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

by Joanne Irving-Walton

What AI absorbs and why that matters

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

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

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

Four modes of engagement and emotional labour

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

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

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

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

Figure: Where the labour of learning now lives

Accessibility, safety and the risk of smoothing too much

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

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

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

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

Scaffold or substitute

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

Individual awareness and institutional responsibility

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

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

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

Where the emotional work now lives

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

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

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

References

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

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

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

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

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

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


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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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Who gets to decide what counts as knowledge? Big tech, AI, and the future of epistemic agency in higher education

by Mehreen Ashraf, Eimear Nolan, Manuel F Ramirez, Gazi Islam and Dirk Lindebaum

Walk into almost any university today, and you can be sure to encounter the topic of AI and how it affects higher education (HE). AI applications, especially large language models (LLM), have become part of everyday academic life, being used for drafting outlines, summarising readings, and even helping students to ‘think’. For some, the emergence of LLMs is a revolution that makes learning more efficient and accessible. For others, it signals something far more unsettling: a shift in how and by whom knowledge is controlled. This latter point is the focus of our new article published in Organization Studies.

At the heart of our article is a shift in what is referred to epistemic (or knowledge) governance: the way in which knowledge is created, organised, and legitimised in HE. In plain terms, epistemic governance is about who gets to decide what counts as credible, whose voices are heard, and how the rules of knowing are set. Universities have historically been central to epistemic governance through peer review, academic freedom, teaching, and the public mission of scholarship. But as AI tools become deeply embedded in teaching and research, those rules are being rewritten not by educators or policymakers, but by the companies that own the technology.

From epistemic agents to epistemic consumers

Universities, academics, and students have traditionally been epistemic agents: active producers and interpreters of knowledge. They ask questions, test ideas, and challenge assumptions. But when we rely on AI systems to generate or validate content, we risk shifting from being agents of knowledge to consumers of knowledge. Technology takes on the heavy cognitive work: it finds sources, summarises arguments, and even produces prose that sounds academic. However, this efficiency comes at the cost of profound changes in the nature of intellectual work.

Students who rely on AI to tidy up their essays, or generate references, will learn less about the process of critically evaluating sources, connecting ideas and constructing arguments, which are essential for reasoning through complex problems. Academics who let AI draft research sections, or feed decision letters and reviewer reports into AI with the request that AI produces a ‘revision strategy’, might save time but lose the slow, reflective process that leads to original thought, while undercutting their own agency in the process. And institutions that embed AI into learning systems hand part of their epistemic governance – their authority to define what knowledge is and how it is judged – to private corporations.

This is not about individual laziness; it is structural. As Shoshana Zuboff argued in The age of surveillance capitalism, digital infrastructures do not just collect information, they reorganise how we value and act upon it. When universities become dependent on tools owned by big tech, they enter an ecosystem where the incentives are commercial, not educational.

Big tech and the politics of knowing

The idea that universities might lose control of knowledge sounds abstract, but it is already visible. Jisc’s 2024 framework on AI in tertiary education warns that institutions must not ‘outsource their intellectual labour to unaccountable systems,’ yet that outsourcing is happening quietly. Many UK universities, including the University of Oxford, have signed up to corporate AI platforms to be used by staff and students alike. This, in turn, facilitates the collection of data on learning behaviours that can be fed back into proprietary models.

This data loop gives big tech enormous influence over what is known and how it is known. A company’s algorithm can shape how research is accessed, which papers surface first, or which ‘learning outcomes’ appear most efficient to achieve. That’s epistemic governance in action: the invisible scaffolding that structures knowledge behind the scenes. At the same time, it is easy to see why AI technologies appeal to universities under pressure. AI tools promise speed, standardisation, lower costs, and measurable performance, all seductive in a sector struggling with staff shortages and audit culture. But those same features risk hollowing out the human side of scholarship: interpretation, dissent, and moral reasoning. The risk is not that AI will replace academics but that it will change them, turning universities from communities of inquiry into systems of verification.

The Humboldtian ideal and why it is still relevant

The modern research university was shaped by the 19th-century thinker Wilhelm von Humboldt, who imagined higher education as a public good, a space where teaching and research were united in the pursuit of understanding. The goal was not efficiency: it was freedom. Freedom to think, to question, to fail, and to imagine differently.

That ideal has never been perfectly achieved, but it remains a vital counterweight to market-driven logics that render AI a natural way forward in HE. When HE serves as a place of critical inquiry, it nourishes democracy itself. When it becomes a service industry optimised by algorithms, it risks producing what Žižek once called ‘humans who talk like chatbots’: fluent, but shallow.

The drift toward organised immaturity

Scholars like Andreas Scherer and colleagues describe this shift as organised immaturity: a condition where sociotechnical systems prompt us to stop thinking for ourselves. While AI tools appear to liberate us from labour, what is happening is that they are actually narrowing the space for judgment and doubt.

In HE, that immaturity shows up when students skip the reading because ‘ChatGPT can summarise it’, or when lecturers rely on AI slides rather than designing lessons for their own cohort. Each act seems harmless; but collectively, they erode our epistemic agency. The more we delegate cognition to systems optimised for efficiency, the less we cultivate the messy, reflective habits that sustain democratic thinking. Immanuel Kant once defined immaturity as ‘the inability to use one’s understanding without guidance from another.’ In the age of AI, that ‘other’ may well be an algorithm trained on millions of data points, but answerable to no one.

Reclaiming epistemic agency

So how can higher education reclaim its epistemic agency? The answer lies not only in rejecting AI but also in rethinking our possible relationships with it. Universities need to treat generative tools as objects of inquiry, not an invisible infrastructure. That means embedding critical digital literacy across curricula: not simply training students to use AI responsibly, but teaching them to question how it works, whose knowledge it privileges, and whose it leaves out.

In classrooms, educators could experiment with comparative exercises: have students write an essay on their own, then analyse an AI version of the same task. What’s missing? What assumptions are built in? How were students changed when the AI wrote the essay for them and when they wrote them themselves? As the Russell Group’s 2024 AI principles note, ‘critical engagement must remain at the heart of learning.’

In research, academics too must realise that their unique perspectives, disciplinary judgement, and interpretive voices matter, perhaps now more than ever, in a system where AI’s homogenisation of knowledge looms. We need to understand that the more we subscribe to values of optimisation and efficiency as preferred ways of doing academic work, the more natural the penetration of AI into HE will unfold.

Institutionally, universities might consider building open, transparent AI systems through consortia, rather than depending entirely on proprietary tools. This isn’t just about ethics; it’s about governance and ensuring that epistemic authority remains a public, democratic responsibility.

Why this matters to you

Epistemic governance and epistemic agency may sound like abstract academic terms, but they refer to something fundamental: the ability of societies and citizens (not just ‘workers’) to think for themselves when/if universities lose control over how knowledge is created, validated and shared. When that happens, we risk not just changing education but weakening democracy. As journalist George Monbiot recently wrote, ‘you cannot speak truth to power if power controls your words.’ The same is true for HE. We cannot speak truth to power if power now writes our essays, marks our assignments, and curates our reading lists.

Mehreen Ashraf is an Assistant Professor at Cardiff Business School, Cardiff University, United Kingdom.

Eimear Nolan is an Associate Professor in International Business at Trinity Business School, Trinity College Dublin, Ireland.

Manuel F Ramirez is Lecturer in Organisation Studies at the University of Liverpool Management School, UK.

Gazi Islam is Professor of People, Organizations and Society at Grenoble Ecole de Management, France.

Dirk Lindebaum is Professor of Management and Organisation at the School of Management, University of Bath.


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The challenge of AI declaration in HE – what can we do?

by Chahna Gonsalves

The rapid integration of AI tools like ChatGPT into academic life has raised significant concerns about academic integrity. Universities worldwide are grappling with how to manage this new frontier of technology. My recent research at King’s Business School sheds light on an intriguing challenge: student non-compliance with mandatory AI use declarations. Despite clear institutional requirements to declare AI usage in their coursework, up to 74% of students did not comply. This raises key questions about how we think about academic honesty in the age of AI, and what can be done to improve compliance and foster trust.

In November 2023, King’s Business School introduced an AI declaration section as part of the coursework coversheet. Students were required to either declare their AI use or confirm that they hadn’t used any AI tools in their work. This research, which started as an evaluation of the revised coversheet, was conducted a year after the implementation of this policy, providing insights into how students have navigated these requirements over time. The findings reveal important challenges for both educators and students in adapting to this new reality.

Fear and ambiguity: barriers to transparency

In interviews conducted as part of the study, students frequently voiced their apprehension about how AI declarations might be perceived. One student likened it to “admitting to plagiarism,” reflecting a widespread fear that transparency could backfire. Such fears illustrate a psychological barrier to compliance, where students perceive AI use declarations as risky rather than neutral. This tension is exacerbated by the ambiguity of current policies. Guidelines are often unclear, leaving students uncertain about what to declare and how that declaration will impact their academic standing.

Moreover, the rapid evolution of AI tools has blurred traditional lines of authorship and originality. Before the rise of AI, plagiarism was relatively easy to define. But now, as AI tools generate content that is indistinguishable from human-authored work, what does it mean to be original? The boundaries of academic integrity are being redrawn, and institutions need to adapt quickly to provide clearer guidance. As AI technologies become more integrated into academic practice, we must move beyond rigid policies and have more nuanced conversations about what responsible AI use looks like in different contexts.

Peer influence: AI as the “fourth group member”

A particularly striking finding from the research was the role of peer influence in shaping students’ decisions around AI use and its declaration. In group work contexts, AI tools like ChatGPT have become so normalized that one student referred to ChatGPT as the “fourth man” in group projects. This normalization makes it difficult for students to declare AI use, as doing so might set them apart from their peers who choose not to disclose. The pressure to conform can be overwhelming, and it drives non-compliance as students opt to avoid the risk of being singled out.

The normalising effect of AI usage amongst peers reflects a larger trend in academia, where technological adoption is outpacing institutional policy. This raises an urgent need for universities to not only set clear guidelines but also engage students and faculty in open discussions about AI’s role in academic work. Creating a community of transparency where AI use is openly acknowledged and discussed is crucial to overcoming the current challenges.

Solutions: clearer policies, consistent enforcement, and trust

What can be done to improve compliance with AI declarations? The research offers several recommendations. First, institutions need to develop clearer and more consistent policies around AI use. The ambiguity that currently surrounds AI guidelines must be addressed. Students need to know exactly what is expected of them, and this starts with clear definitions of what constitutes AI use and how it should be declared.

Second, enforcement of these policies needs to be consistent across all courses. Many students reported that AI declarations were emphasized in some modules but barely mentioned in others. This inconsistency breeds confusion and scepticism about the importance of the policy. Faculty training is crucial to ensuring that all educators communicate the same message to students about AI use and its implications for academic integrity.

Finally, building trust between students and institutions is essential. Students must feel confident that declaring AI use will not result in unfair penalties. One approach to building this trust is to integrate AI use into low-stakes formative assessments before moving on to higher-stakes summative assessments. This gradual introduction allows students to become comfortable with AI policies and to see that transparency will not harm their academic performance. In the long run, fostering an open, supportive dialogue around AI use can help reduce the fear and anxiety currently driving non-compliance.

Moving forward: a call for open dialogue and innovation

As AI continues to revolutionize academic work, institutions must rise to the challenge of updating their policies and fostering a culture of transparency. My research suggests that fear, ambiguity, and peer influence are key barriers to AI declaration, but these challenges can be overcome with clearer policies, consistent enforcement, and a foundation of trust. More than just a compliance issue, this is an opportunity for higher education to rethink academic integrity in the age of AI and to encourage ethical, transparent use of technology in learning.

In the end, the goal should not be to police AI use, but to harness its potential for enhancing academic work while maintaining the core values of honesty and originality. Now is the time to open up the conversation and invite both students and educators to reimagine how we define integrity in the evolving landscape of higher education. Let’s make AI part of the learning process—not something to be hidden.

This post is based on my paper Addressing Student Non-Compliance in AI Use Declarations: Implications for Academic Integrity and Assessment in Higher Education in Assessment & Evaluation in Higher Education (Published online: 22 Oct 2024).

I hope this serves as a starting point for broader discussions about how we can navigate the complexities of AI in academic settings. I invite readers to reflect on these findings and share their thoughts on how institutions can better manage the balance between technological innovation and academic integrity. 

Chahna Gonsalves is a Senior Lecturer in Marketing (Education) at King’s College London. She is Senior Fellow of the Higher Education Association and Associate Fellow of the Staff Educational Development Association.


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Restraining the uncanny guest: AI ethics and university practice

by David Webster

If GAI is the ‘uncanniest of guests’ in the University what can we do about any misbehaviour? What do we do with this uninvited guest who behaves badly, won’t leave and seems intent on asserting that it’s their house now anyway?  They won’t stay in their room and seem to have their fingers in everything.

Nihilism stands at the door: whence comes this uncanniest of all guests?[1]

Nietzsche saw the emergence of nihilistic worldviews as presaging a century of turmoil and destruction, only after which might more creative responses to the sweeping away of older systems of thought be possible. Generative Artificial Intelligence, uncanny in its own discomforting ways, might be argued as threatening the world of higher education with an upending of the existing conventions and practices that have long been the norm in the sector. Some might welcome this guest, in that there is much wrong in the way universities have created knowledge, taught students, served communities and reproduced social practice. The concern must surely be though that GAI is not a creative force, but a repackaging and re-presenting of existing human understanding and belief. We need to think carefully about the way this guest’s behaviour might exert influence in our house.

After decades of seeking to eliminate prejudices and bias, GAI threatens to reinscribe misogyny, racism, homophobia and other unethical discrimination back into the academy. Since  the majority of content used to train large language models has been generated by the most prominent and privileged groups in human culture, might not we see a recolonisation, just as universities are starting to push for a more decolonised, inclusive and equitable learning experience?

After centuries of citation tradition and careful attribution of sources, GAI seems intent on shuffling the work of human scholars and presenting it without any clarity as to whence it came. Some news organisations and  authors are even threatening to sue OpenAI as they believe their content has been used, without permission, to train the company’s ChatGPT tool.

Furthermore, this seems to be a guest inclined to hallucinate and recount their visions as the earnest truth. The guest has also imbibed substantive propaganda, taken satirical articles as serious factual account (hence the glue pizza and rock AI diet), and is targeted by pseudo-science dressed in linguistic frames of respectability. How can we deal with this confident, ambitious, and ill-informed guest who keeps offering to save us time and money?

While there isn’t a simple answer (if I had that, I’d be busy monetising it!), an adaptation of this guest metaphor might help. This is to view GAI rather like an unregulated child prodigy: awash with talent but with a lacuna of discernment. It can do so much, but often doesn’t have the capacity to know what it shouldn’t do, what is appropriate or helpful and what is frankly dangerous.

GAI systems are capable of almost magical-seeming feats, but also lack basic understanding of how the world operates and are blind to all kinds of contextual appreciation. Most adults would take days trying to draw what a GAI system can generate in seconds, and would struggle to match its ‘skills’, but even an artistically-challenged adult likely myself with barely any artistic talent at all would know how many fingers, noses or arms, were appropriate in a picture – no matter how clumsily I rendered them. The idea of GAI as a child prodigy, in need of moral guidance and requiring tutoring and careful curation of the content they are exposed to, can help us better understand just how limited these systems are. This orientation to GAI also helps us see that what are witnessing is not a finished solution to various tasks currently undertaken by people, but rather a surplus of potential. The child prodigy is capable of so much, but is still a child and critically, still requires prodigious supervision.

So as universities look to use student-facing chatbots for support and answering queries, to automate their arcane and lengthy internal processes, to sift through huge datasets and to analyse and repackage existing learning content, we need to be mindful of GAI’S immaturity. It offers phenomenal potential in all these areas and despite the overdone hype  it will drive a range of huge changes to how we work in higher education, but it is far from ready to work unsupervised. GAI needs moral instruction, it needs to be reshaped as it develops and we might do this through assuming the mindset of a watchful, if also proud, parent.

Professor Dave Webster is Director of Education, Quality & Enhancement at the University of Liverpool. He has a background in teaching philosophy, and the study of religion, with ongoing interests in Buddhist thought, and the intersections of new religious movements and conspiracy theory.  He is also concerned about pedagogy, GAI and the future of Higher Education.

[1] The Will to Power, trans. Walter Kaufmann and R. J. Hollingdale, ed., with commentary, Walter Kaufmann, Vintage, 1968.               


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Reflecting on five years of feedback research and practice: progress and prospects

by Naomi Winstone and David Carless

Over the past few years, feedback research and practice in higher education have experienced sustained research interest and significant advancements. These developments have been propelled by a deeper understanding of student responses to feedback, the impact of cultural and sociomaterial factors, and the affordances and challenges posed by digital assessment and feedback methods. In 2019, we published a book in the SRHE series titled Designing Effective Feedback Processes in Higher Education: A Learning-Focused Approach. Five years later, we find it pertinent to reflect on the changes in research, practice, and discourse surrounding feedback processes in higher education since the book’s release.

Shifting paradigms in feedback processes

The book aimed to achieve two primary objectives: to present findings from the SRHE-funded ‘feedback cultures’ project and to synthesise evidence on feedback processes that prioritise student learning – what we called learning-focused feedback. This evidence was then translated into practical guidance and stimulus for reflection. A core distinction made in the book was between an ‘old paradigm’, characterized by the one-way transmission of feedback comments from educators to students, and a ‘new paradigm’, which emphasises student learning through active engagement with feedback processes of different forms, including peer feedback, self-feedback and automated feedback.

The impact of recent developments

The past five years have seen seismic shifts affecting feedback processes. The COVID-19 pandemic demonstrated the feasibility of alternative approaches to assessment and feedback, debunking many myths about insurmountable constraints. It brought issues of relationality and social presence to the forefront. Additionally, the launch of ChatGPT in November 2022 sparked debates on the distinct value of human involvement in feedback processes. Concurrently, higher education has grappled with sector-wide challenges, such as the devaluation of tuition fees in the UK and the intensification of the consumer-provider relationship.

Significant developments in feedback research and practice

Since 2019, feedback research and practice have evolved significantly. Two developments stand out to us as particularly impactful:

1. The ongoing boom of interest in feedback literacy

Feedback literacy research has become a fast-growing trend within research into feedback in higher education. The basis of feedback literacy is that students need a set of competencies which enable them to make the most of feedback opportunities of different kinds. And for students to develop these competencies, teachers need to design opportunities for students to generate, make sense of and use a variety of feedback inputs from peers, the self, teachers, or automated systems.

Student feedback literacy includes the ability to appreciate and judge the value of feedback inputs of different forms. This attribute remains relevant to both human and non-human feedback exchanges. Sometimes feedback inputs are off-target or inaccurate, so responsibility lies with the learner in using information prudently to move work forward. This is particularly pertinent in terms of inputs or feedback from generative AI (GenAI) to which we turn next. Judging the value and accuracy of GenAI inputs, and deciding what further probing or verifying is needed become important learning strategies.

2. Challenges and affordances of GenAI

The potential impact of technological disruption is often overestimated. However, the advent of ChatGPT and other large language models (LLMs) has undeniably generated both excitement and anxiety. In higher education, while assessment design has been the primary concern, discussions around feedback have also intensified.

Given the escalating and unsustainable costs of teaching in higher education, AI is sometimes seen as a panacea. Providing feedback comments – a time-consuming task for academics – could be outsourced to GenAI, theoretically freeing up time for other activities such as teaching, administration, or research. However, we caution against this approach. The mere provision of feedback comments, regardless of their origin, epitomises an old paradigm practice. As argued in our book, a process-oriented approach to feedback means that comments alone do not constitute feedback; they are merely inputs into a feedback process. Feedback occurs only when students engage with and act upon these comments.

Nevertheless, AI offers potential benefits for new paradigm feedback practices. A potential benefit of GenAI feedback is that it can be provided at a time when students need it. And if GenAI can assist educators in drafting feedback comments, it could free up time for more meaningful engagement with students, such as facilitating the implementation of feedback, supporting peer dialogue, and enhancing evaluative expertise. GenAI can also help students generate feedback on their own work, thereby developing their own evaluative judgement. In short, GenAI may not be harmful to feedback processes if we hold true to the principles of new paradigm learning-focused approaches we presented in our book.

Looking ahead: future directions in feedback research and practice

What might the next five years hold for feedback research and practice? Feedback literacy is likely to remain a key research theme because without feedback literacy it is difficult for both teachers and students to derive benefits and satisfaction from feedback processes. The potential and pitfalls of GenAI as a feedback source is likely to be a heavily populated research field. Methodologically, we anticipate a shift towards more longitudinal studies and a greater focus on behavioural outcomes, acknowledging the complexity of feedback impacts. These can be investigated over long-term durations as well as short-term ones because the benefits of complex, higher-order feedback often take time to accrue. As researchers, we are privileged to be part of a dynamic international community, working within a rapidly evolving policy and practice landscape. The field abounds with questions, challenges, and opportunities for exploration. We are excited to see what developments the future holds.

Naomi Winstone is a cognitive psychologist specialising in the processing and impact of instructional feedback, and the influence of dominant discourses of assessment and feedback in policy and practice on the positioning of educators and students in feedback processes. Naomi is Professor of Educational Psychology and Director of the Surrey Institute of Education at the University of Surrey, UK. She is also an Honorary Professor in the Centre for Research in Assessment and Digital Learning (CRADLE) at Deakin University, Australia. Naomi is a Principal Fellow of the Higher Education Academy and a UK National Teaching Fellow.

David Carless works as a Professor at the Faculty of Education, University of Hong Kong, and is Head of the Academic Unit SCAPE (Social Contexts and Policies in Education). He is one of the pioneers of feedback literacy research and is listed as a top 0.1% cited researcher in the Stanford top 2% list for social sciences. His books include Designing effective feedback processes in higher education: A learning-focused approach, by Winstone and Carless, 2019 published by Routledge. He was the winner of a University Outstanding Teaching Award in 2016. The latest details of his work are on his website: https://davidcarless.edu.hku.hk/.


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Unmasking the complexities of academic work

by Inger Mewburn

Hang out in any tearoom and you will hear complaints about work – that’s if there even is a tea room at the end of your open plan cubicle farm. Yet surprisingly little is known about the mundane, daily realities of academic work itself – despite the best efforts of many SRHE members.

Understanding the source of academic work unhappiness is important: unhappy academics lead to unhappy students and stressed-out administrators. If we know more about academics’ working lives, we are better placed to care for our colleagues and produce the kind of research and teaching our broader communities expect of us.

To understand more about academics’ working lives, we are embarking on an ambitious research project to survey 5000 working academics and would love you to take part.

Who is doing the ‘academic housework’?

Higher education institutions are major employers and substantial contributors to national economies. Yet there is a notable lack of comprehensive research on the practicalities of academic work, particularly with respect to how we bring our ‘whole self’ to work.

Just about everyone in academia is dealing with some aspect of their lives which affects how they do their work. Some are neurodiverse, with neurodiverse teenagers at home. Others may have a disability and are part of an under-represented group. More of us than you would think face financial precariousness and just being a woman can result in being given more of the ‘academic housework’. The impact of these various circumstances can be negative or positive from the employer point of view. For example, we know that neurodivergent academics spend a lot of energy ‘masking’ to make other people’s work lives easier, often at the expense of their own wellbeing (Jones, 2023). But we also know that including neurodiverse people in research groups can increase scientific productivity. At the same time, many neurodivergent people avoid disclosing for fear of stigma (even the word ‘disclose’ suggests that individuals should feel shame for merely being who they are).

Benefits for our employers can come at a great cost for us as individuals. While a body of literature exists on factors that affect student academic performance in university settings, there is no equivalent focus on university staff. The literature on students helps us design appropriate processes and services to try to even out the playing field and help everyone reach their potential. But we do not show this same compassion towards ourselves. The existing discourse on academics as workers tends to revolve around output metrics and shallow performance measures. This narrow focus fails to capture the full spectrum of academic labour and our lived experiences.

Our research aims to fill this gap by exploring how academics experience their work from their own perspectives. We seek to understand how the production of knowledge occurs, how academic work is constructed and experienced through daily practices, with a specific focus on academic productivity and distraction. We want to see how various bio-demographic factors interrelate and impact feelings like overwhelm and exhaustion.

Why this research matters

The importance of this study is multifaceted:

1. Informing Policy and Practice: By gaining a deeper understanding of academic work patterns, institutions can develop more effective policies to support their staff and enhance productivity and wellbeing.

2. Addressing Inequalities: The COVID-19 pandemic has highlighted and exacerbated existing inequalities in academia. Our research will explore how factors such as gender, caring responsibilities, and neurodiversity impact academic work experiences.

3. Adapting to Change: As the higher education sector continues to evolve, particularly in the wake of the pandemic and the rise of digital technologies like AI, it’s crucial to understand how these changes affect academic work practices.

4. Supporting Well-being: By examining the interplay between productivity, distraction, and work intensity, we can identify strategies to better support academics’ well-being and job satisfaction.

5. Enhancing Knowledge Production: Ultimately, by understanding and improving the conditions of academic work, we can enhance the quality and quantity of knowledge production in higher education and make better classrooms for everyone.

A comprehensive approach

Our study employs a mixed-methods approach, combining a large-scale survey with follow-up interviews. This methodology allows us to capture both broad trends and individual experiences, providing a nuanced picture of academic work life.

The survey covers a wide range of topics, including:

– Perceptions of academic productivity

– Experiences of distraction and focus

– Work distribution across research, teaching, and administration

– Impact of factors such as neurodiversity, caring responsibilities, and chronic conditions

– Use of technology and AI in academic work

– Feelings of belonging and value within the academic community

We are particularly interested in exploring how these factors intersect and influence each other. For instance, how does neurodiversity impact experiences of productivity and distraction? How do caring responsibilities interact with gender in relation to the number of hours worked and where the work takes place? And who thinks AI is helpful to their work and how are people ‘cognitively offloading’ to machines?

Call for participation

The success of this research hinges on wide participation from across the academic community. We are seeking respondents from all career stages, disciplines, and geographical locations. Whether you’re a seasoned professor or a new PhD student, whether you identify as neurodivergent or not, whether you love academic life or find it challenging – your experiences are valuable and needed.

Moreover, this research provides an opportunity for self-reflection. By engaging with the survey questions, you may gain new insights into your own work practices and experiences, potentially leading to personal growth and improved work strategies.

Looking ahead

The findings from this study will be disseminated through various channels, including academic publications, teaching materials, and potentially, policy recommendations. We are committed to making our results accessible and applicable to the wider academic community.

We stand at a critical juncture in higher education. As the sector faces unprecedented challenges and changes, understanding the nature of academic work has never been more important. By participating in this research, you can play a crucial role in shaping the future of academia.

To participate in the survey or learn more about the study, please visit the survey here: https://anu.au1.qualtrics.com/jfe/form/SV_eEeXg1L3RZJJWce.

Professor Inger Mewburn is the Director of Researcher Development at The Australian National University where she oversees professional development workshops and programs for all ANU researchers. Aside from creating new posts on the Thesis Whisperer blog (www.thesiswhisperer.com), she writes scholarly papers and books about research education, with a special interest in post PhD employability, research communications and neurodivergence.

Reference

Jones, S (2023) ‘Advice for autistic people considering a career in academia’ Autism 27(7) pp 2187–2192


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Spotlight on the inclusion process in developing AI guidance and policy

by Lilian Schofield and Joanne J. Zhang

Introduction

When the discourse on ChatGPT started gaining momentum in higher education in 2022, the ‘emotions’ behind the response of educators, such as feelings of exclusion, isolation, and fear of technological change, were not initially at the forefront. Even educators’ feelings of apprehension about the introduction and usage of AI in education, which is an emotional response, were not given much attention. This feeling was highlighted by Ng et al (2023), who stated that many AI tools are new to educators, and many educators may feel overwhelmed by them due to a lack of understanding or familiarity with the technology. The big issues then were talks on banning the use of ChatGPT, ethical and privacy concerns, inclusive issues and concerns about academic misconduct (Cotton et al, 2023; Malinka et al, 2023; Rasul et al, 2023; Zhou & Schofield, 2023).

As higher education institutions started developing AI guidance in education, again the focus seemed to be geared towards students’ ethical and responsible usage of AI and little about educators’ guidance. Here we reflect on the process of developing the School of Business and Management, Queen Mary University of London’s AI guidance through the lens of inclusion and educators’ ‘voice’. We view ‘inclusion’ as the active participation and contribution of educators in the process of co-creating the AI policy alongside multiple voices from students and staff.

Co-creating inclusive AI guidance

Triggered by the lack of clear AI guidance for students and educators, the School of Business and Management at the Queen Mary University of London (QMUL) embarked on developing AI guidance for students and staff from October 2023 to March 2024.  Led by Deputy Directors of Education Dr Joanne J. Zhang and Dr Darryn Mitussis, the guidance was co-created with staff members through different modes, such as the best practice sharing sessions, staff away day, student-staff consultation, and staff consultation. These experiences helped shape the inclusive way and bottom-up approach of developing the AI guidance. The best practice sharing sessions allowed educators to contribute their expertise as well as provide a platform to voice their fears and apprehensions about adopting and using AI for teaching. The sessions acted as a space to share concerns and became a space where educators could have a sense of relief and solidarity. Staff members shared that knowing that others share similar apprehensions was reassuring and reduced the feeling of isolation. This collective space helped promote a more collaborative and supportive environment for educators to comfortably explore AI applications in their teaching.

Furthermore, the iterative process of developing this guidance has engaged different ‘voices’ within and outside the school. For instance, we discussed with the QMUL central team their approach and resources for facilitating AI usage for students and staff. We discussed Russell Group principles on AI usage and explored different universities’ AI policies and practices. The draft guideline was discussed and endorsed at the Teaching Away Day and education committee meetings. As a result, we suggested three principles for developing effective practices in teaching and learning:

  1. Explore and learn.
  2. Discuss and inform.
  3. Stress test and validate.

Key learning points from our process include having the avenue to use voice, whether in support of AI or not, and ensuring educators are active participants in the AI guidance-making process. This is also reflected in the AI guidance, which supports all staff in developing effective practices at their own pace.

Consultation with educators and students was an important avenue for inclusion in the process of developing the AI policy. Open communication and dialogue facilitated staff members’ opportunities to contribute to and shape the AI policy. This consultative approach enhanced the inclusion of educators and strengthened the AI policy.

Practical suggestions

Voice is a powerful tool (Arnot & Reay, 2007). However, educators may feel silenced and isolated without an avenue for their  voice. This ‘silence’ and isolation takes us back to the initial challenges experienced at the start of AI discourse, such as apprehension, fear, and isolation. The need to address these issues is pertinent, especially now when employers, students and higher education drive AI to be embedded in the curriculum and have AI-skilled graduates (Southworth et al, 2023). A co-creative approach to developing AI policies is crucial to enable critique and learning, promoting a sense of ownership and commitment to the successful integration of AI in education.

The process of developing an AI policy itself serves as the solution to the barriers to educators adopting AI in their practice and an enabler for inclusion. It ensures educators’ voices are heard, addresses their fears, and finds effective ways to develop a co-created AI policy. This inclusive participatory and co-creative approach helped mitigate fears associated with AI by creating a supportive environment where apprehensions can be openly discussed and addressed.

The co-creative approach of developing the policy with educators’ voices plays an important role in AI adoption. Creating avenues, such as the best practice sharing sessions where educators can discuss their experiences with AI, both positive and negative, ensures that voices are heard and concerns are acknowledged and addressed. This collective sharing builds a sense of community and support, helping to alleviate individual anxieties.

Steps that could be taken towards an inclusive approach to developing an inclusive AI guidance and policy are as follows:

  1. Set up the core group – Director for Education, chair of the exam board, and the inclusion of educators from different subject areas. Though the development of AI guidance can have a top-down approach, it is important that the group set-up is inclusive of educators’ voices and concerns.
  2. Design multiple avenues for educators ‘voices’ to be heard (best practice sharing sessions within and cross faulty, teaching away day).
  3. Communication channels are clear and open for all to contribute.
  4. Engaging all staff and students – hearing from students directly is powerful for staff, too; we learned a lot from students and included their voices in the guidance.
  5. Integrate and gain endorsements from the school management team. Promoting educators’ involvement in creating AI guidance legitimises their contributions and ensures that their insights are taken seriously. Additionally, such endorsement ensures that AI guidance is aligned with the needs and ethical considerations of those directly engaged and affected by the guidance.

Conclusion

As many higher education institutions move towards embedding AI into the curriculum and become clearer in their AI guidance, it is crucial to acknowledge and address the emotional dimensions educators face in adapting to AI technologies in education. Educators’ voices in contributing to AI policy and guidance are important in ensuring that they are clear about the guidance, embrace it and are upskilled in order for the embedding and implementation of AI in teaching and learning to be successful.

Dr. Lilian Schofield is a senior lecturer in Nonprofit Management and the Deputy Director of Student Experience at the School of Business and Management, Queen Mary University of London. Her interests include critical management pedagogy, social change, and sustainability. Lilian is passionate about incorporating and exploring voice, silence, and inclusion into her practice and research. She is a Queen Mary Academy Fellow and has taken up the Learning and Teaching Enhancement Fellowship, where she works on student skills enhancement practice initiatives at Queen Mary University of London.

Dr Joanne J. Zhang is Reader in Entrepreneurship, Deputy Director of Education at the School of Business and Management, Queen Mary University of London, and a visiting fellow at the University of Cambridge. She is the ‘Entrepreneurship Educator of the Year’, Triple E European Award 2022. Joanne is also the founding director of the Entrepreneurship Hub , and the QM Social Venture Fund  - the first student-led social venture fund investing in ‘startups for good’ in the UK.  Joanne’s research and teaching interests are entrepreneurship, strategy and entrepreneurship education. She has led and engaged in large-scale research and scholarship projects totalling over GBP£7m.  Email: Joanne.zhang@qmul.ac.uk


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For meta or for worse…

by Paul Temple

Remember the Metaverse? Oh, come on, you must remember it, just think back a year, eighteen months ago, it was everywhere! Mark Zuckerberg’s new big thing, ads everywhere about how it was going to transform, well, everything! I particularly liked the ad showing a school group virtually visiting the Metaverse forum in ancient Rome, which was apparently going to transform their understanding of the classical world. Well, that’s what $36 bn (yes, that’s billion) buys you. Accenture were big fans back then, displaying all the wide-eyed credulity expected of a global consultancy firm when they reported in January 2023 that “Growing consumer and business interest in the Metaverse [is] expected to fuel [a] trillion dollar opportunity for commerce, Accenture finds”.

It was a little difficult, though, to find actual uses of the Metaverse, as opposed to vague speculations about its future benefits, on the Accenture website. True, they’d used it in 2022 to prepare a presentation for Tuvalu for COP27; and they’d created a virtual “Global Collaboration Village” for the 2023 Davos get-together; and we mustn’t overlook the creation of the ChangiVerse, “where visitors can access a range of fun-filled activities and social experiences” while waiting for delayed flights at Singapore’s Changi airport. So all good. Now tell me that I don’t understand global business finance, but I’d still be surprised if these and comparable projects added up to a trillion dollars.

But of course that was then, in the far-off days of 2023. In 2024, we’re now in the thrilling new world of AI, do keep up! Accenture can now see that “AI is accelerating into a mega-trend, transforming industries, companies and the way we live and work…better positioned to reinvent, compete and achieve new levels of performance.” As I recall, this is pretty much what the Metaverse was promising, but never mind. Possible negative effects of AI? Sorry, how do you mean, “negative”?

It’s been often observed that every development in communications and information technology – radio, TV, computers, the internet – has produced assertions that the new technology means that the university as understood hitherto is finished. Amazon is already offering a dozen or so books published in the last six months on the impact of the various forms of AI on education, which, to go by the summaries provided, mostly seem to present it in terms of the good, the bad, and the ugly. I couldn’t spot an “end of the university as we know it” offering, but it has to be along soon.

You’ve probably played around with ChatGPT – perhaps you were one of its 100 million users logging-on within two months of its release – maybe to see how students (or you) might use it. I found it impressive, not least because of its speed, but at the same time rather ordinary: neat B-grade summaries of topics of the kind you might produce after skimming the intro sections of a few standard texts but, honestly, nothing very interesting. Microsoft is starting to include ChatGPT in its Office products; so you might, say, ask it to list the action points from the course committee minutes over the last year, based on the Word files it has access to. In other words, to get it to undertake, quickly and accurately, a task that would be straightforward yet tedious for a person: a nice feature, but hardly transformative. (By the way, have you tried giving ChatGPT some text it produced and asking where it came from? It said to me, in essence, I don’t remember doing this, but I suppose I might have: it had an oddly evasive feel.)

So will AI transform the way teaching and learning works in higher education? A recent paper by Strzelecki (2023) reporting on an empirical study of the use of ChatGPT by Polish university students notes both the potential benefits if it can be carefully integrated into normal teaching methods – creating material tailored to individuals’ learning needs, for example – as well as the obvious ethical problems that will inevitably arise. If students are able to use AI to produce work which they pass off as their own, it seems to me that that is an indictment of under-resourced, poorly-managed higher education which doesn’t allow a proper engagement between teachers and students, rather than a criticism of AI as such. Plagiarism in work that I marked really annoyed me, because the student was taking the course team for fools, assuming our knowledge of the topic was as limited as theirs. (OK, there may have been some very sophisticated plagiarism which I missed, but I doubt it: a sophisticated plagiarist is usually a contradiction in terms.)

The 2024 Consumer Electronics Show (CES), held in Las Vegas in January 2024, was all about AI. Last year it was all about the Metaverse; this year, although the Metaverse got a mention, it seemed to rank in terms of interest well below the AI-enabled cat flap on display – it stops puss coming in if it’s got a mouse in its jaws – which I’m guessing cost rather less than $36bn to develop. I’ve put my name down for one.

Dr Paul Temple is Honorary Associate Professor in the Centre for Higher Education Studies, UCL Institute of Education.