, and select phrases from the text that grab your attention. Shape these into a short poem, in any form, with space around the words. Share it with someone you trust. Ask them to respond – not by grading it, but by writing back with their own short poem. Then briefly discuss what the poems say and why that matters.

If you leave with just one idea, let it be this: literature reviewing is not only about demonstrating coverage. It is also about cultivating relationships with ideas, voices, emotions, and sometimes with each other. Collaborative feedback poetry is one way to make these relationships visible and accessible.

By now, we hope you feel encouraged to step into poetic literature reviewing in ways that feel doable and enjoyable. With baby steps, of course.

Acknowledgement

This work was supported by the Leverhulme Trust through the British Academy/Leverhulme Small Research Grants Scheme. (Grant holder: Kathleen Pithouse-Morgan).

Nguyen Phuong Le is a lecturer in English Education at Hanoi National University of Education, Vietnam. She is a graduate of the Master of Arts in Digital Teaching and Learning at the University of Nottingham, UK, and the Bachelor of Arts in English at Northern Kentucky University, US. Passionate about digital education and literature, she has held various positions in research, teaching, and learning across higher education and educational organisations.

Kathleen Pithouse-Morgan is a Professor of Education at the University of Nottingham, UK, and an honorary professor at the University of KwaZulu-Natal, South Africa. She focuses on professional learning and supporting professionals as self-reflexive, creative learners. Passionate about arts-inspired research and teaching, especially using poetic methods, she co-convenes the British Educational Research Association’s Arts-Based Educational Research group.

Thang Long Nguyen is currently a student of the Master of Arts in Sociology at University College Dublin, Ireland. Graduated from Doshisha University in Japan with a Bachelor of Arts in Liberal Arts, he has an interdisciplinary interest in themes of nationalism. Still, he is deeply concerned with the progress of education in social sciences and humanities in his home country, Vietnam.