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Enhancing student learning through innovative scholarship

by Jenny Burnham, Matt Mears, Kennedy Offor, Jane Woodin and Tim Herrick

This blog is a report on the Enhancing Student Learning Through Innovative Scholarship (ESLTIS) conference held at the University of Sheffield in July 2026. The conference involved participants from 24 different universities and nearly 50 sessions.

When higher education faces real pressure, coming together to reimagine how we teach and learn is more than just professional development – it is a vital act of community. In July 2026, 97 educators, researchers, and student partners gathered at the University of Sheffield for two energising days at the Enhancing Student Learning Through Innovative Scholarship (ESLTIS) conference.

Across two inspiring keynotes, 39 talks and oral bites, and seven hands-on workshops, the atmosphere was filled with practical optimism, bold experimentation, and genuine connection. At a time when financial constraints weigh heavily across the sector, ESLTIS 2026 proved that an exceptional, high-impact academic gathering does not have to cost the earth. By keeping delegate fees as low as possible without compromising on quality, the conference demonstrated that transformative scholarship can remain accessible, inclusive, and deeply rewarding for everyone involved.

To capture the spirit of those two days, this blog brings together the key ideas, personal reflections, and practical insights that defined the event. We explore the overarching themes shaping the sector, followed by firsthand perspectives from our keynote speaker, insights from a session chair navigating AI in the curriculum, and the invaluable contributions of our student partners.

Key Conference Themes

With the tone of ‘Dealing with Disruption’ set by Professor Sam Nolan, the conference explored how learning and teaching in higher education is responding to and being shaped by the outside influences bringing both adversity and opportunity into the sector. A recurrent theme of innovations drawing on AI technology encompassed support for diverse student needs, its use in assessment, and as a teaching aid. The conference mirrored the acknowledgement of the evolving higher education demographic, with discussions covering inclusivity, decolonisation, engagement, identity, and self-worth, with structured gap-filling support, and breaking down of participation barriers, busting out of disciplinary silos to support students’ belonging and wellbeing. Wide-ranging talks covered: curriculum both hidden and carefully designed, strategies to augment students’ development of their skills and their readiness for their post-graduation world, and experiences of collaboration with and between students, staff, and professional services teams

Underpinning it all was the continuing ethos of the ESLTIS community that recognises and celebrates the education-focus of academia, exploring this with talks and workshops on academic identity – how it grows and develops, the things that shape it, and how it can be described and evidenced. The conference was not one thing for all delegates, it had something for everyone; areas with which we are familiar and aspects provoking thought and reflection, from which we could learn.  Yet again, I have attended a conference and come away with more than I expected. 

Reflections from a keynote speaker 

Dr Jenny Burnham is a Senior University Teacher in Chemistry at the University of Sheffield. Here she writes about her career and how she shared her experience at the conference.

I am interested in everything and my career highlights have been similarly eclectic. Rather than focus on a single speciality, I used myself as the common thread to weave a story that would be useful, relevant, or inspirational for a diverse audience of enthusiastic and accomplished educators. Taking inspiration from the conference name and conference themes (academic identity, community in learning and teaching, SoTL, supporting student success, and learning and teaching possibilities), my keynote made a virtue of the meanders of my academic career so far.  

My academic journey started in chemistry, shaping my relationship with information (evidence is key) and placing the primacy of experimental investigation at the centre of my disciplinary awareness. My early scholarship interests grew from this, exploring the development of inquiry skills through laboratory education. Spending a lot of time teaching students in the lab and enjoying their individuality shaped a desire to see them all succeed. This developed into an education philosophy that students are clever and capable with enormous potential, which permeates my work. An MEd drawing on what can be learned from sharing practice and insight (Rowland, 1999) transformed my approach to education. The benefits I felt in sharing my work with people who were interested motivated me to extend my networks. These have been a source of inspiration, opportunity and support, and allowed me to define my leadership identity; supporting, facilitating, promoting, and enabling collegial interaction and growth within my community.

My keynote aimed to provide a little of something for everyone, and evidence that I succeeded in this is captured in three different post-keynote interactions. I was thanked for owning my nerves: “it helps” she said. I had lunch with a PhD chemist considering an academic career; and I received an enquiry about a Learning Landscapes initiatives I worked on. There was something for me too: a final, unexpected result of my talk was that pulling it together reminded me of the cool stuff I have done.  Celebrating successes is definitely something I intend to do more of.  

Reflections from a session chair 

Dr Kennedy Offor was a member of the local organising committee and reflects on his experiences at ESLTIS in the role of session chair.

Chairing the ‘AI in the Curriculum’ session gave me with a unique perspective on how colleagues from different disciplines are responding to the rapid emergence of generative AI in higher education. Although the eight presentations ranged from institutional policy to engineering, mathematics, business, humanities, and language learning, a common thread emerged throughout the session – how learning, assessment, and curriculum design should evolve to ensure that students continue to develop critical judgement, disciplinary understanding, and professional responsibility. The discussion reflected a noticeable shift from reacting to AI as a disruptive technology towards designing learning environments that make purposeful and educationally meaningful use of it.

Laurie Wilson opened the session by introducing the University of Sheffield’s developing ‘Common Approach to AI in the Curriculum’, emphasising “harmonisation rather than homogenisation” and arguing that AI should support good disciplinary teaching rather than drive it. Joanne Irving-Walton then offered a thought-provoking perspective by examining the “gaps” where judgement develops, suggesting that AI increasingly participates in the uncertainty, rehearsal and reflection through which students and educators learn. 

Panos Kloukinas, Nick McCullen and Will Roberts built on this by arguing that mathematics and computation education should increasingly prioritise application, interpretation and problem formulation over procedural execution, while Dawn Whitton demonstrated how students could critically evaluate AI itself through a staged business assessment that required them not only to use AI, but also to critique its assumptions, biases and wider societal implications.

The engineering perspective was represented through our own presentation, which used longitudinal Graduate Teaching Assistant reflections to show how observations of student uncertainty, teamwork and AI use informed iterative redesign of an MSc engineering design module. Mireilla Bikanga Ada’s graduate study provided an interesting counterpoint by showing that, despite widespread AI adoption, teamwork remained the strongest predictor of graduates’ perceptions of degree relevance. 

Rachel Johnson illustrated how a simple, low-stakes classroom activity could encourage students critically to examine AI-generated information and its biases, while Franziska Collier demonstrated how AI avatars could provide authentic, low-risk opportunities for students to practise high-stakes language assessments.

The discussions reinforced my own view that the key educational question is no longer whether students should use AI, but how curricula can help them develop the professional judgement needed to use it responsibly. Rather than diminishing the role of educators, the session demonstrated that AI makes thoughtful curriculum design, assessment, and disciplinary expertise more important than ever, and highlighted an encouraging direction of travel for the Scholarship of Teaching and Learning community.

Students as Partners 

Dr. Jane Woodin was a member of the local organising committee and offers her thoughts on working with students as volunteer partners 

The conference ethos of inclusive dialogue was also evident in the organisation of student volunteers who were invited to participate in whatever they wished, from visiting and advising on the venue beforehand, supporting registration and general enquiries, and co-chairing sessions together with a staff volunteer. Responses from the eight volunteers ranged from tentative interest in helping out to requests to ‘have a go at everything’, and others who did the minimum in terms of active volunteering because they found the topics of the talk so interesting and important.

It was heartening to hear one volunteer offer to sit at the front of the large auditorium to oversee a presentation session, handling the Q &A masterfully, including interventions from the co-chair. Another volunteer started out shyly offering to help at the registration desk, but seeing fellow students taking the chairing role, threw herself into it enthusiastically. 

These experiences remind us how the processes surrounding education are as important as the education itself – or perhaps are or should be inseparable, as the ‘way that you do it’ melds seamlessly into ‘what you do’; offering an example of SoTL in action at its best.  

Final Reflections

Dr Matt Mears is a Senior University Teacher in Physics at the University of Sheffield, and was chair of the Local Organising Committee for ESLTIS2026

As Conference Chair, reflecting on the energy across those two days fills me with immense pride and a distinct sense of gratitude. While hosting an event of this scale undoubtedly takes work, the dividend it pays back to the host team and the wider community is extraordinary. Watching colleagues reconnect, draw genuine inspiration from such a rich variety of presenters, and spark new collaborations in real time reminded me why these gatherings are so vital for our sector.

If you have ever toyed with the idea of organising a conference, or perhaps bringing ESLTIS to your own institution, I cannot encourage you enough to take the leap. The effort involved is far outweighed by the connection, camaraderie, and fresh momentum it generates. Whether as an attender, a presenter, or a future host, I look forward to seeing where our community goes next.


Dr Jenny Burnham is a chemistry university teaching specialist with 20 years’ experience teaching and learning about education at the University of Sheffield. 

Dr Matt Mears is a Senior University Teacher in Physics in the School of Mathematical and Physical Sciences at the University of Sheffield. His research focuses on inclusive education practices and laboratory-based teaching.

Dr Kennedy Offor is an engineering educator and researcher with experience teaching undergraduate and postgraduate engineering at the University of Sheffield’s School of Electrical and Electronic Engineering and, more recently, apprenticeship and technical engineering education at the AMRC Training Centre.  

Dr. Jane Woodin is a Senior University Teacher in the School of Languages, Arts and Societies at the University of Sheffield. She has taught and researched in intercultural communication, focusing on peer interaction and dialogue approaches.

Dr Tim Herrick is a Senior University Teacher in the School of Education, and a National Teaching Fellow. He was part of the ESLTIS conference committee and helped co-ordinate this blogpost.

Reference Rowland, S (1999) ‘The role of theory in a pedagogical model for lecturers in higher education’ Studies in Higher Education, 24(3), 303–314. https://doi.org/10.1080/03075079912331379915


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When assessment goes local, who keeps what students bring back?

by Carlene Kyeremeh

A student sent me a photograph of a handwritten waybill. Dated November 2022, it records a unit price for printed sachet film. It also carries the names of a supplier and a customer and the given name of the person who issued it. The waybill appeared in a term paper on fiscal consolidation as evidence of a cost increase that no statistical series I have found records.

It is in my files. Not the department’s. Mine.

I describe the waybill here but do not reproduce it, identify those named, or give the figure it records. The students who wrote the paper agreed to this account. I have no record that the supplier, the customer or the person named agreed to the document’s retention or wider use, so I have minimised the identifying detail, including the price, which is the most commercially sensitive thing on the page.

I have argued elsewhere that the curriculum can help to thicken a thin documentary record. Ask each student to obtain one item of locally sourced evidence: an interview, a quoted price, a document from a firm or public office, and, over successive cohorts, a course can assemble material that no database previously held. This is no longer only my ambition. Programmes are already redesigning assessment in response to generative AI, and one attractive move is to require evidence a model cannot retrieve: a price obtained in a market, an interview conducted locally, a document held by a firm or public office. That redesign brings third-party material into course files at scale, often without a parallel decision about what should happen to it after assessment.

That is the promise. The waybill in my files is the problem that follows it.

What universities already know how to govern

Research ethics and commercial confidentiality are not routinely ignored in student work. Professionally oriented programmes have long had to decide what an organisation may disclose, who may see it and when it must be returned. For example, McMaster University’s course-based research guidance explicitly covers undergraduate, MBA and graduate courses. The University of Toronto provides both a course-based ethics route and a classroom confidentiality agreement for third-party proprietary information. In the UK, the University of Manchester’s ethics guidance asks researchers to establish first whether an activity is research training conducted as coursework, described as collecting and analysing a small amount of data to gain experience in method, or research proper. These arrangements show that collection, consent, confidentiality and use within a defined project can be governed. My argument is therefore not that universities lack such protections. It begins where a normal project would end.

The problem begins when the course ends

If material is returned, destroyed or retained only with the assessed work, its lifecycle has a clear end. But the design I am proposing depends on the evidence outliving the assignment. A document collected by one student in 2026 should be available to another cohort in 2028 or 2029, alongside prices, interviews and documents gathered in the intervening years. The course is not only assessing students. It is building a local source collection.

That changes the questions. What exactly does the university hold after the assessment period? Who is its custodian? How long may it be retained? May another cohort consult it? What fresh permission is needed if it is later used for research or publication? These procedures can help answer those questions, but not automatically. An agreement that properly confines information to one course may rule out the very reuse on which the proposed archive depends.

The evidence is not the assignment

The student’s analysis is coursework. The material used to produce it is not necessarily the same thing. A waybill may contain personal or commercially sensitive information. Interview notes are harder still: they are neither the source’s own record nor the student’s analysis, but one person’s written account of what another person said, made for an assignment and now held by a lecturer.

Where the activity is classified as teaching rather than research, this third-party material can still raise a lifecycle question: do institutional rules cover its custody, retention and reuse after the assessment for which it was collected? The Manchester distinction is instructive here. A course collection built to outlive its cohorts is not a small amount of data gathered to practise a method, but neither does it fit neatly within research as that definition intends. It is a lifecycle problem created by a particular curricular ambition.

The reuse is the point

Ghana’s Data Protection Act, 2012 (Act 843) requires personal data to be collected for a specific, explicitly defined and lawful purpose, limits retention and requires further processing to be compatible with the original purpose. Material obtained for one student’s term paper and later made available to another cohort may involve further processing. Nothing in the waybill I received establishes permission for it to become part of a standing course collection.

The Act contains provisions for historical, statistical and research uses where the relevant conditions are met. Those provisions offer a governed route, not a retrospective blank cheque. They do not turn a lecturer’s folder into an institutional archive. The underlying principle is not unique to Ghana: UK guidance likewise treats purpose, storage and research-related reuse as connected but distinct questions.

Ordinary assignment governance is adequate for ordinary assignments. The problem appears when coursework is asked to do something additional: to enlarge a locally available documentary record over time.

The archival value and the governance risk arise from the same feature. An archive that cannot be consulted next year is not an archive. A collection that can be consulted next year without the source having been told is not a defensible one.

A small institutional answer

The answer need not be a new bureaucracy. It could begin with a short institutional instrument that separates use in a student’s assessment from retention in a course source collection. Before collection begins, it should state the teaching purpose, what may be collected, who may later consult it, how long it will be kept, which unit is responsible and what choices the source has. Research use or publication should require a separate decision rather than being hidden inside the phrase ‘educational purposes’.

The remaining requirement is custody. What one lecturer can require, only an institution can keep. A named custodian, controlled access, a retention and disposal schedule, and a record of what the course has produced would convert a personal folder into an accountable institutional practice. Existing MBA, ethics and confidentiality processes provide models; the task is to extend their discipline to the intended afterlife of the evidence.

Proportionality matters. If the process becomes heavier than the assessment it supports, colleagues may stop asking students to gather local evidence. But a department unable to provide a clear collection notice, a retention rule and somebody accountable for what returns should not require the fieldwork. Otherwise, institutional risk is transferred to students and to the businesses that agree to speak with them.

Readers can test this in ten minutes. Take your own institution’s course-based ethics route or classroom confidentiality agreement and look for two things: whether either says anything about third-party material after the assessment period, and who is named as responsible for it. If neither appears, ask where else the institution governs the evidence after the assignment ends. If there is no answer, the arrangements may govern the assignment without governing its evidentiary afterlife.

Before we ask for more

The curriculum can enlarge the archive. The qualification is that the archival ambition creates an additional governance obligation. The question is not only whether the evidence was collected ethically and kept confidentially. It is whether its intended custody, retention and reuse were made explicit before collection began.

I have a waybill in my files and no good institutional answer to those questions. That is not an argument against asking students to go and find out. It is an argument for deciding what we will do with what comes back, before we ask them to go.

Dr Carlene Kyeremeh is Associate Professor and Vice President, University Advancement, Recruitment and Research at All Nations University, Koforidua, Ghana. Her research addresses curriculum decolonisation, higher education policy in Africa and the Caribbean, and the political economy of educational technology.This post develops a governance question raised in Issues 14 and 15 of The Decolonized Curriculum, her LinkedIn newsletter.


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Learning spaces, educational technology and the future of the physical learning environment

by James Rutherford

To look into the near future, it would be sensible to start by avoiding this typical question: ‘What technology do we need to install in these rooms?’ We would be wise to ask instead: ‘What do we want our education to look like?’ Future learning spaces and associated educational technologies require an approach that supports teaching and student collaboration, with accessibility features and environmental adaptations embedded within the design of learning spaces rather than delivered through standalone audio-visual equipment.

Research on learning spaces highlights the importance of aligning technology decisions with pedagogy and intended learning activities, rather than allowing the space and in-class technology to dictate educational practice. (Radcliffe et al, 2008) This principle resonates strongly with my experience working in HE learning environments, where the most successful spaces are those that enable effective communication and participation for all learners. The technical and physical space itself must therefore support diverse forms of engagement, ensuring that staff can teach effectively and that students can participate fully through listening, discussion, collaboration, practice and observation. (Jamieson, 2003)

Audiovisual technology

At City St George’s, University of London, the educational technology allows staff to enter any learning space, connect a laptop, teach, collaborate, record or stream sessions with minimal effort. This has largely been achieved through the learning spaces project (Designing Active Learning Initiative (DALI), 2026) and subsequent upgrade work, with room controls and user experiences standardised across the institution to reduce complexity and support requirements.

Design for learning and teaching

However, this can mean that technical decisions are made before the educational intent has been planned and shared with all stakeholders. It is advisable that educational technology supports the intended teaching activities rather than determines them, with the educational strategy and signature pedagogy of the university driving the learning environment design (Rudd, 2006; Hasper, 2024). Spaces ought to be designed around the learning and teaching needs of an institution, making it straightforward for staff to teach effectively and for students to engage successfully.

Environmental prerequisites

In many learning environments, audio quality often receives less attention than display technology, but it is obvious that students need to hear clearly, be able to participate in discussions and access lecture capture recordings. It is increasingly recognised as a key contributor to students’ learning outcomes, and there is evidence that speech intelligibility and the control of background noise are fundamental requirements for effective learning. This is because the design of each space directly influences effective communication. (Acoustic Design for Schools, DFE, 2015) This includes unwanted noise from corridors, neighbouring rooms, ventilation and external sources, as extraneous noise can be a real hindrance to learning and teaching capabilities.

Therefore, acoustic treatment needs to be considered as fundamental, now and in the future, not least with accessibility at the forefront of our minds. Universities can often improve existing spaces through acoustic treatment and interventions designed to reduce reverberation, although the feasibility and cost will vary by building.

Controllable lighting, appropriately designed and ergonomic furniture, thermal comfort and excellent sightlines all contribute to an effective learning experience. Classroom furniture is now expected to be comfortable, robust and appropriate for the type of teaching taking place. Spaces need to accommodate different body sizes, the requirements of student movement, with diversity and accessibility alongside different timetabled patterns of use and perhaps involving longer teaching sessions.

Accessibility is fundamental

Future learning spaces will need to be designed with diverse learners in mind, consistent with the principles of Universal Design for Learning (Universal Design for Learning｜CAST, 2026). Spaces are required to include captioning, recordings, remote participation and flexible methods of seating and student engagement. Across HE, accessibility is increasingly being incorporated into space planning from the outset through features such as captions, accessible controls, suitable furniture for students with access needs, clear audio and barrier-free access. Real inclusion is developing into a measure of quality rather than an afterthought.

Design for hybrid delivery

Hybrid delivery could be seamlessly integrated into learning space design while ensuring that the on-campus experience remains the priority. Spaces need to support clear sound and appropriately intelligent camera angles. Video conferencing tools permit content sharing, recording and online participation as standard, but hybrid teaching must not feel like an add-on; rather, it should be a flexible and inclusive addition to the learning approach (Beatty, 2007; Melcher et al, 2025). In hybrid spaces, students need to hear the lecturer, hear questions from the room, follow discussions and revisit recordings afterwards.Consequently, excellent microphones, clear loudspeakers, robust voice support, as well as captions and assistive listening are expected to be core requirements.

AI tools

AI is likely to support areas such as transcription, captioning, camera tracking controls, fault detection and remote monitoring for the service teams. Its role would be to improve accessibility, as well as provide data analysis of how the space is performing environmentally and reduce workload rather than add complexity. Such developments must, however, consider how AI tools relate to privacy, ethics, and data governance, which will remain important factors in their implementation.

Support and maintenance

Learning spaces will become easier to monitor, maintain and update through centralised management tools that do exist, but with emerging AI capabilities that could enhance these processes through predictive maintenance, intelligent system monitoring, automated troubleshooting, and data-driven insights. Support teams would then be able to identify and resolve many issues remotely, improving reliability and assisting with planning of new environments.

The transformation of accessibility and inclusion

Accessibility is no longer regarded purely as a matter of compliance, but as an essential component of the learning environment. Future learning spaces ought to be planned and designed on the basis that:

  • some students will need captions
  • some students will rely heavily on lecture capture recordings
  • some students will be neurodivergent
  • some students will join remotely
  • some students will be working in noisy or shared environments
  • some students will need language support

This is key because it changes accessibility from a ‘special provision’ into part of the normal learning experience, in particular the essential role that lecture recordings play for neurodivergent and disabled students (Horlin, Hronska and Nordmann, 2024).

The AI-supported room

AI technology is likely to be adopted within learning spaces with support for:

  • automatic camera framing
  • lecturer tracking
  • capture of student questions
  • reduction of background noise in lecture capture
  • live transcription
  • searchable lecture capture recordings
  • automatic chapters in those recordings
  • summaries of sessions
  • early warning of equipment faults
  • better support information for technical teams
  • information about how rooms are being used

There are obviously numerous risks with adopting AI: too much monitoring; weak governance; data protection and privacy concerns; intellectual property questions; and technology that looks clever but does not help teaching. However, as two recent literature reviews illustrate, the opportunity that AI tools can bring is undoubtedly significant, with the potential for less pressure on academics to operate the technology in a classroom and by offering better access for students (Crompton and Burke, 2023; Adamakis and Rachiotis, 2025).

Conclusion

If the last twenty years were defined by the digitisation of teaching, the next decade could be defined by the redesign of learning itself. Will higher education across the UK embrace this opportunity? Can it afford not to in the current climate of financial uncertainty and geopolitical vulnerability? Accessibility, hybrid participation, intelligent systems and engaging learning environments can no longer be regarded as enhancements, as they will surely become the standard to meet the expectations of students and staff. The institutions that thrive will not be those with the most technology, but those that use it most purposefully. The future of learning spaces is not about what technology we install; it is about what we enable.

James Rutherford is a Senior Educational Technologist at City St George’s, University of London, specialising in learning spaces, educational technology, and hybrid delivery. With over 35 years’ experience in higher education, he has worked as a learning space designer, AV project manager, and video producer, leading initiatives that support innovative and inclusive learning and teaching. James holds a Master’s in Advanced Educational Practice from the UCL Institute of Education and is a Fellow of Advance HE.


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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).