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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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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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Teaching students to use AI: from digital competence to a learning outcome

by Concepción González García and Nina Pallarés Cerdà

Debates about generative AI in higher education often start from the same assumption: students need a certain level of digital competence before they can use AI productively. Those who already know how to search, filter and evaluate online information are seen as the ones most likely to benefit from tools such as ChatGPT, while others risk being left further behind.

Recent studies reinforce this view. Students with stronger digital skills in areas like problem‑solving and digital ethics tend to use generative AI more frequently (Caner‑Yıldırım, 2025). In parallel, work using frameworks such as DigComp has mostly focused on measuring gaps in students’ digital skills – often showing that perceived “digital natives” are less uniformly proficient than we might think (Lucas et al, 2022). What we know much less about is the reverse relationship: can carefully designed uses of AI actually develop students’ digital competences – and for whom?

In a recent article, we addressed this question empirically by analysing the impact of a generative AI intervention on university students’ digital competences (García & Pallarés, 2026). Students’ skills were assessed using the European DigComp 2.2 framework (Vuorikari et al, 2022).

Moving beyond static measures of digital competence

Research on students’ digital competences in higher education has expanded rapidly over the past decade. Yet much of this work still treats digital competence as a stable attribute that students bring with them into university, rather than as a dynamic and educable capability that can be shaped through instructional design. The consequence is a field dominated by one-off assessments, surveys and diagnostic tools that map students’ existing skills but tell us little about how those skills develop.

This predominant focus on measurement rather than development has produced a conceptual blind spot: we know far more about how digital competences predict students’ use of emerging technologies than about how educational uses of these technologies might enhance those competences in the first place.

Recent studies reinforce this asymmetry. Students with higher levels of digital competence are more likely to engage with generative AI tools and to display positive attitudes towards their use (Moravec et al, 2024; Saklaki & Gardikiotis, 2024). In this ‘competence-first’ model, digital competence appears as a precondition for productive engagement with AI. Yet this framing obscures a crucial pedagogical question: might AI, when intentionally embedded in learning activities, actually support the growth of the very competences it is presumed to require?

A second limitation compounds this problem: the absence of a standardised framework for analysing and comparing the effects of AI-based interventions on digital competence development. Although DigComp is widely used for diagnostic purposes, few studies employ it systematically to evaluate learning gains or to map changes across specific competence areas. As a result, evidence from different interventions remains fragmented, making it difficult to identify which aspects of digital competence are most responsive to AI-mediated learning.

There is, nevertheless, emerging evidence that AI can do more than simply ‘consume’ digital competence. Studies by Dalgıç et al (2024) and Naamati-Schneider & Alt (2024) suggest that integrating tools such as ChatGPT into structured learning tasks can stimulate information search, analytical reasoning and critical evaluation—provided that students are guided to question and verify AI outputs rather than accept them uncritically. Yet these contributions remain exploratory. We still lack experimental or quasi-experimental evidence that links AI-based instructional designs to measurable improvements in specific DigComp areas, and we know little about whether such benefits accrue equally to all students or disproportionately to those who already possess stronger digital skills.

This gap matters. If digital competences are conceived as malleable rather than fixed, then AI is not merely a technology that demands certain skills but a pedagogical tool through which those skills can be cultivated. This reframing shifts the centre of the debate: away from asking whether students are ready for AI, and towards asking whether our teaching practices are ready to use AI in ways that promote competence development and reduce inequalities in learning.

Our study: teaching students to work with AI, not around it

We designed a randomised controlled trial with 169 undergraduate students enrolled in a Microeconomics course. Students were allocated by class group to either a treatment or a control condition. All students followed the same curriculum and completed the same online quizzes through the institutional virtual campus.

The crucial difference lay in how generative AI was integrated:

  • In the treatment condition, students received an initial workshop on using large language models strategically. They practised:
  • contextualising questions
  • breaking problems into steps
  • iteratively refining prompts
  • and checking their own solutions before turning to the AI.
  • Throughout the course, their online self-assessments included adaptive feedback: instead of simply marking answers as right or wrong, the system offered hints, step-by-step prompts and suggestions on how to use AI tools as a thinking partner.
  • In the control condition, students completed the same quizzes with standard right/wrong feedback, and no training or guidance on AI.

Importantly, the intervention did not encourage students to outsource solutions to AI. Rather, it framed AI as an interactive study partner to support self-explanation, comparison of strategies and self-regulation in problem solving.

We administered pre- and post-course questionnaires aligned with DigComp 2.2, focusing on five competences: information and data literacy, communication and collaboration, safety, and two aspects of problem solving (functional use of digital tools and metacognitive self-regulation). Using a difference-in-differences model with individual fixed effects, we estimated how the probability of reporting the highest level of each competence changed over time for the treatment group relative to the control group.

What changed when AI was taught and used in this way?

At the overall sample level, we found statistically significant improvements in three areas:

  • Information and data literacy – students in the AI-training condition were around 15 percentage points more likely to report the highest level of competence in identifying information needs and carrying out effective digital searches.
  • Problem solving – functional dimension – the probability of reporting the top level in using digital tools (including AI) to solve tasks increased by about 24 percentage points.
  • Problem solving – metacognitive dimension – a similar 24-point gain emerged for recognising what aspects of one’s digital competences need to be updated or improved.

In other words, the AI-integrated teaching design was associated not only with better use of digital tools, but also with stronger awareness of digital strengths and weaknesses – a key ingredient of autonomous learning. Communication and safety competences also showed positive but smaller and more uncertain effects. Here, the pattern becomes clearer when we look at who benefited most.

A compensatory effect: AI as a potential leveller, not just an amplifier

When we distinguished students by their initial level of digital competence, a pattern emerged. For those starting below the median, the intervention produced large and significant gains in all five competences, with improvements between 18 and 38 percentage points depending on the area. For students starting above the median, effects were smaller and, in some cases, non-significant.

This suggests a compensatory effect: students who began the course with weaker digital competences benefited the most from the AI-based teaching design. Rather than widening the digital gap, guided use of AI acted as a levelling mechanism, bringing lower-competence students closer to their more digitally confident peers.

Conceptually, this challenges an implicit assumption in much of the literature – namely, that generative AI will primarily enhance the learning of already advantaged students, because they are the ones with the skills and confidence to exploit it. Our findings show that, when AI is embedded within intentional pedagogy, explicit training and structured feedback, the opposite can happen: those who started with fewer resources can gain the most.

From ‘allow or ban’ to ‘how do we teach with AI?’

For higher education policy and practice, the implications are twofold.

First, we need to stop thinking of digital competence purely as a prerequisite for using AI. Under the right design conditions, AI can be a pedagogical resource to build those competences, especially in information literacy, problem solving and metacognitive self-regulation. That means integrating AI into curricula not as an add-on, but as part of how we teach students to plan, monitor and evaluate their learning.

Second, our results suggest that universities concerned with equity and digital inclusion should focus less on whether students have access to AI tools (many already do) and more on who receives support to learn how to use them well. Providing structured opportunities to practise prompting, to critique AI outputs and to reflect on one’s own digital skills may be particularly valuable for students who enter university with lower levels of digital confidence.

This does not resolve all the ethical and practical concerns around generative AI – far from it. But it shifts the conversation. Instead of treating AI as an external threat to academic integrity that must be tightly controlled, we can start to ask:

  • How can we design tasks where the added value lies in asking good questions, justifying decisions and evaluating evidence, rather than in producing a single ‘correct’ answer?
  • How can we support students to see AI not as a shortcut to avoid thinking, but as a tool to think better and know themselves better as learners?
  • Under what conditions does AI genuinely help to close digital competence gaps, and when might it risk opening new ones?

Answering these questions will require further longitudinal and multi-institutional research, including replication studies and objective performance measures alongside self-reports. Yet the evidence we present offers a cautiously optimistic message: teaching students how to use AI can be part of a strategy to strengthen digital competences and reduce inequalities in higher education, rather than merely another driver of stratification.

Concepción González García is Assistant Professor of Economics at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain, and holds a PhD in Economics from the University of Alicante. Her research interests include macroeconomics, particularly fiscal policy, and education.

Nina Pallarés is Assistant Professor of Economics and Academic Coordinator of the Master’s in Management of Sports Entities at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain. Her research focuses on applied econometrics, with particular emphasis on health, labour, education, and family economics.


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Widely used but barely trusted: understanding student perceptions on the use of generative AI in higher education

by Carmen Cabrera and Ruth Neville

Generative artificial intelligence (GAI) tools are rapidly transforming how university students learn, create and engage with knowledge. Powered by techniques such as neural network algorithms, these tools generate new content, including text, tables, computer code, images, audio and video, by learning patterns from existing data. The outputs are usually characterised by their close resemblance to human-generated content. While GAI shows great promise to improve the learning experience in various disciplines, its growing uptake also raises concerns about misuse, over-reliance and more generally, its impact on the learning process. In response, multiple UK HE institutions have issued guidance outlining acceptable use and warning against breaches of academic integrity. However, discussions about the role of GAI in the HE learning process have been led mostly by educators and institutions, and less attention has been given to how students perceive and use GAI.

Our recent study, published in Perspectives: Policy and Practice in Higher Education, helps to address this gap by bringing student perspectives into the discussion. Drawing on a survey conducted in early 2024 with 132 undergraduate students from six UK universities, the study reveals an impactful paradox. Students are using GAI tools widely, and expect their use to increase, yet fewer than 25% regard its outputs as reliable. High levels of use therefore coexist with low levels of trust.

Using GAI without trusting it

At first glance, the widespread use of GAI among students might be taken as a sign of growing confidence in these tools. Yet, when students are asked about their perceptions on the reliability of GAI outputs, many express disagreement when asked if GAI could be considered a reliable source of knowledge. This apparent contradiction raises the question of why are students still using tools they do not fully trust? The answer lies in the convenience of GAI. Students are not necessarily using GAI because they believe it is accurate. They are using it because it is fast, accessible and can help them get started or work more efficiently. Our study suggests that perceived usefulness may be outweighing the students’ scepticism towards the reliability of outputs, as this scepticism does not seem to be slowing adoption. Nearly all student groups surveyed reported that they expect to continue using generative AI in the future, indicating that low levels of trust are unlikely to deter ongoing or increased use.

Not all perceptions are equal

While the “high use – low trust” paradox is evident across student groups, the study also reveals systematic differences in the adoption and perceptions of GAI by gender and by domicile status (UK v international students). Male and international students tend to report higher levels of both past and anticipated future use of GAI tools, and more permissive attitudes towards AI-assisted learning compared to female and UK-domiciled students. These differences should not necessarily be interpreted as evidence that some students are more ethical, critical or technologically literate than others. What we are likely seeing are responses to different pressures and contexts shaping how students engage with these tools. Particularly for international students, GAI can help navigate language barriers or unfamiliar academic conventions. In those circumstances, GAI may work as a form of academic support rather than a shortcut. Meanwhile, differences in attitudes by gender reflect wider patterns often observed on academic integrity and risk-taking, where female students often report greater concern about following rules and avoiding sanctions. These findings suggest that students’ engagement with GAI is influenced by their positionality within Higher Education, and not just by their individual attitudes.

Different interpretations of institutional guidance

Discrepancies by gender and domicile status go beyond patterns of use and trust, extending to how students interpret institutional guidance on generative AI. Most UK universities now publish policies outlining acceptable and unacceptable uses of GAI in relation to assessment and academic integrity, and typically present these rules as applying uniformly to all students. In practice, as evidenced by our study, students interpret these guidelines differently. UK-domiciled students, especially women, tend to adopt more cautious readings, sometimes treating permitted uses, such as using GAI for initial research or topic overviews, as potential misconduct. International students, by contrast, are more likely to express permissive or uncertain views, even in relation to practices that are more clearly prohibited. Shared rules do not guarantee shared understanding, especially if guidance is ambiguous or unevenly communicated. GAI is evolving faster than University policy, so addressing this unevenness in understanding is an urgent challenge for higher education.

Where does the ‘problem’ lie?

Students are navigating rapidly evolving technologies within assessment frameworks that were not designed with GAI in mind. At the same time, they are responding to institutional guidance that is frequently high-level, unevenly communicated and difficult to translate into everyday academic practice. Yet there is a tendency to treat GAI misuse as a problem stemming from individual student behaviour. Our findings point instead to structural and systemic issues shaping how students engage with these tools. From this perspective, variation in student behaviour could reflect the uneven inclusivity of current institutional guidelines. Even when policies are identical for all, the evidence indicates that they are not experienced in the same way across student groups, calling for a need to promote fairness and reduce differential risk at the institutional level.

These findings also have clear implications for assessment and teaching. Since students are already using GAI widely, assessment design needs to avoid reactive attempts to exclude GAI. A more effective and equitable approach may involve acknowledging GAI use where appropriate, supporting students to engage with it critically and designing learning activities that continue to cultivate critical thinking, judgement and communication skills. In some cases, this may also mean emphasising in-person, discussion-based or applied forms of assessment where GAI offers limited advantage. Equally, digital literacy initiatives need to go beyond technical competence. Students require clearer and more concrete examples of what constitutes acceptable and unacceptable use of GAI in specific assessment contexts, as well as opportunities to discuss why these boundaries exist. Without this, institutions risk creating environments in which some students become too cautious in using GAI, while others cross lines they do not fully understand.

More broadly, policymakers and institutional leaders should avoid assuming a single student response to GAI. As this study shows, engagement with these tools is shaped by gender, educational background, language and structural pressures. Treating the student body as homogeneous risks reinforcing existing inequalities rather than addressing them. Public debate about GAI in HE frequently swings between optimism and alarm. This research points to a more grounded reality where students are not blindly trusting AI, but their use of it is increasing, sometimes pragmatically, sometimes under pressure. As GAI systems continue evolving, understanding how students navigate these tools in practice is essential to developing policies, assessments and teaching approaches that are both effective and fair.

You can find more information in our full research paper: https://www.tandfonline.com/doi/full/10.1080/13603108.2025.2595453

Dr Carmen Cabrera is a Lecturer in Geographic Data Science at the Geographic Data Science Lab, within the University of Liverpool’s Department of Geography and Planning. Her areas of expertise are geographic data science, human mobility, network analysis and mathematical modelling. Carmen’s research focuses on developing quantitative frameworks to model and predict human mobility patterns across spatiotemporal scales and population groups, ranging from intraurban commutes to migratory movements. She is particularly interested in establishing methodologies to facilitate the efficient and reliable use of new forms of digital trace data in the study of human movement. Prior to her position as a Lecturer, Carmen completed a BSc and MSc in Physics and Applied Mathematics, specialising in Network Analysis. She then did a PhD at University College London (UCL), focussing on the development of mathematical models of social behaviours in urban areas, against the theoretical backdrop of agglomeration economies. After graduating from her PhD in 2021, she was a Research Fellow in Urban Mobility at the Centre for Advanced Spatial Analysis (CASA), at UCL, where she currently holds a honorary position.

Dr Ruth Neville is a Research Fellow at the Centre for Advanced Spatial Analysis (CASA), UCL, working at the intersection of Spatial Data Science, Population Geography and Demography. Her PhD research considers the driving forces behind international student mobility into the UK, the susceptibility of student applications to external shocks, and forecasting future trends in applications using machine learning. Ruth has also worked on projects related to human mobility in Latin America during the COVID-19 pandemic, the relationship between internal displacement and climate change in the East and Horn of Africa, and displacement of Ukrainian refugees. She has a background in Political Science, Economics and Philosophy, with a particular interest in electoral behaviour.


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How educators can use Gen AI to promote inclusion and widen access

by Eleni Meletiadou

Introduction

Higher education faces a pivotal moment as Generative AI becomes increasingly embedded within academic practice. While AI technologies offer the potential to personalize learning, streamline processes, and expand access, they also risk exacerbating existing inequalities if not intentionally aligned with inclusive values. Building on our QAA-funded project outputs, this blog outlines a strategic framework for deploying AI to foster inclusion, equity, and ethical responsibility in higher education.

The digital divide and GenAI

Extensive research shows that students from marginalized backgrounds often face barriers in accessing digital tools, digital literacy training, and peer networks essential for technological confidence. GenAI exacerbates this divide, demanding not only infrastructure (devices, subscriptions, internet access) but also critical AI literacy. According to previous research, students with higher AI competence outperform peers academically, deepening outcome disparities.

However, the challenge is not merely technological; it is social and structural. WP (Widening Participation) students often remain outside informal digital learning communities where GenAI tools are introduced and shared. Without intervention, GenAI risks becoming a “hidden curriculum” advantage for already-privileged groups.

A framework for inclusive GenAI adoption

Our QAA-funded “Framework for Educators” proposes five interrelated principles to guide ethical, inclusive AI integration:

  • Understanding and Awareness Foundational AI literacy must be prioritized. Awareness campaigns showcasing real-world inclusive uses of AI (eg Otter.ai for students with hearing impairments) and tiered learning tracks from beginner to advanced levels ensure all students can access, understand, and critically engage with GenAI tools.
  • Inclusive Collaboration GenAI should be used to foster diverse collaboration, not reinforce existing hierarchies. Tools like Miro and DeepL can support multilingual and neurodiverse team interactions, while AI-powered task management (eg Notion AI) ensures equitable participation. Embedding AI-driven teamwork protocols into coursework can normalize inclusive digital collaboration.
  • Skill Development Higher-order cognitive skills must remain at the heart of AI use. Assignments that require evaluating AI outputs for bias, simulating ethical dilemmas, and creatively applying AI for social good nurture critical thinking, problem-solving, and ethical awareness.
  • Access to Resources Infrastructure equity is critical. Universities must provide free or subsidized access to key AI tools (eg Grammarly, ReadSpeaker), establish Digital Accessibility Centers, and proactively support economically disadvantaged students.
  • Ethical Responsibility Critical AI literacy must include an ethical dimension. Courses on AI ethics, student-led policy drafting workshops, and institutional AI Ethics Committees empower students to engage responsibly with AI technologies.

Implementation strategies

To operationalize the framework, a phased implementation plan is recommended:

  • Phase 1: Needs assessment and foundational AI workshops (0–3 months).
  • Phase 2: Pilot inclusive collaboration models and adaptive learning environments (3–9 months).
  • Phase 3: Scale successful practices, establish Ethics and Accessibility Hubs (9–24 months).

Key success metrics include increased AI literacy rates, participation from underrepresented groups, enhanced group project equity, and demonstrated critical thinking skill growth.

Discussion: opportunities and risks

Without inclusive design, GenAI could deepen educational inequalities, as recent research warns. Students without access to GenAI resources or social capital will be disadvantaged both academically and professionally. Furthermore, impersonal AI-driven learning environments may weaken students’ sense of belonging, exacerbating mental health challenges.

Conversely, intentional GenAI integration offers powerful opportunities. AI can personalize support for students with diverse learning needs, extend access to remote or rural learners, and reduce administrative burdens on staff – freeing them to focus on high-impact, relational work such as mentoring.

Conclusion

The future of inclusive higher education depends on whether GenAI is adopted with a clear commitment to equity and social justice. As our QAA project outputs demonstrate, the challenge is not merely technological but ethical and pedagogical. Institutions must move beyond access alone, embedding critical AI literacy, equitable resource distribution, community-building, and ethical responsibility into every stage of AI adoption.

Generative AI will not close the digital divide on its own. It is our pedagogical choices, strategic designs, and values-driven implementations that will determine whether the AI-driven university of the future is one of exclusion – or transformation.

This blog is based on the recent outputs from our QAA-funded project entitled: “Using AI to promote education for sustainable development and widen access to digital skills”

Dr Eleni Meletiadou is an Associate Professor (Teaching) at London Metropolitan University  specialising in Equity, Diversity, and Inclusion (EDI), AI, inclusive digital pedagogy, and multilingual education. She leads the Education for Social Justice and Sustainable Learning and Development (RILEAS) and the Gender Equity, Diversity, and Inclusion (GEDI) Research Groups. Dr Meletiadou’s work, recognised with the British Academy of Management Education Practice Award (2023), focuses on transforming higher education curricula to promote equitable access, sustainability, and wellbeing. With over 15 years of international experience across 35 countries, she has led numerous projects in inclusive assessment and AI-enhanced learning. She is a Principal Fellow of the Higher Education Academy and serves on several editorial boards. Her research interests include organisational change, intercultural communication, gender equity, and Education for Sustainable Development (ESD). She actively contributes to global efforts in making education more inclusive and future-ready. LinkedIn: https://www.linkedin.com/in/dr-eleni-meletiadou/