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

by Qianqian Chai and Xue Zhou

Introduction – the challenge of GenAI integration in business education

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

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

ABC levels of GenAI integration in the business curriculum

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

Table 1 ABC levels of GenAI curriculum integration

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

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

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

Why constructive integration matters

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

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

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

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

How constructive integration is achieved

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

Table 2 Constructive GenAI Integration into Existing Pedagogical Strategies

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

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

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

Conclusion

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

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

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


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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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Will GenAI narrow or widen the digital divide in higher education?

by Lei Fang and Xue Zhou

This blog is based on our recent publication: Zhou, X, Fang, L, & Rajaram, K (2025) ‘Exploring the digital divide among students of diverse demographic backgrounds: a survey of UK undergraduates’ Journal of Applied Learning and Teaching, 8(1).

Introduction – the widening digital divide

Our recent study (Zhou et al, 2025) surveyed 595 undergraduate students across the UK to examine the evolving digital divide across all forms of digital technologies. Although higher education is expected to narrow this divide and build students’ digital confidence, our findings revealed the opposite. We found that the gap in digital confidence and skills between widening participation (WP) and non-WP students widened progressively throughout the undergraduate journey. While students reported peak confidence in Year 2, this was followed by a notable decline in Year 3, when the digital divide became most pronounced. This drop coincides with a critical period when students begin applying their digital skills in real-world contexts, such as job applications and final-year projects.

Based on our study (Zhou et al, 2025), while universities offer a wide range of support such as laptop loans, free access to remote systems, extracurricular digital skills training, and targeted funding to WP students, WP students often do not make use of these resources. The core issue lies not in the absence of support, but in its uptake. WP students are often excluded from the peer networks and digital communities where emerging technologies are introduced, shared, and discussed. From a Connectivist perspective (Siemens, 2005), this lack of connection to digital, social, and institutional networks limits their awareness, confidence, and ability to engage meaningfully with available digital tools.

Building on these findings, this blog asks a timely question: as Generative Artificial Intelligence (GenAI) becomes embedded in higher education, will it help bridge this divide or deepen it further?

GenAI may widen the digital divide — without proper strategies

While the digital divide in higher education is already well-documented in relation to general technologies, the emergence of GenAI introduces new risks that may further widen this gap (Cachat-Rosset & Klarsfeld, 2023). This matters because students who are GenAI-literate often experience better academic performance (Sun & Zhou, 2024), making the divide not just about access but also about academic outcomes.

Unlike traditional digital tools, GenAI often demands more advanced infrastructure — including powerful devices, high-speed internet, and in many cases, paid subscriptions to unlock full functionality. WP students, who already face barriers to accessing basic digital infrastructure, are likely to be disproportionately excluded. This divide is not only student-level but also institutional. A few well-funded universities are able to subscribe to GenAI platforms such as ChatGPT, invest in specialised GenAI tools, and secure campus-wide licenses. In contrast, many institutions, particularly those under financial pressure, cannot afford such investments. These disparities risk creating a new cross-sector digital divide, where students’ access to emerging technologies depends not only on their background, but also on the resources of the university they attend.

In addition, the adoption of GenAI currently occurs primarily through informal channels via peers, online communities, or individual experimentation rather than structured teaching (Shailendra et al, 2024). WP students, who may lack access to these digital and social learning networks (Krstić et al, 2021), are therefore less likely to become aware of new GenAI tools, let alone develop the confidence and skills to use them effectively. Even when they do engage with GenAI, students may experience uncertainty, confusion, or fear about using it appropriately especially in the absence of clear guidance around academic integrity, ethical use, or institutional policy. This ambiguity can lead to increased anxiety and stress, contributing to wider concerns around mental health in GenAI learning environments.

Another concern is the risk of impersonal learning environments (Berei & Pusztai, 2022). When GenAI are implemented without inclusive design, the experience can feel detached and isolating, particularly for WP students, who often already feel marginalised. While GenAI tools may streamline administrative and learning processes, they can also weaken the sense of connection and belonging that is essential for student engagement and success.

GenAI can narrow the divide — with the right strategies

Although WP students are often excluded from digital networks, which Connectivism highlights as essential for learning (Goldie, 2016), GenAI, if used thoughtfully, can help reconnect them by offering personalised support, reducing geographic barriers, and expanding access to educational resources.

To achieve this, we propose five key strategies:

  • Invest in infrastructure and access: Universities must ensure that all students have the tools to participate in the AI-enabled classroom including access to devices, core software, and free versions of widely used GenAI platforms. While there is a growing variety of GenAI tools on the market, institutions facing financial pressures must prioritise tools that are both widely used and demonstrably effective. The goal is not to adopt everything, but to ensure that all students have equitable access to the essentials.
  • Rethink training with inclusion in mind: GenAI literacy training must go beyond traditional models. It should reflect Equality, Diversity and Inclusion principles recognising the different starting points students bring and offering flexible, practical formats. Micro-credentials on platforms like LinkedIn Learning or university-branded short courses can provide just-in-time, accessible learning opportunities. These resources are available anytime and from anywhere, enabling students who were previously excluded such as those in rural or under-resourced areas to access learning on their own terms.
  • Build digital communities and peer networks: Social connection is a key enabler of learning (Siemens, 2005). Institutions should foster GenAI learning communities where students can exchange ideas, offer peer support, and normalise experimentation. Mental readiness is just as important as technical skill and being part of a supportive network can reduce anxiety and stigma around GenAI use.
  • Design inclusive GenAI policies and ensure ongoing evaluation: Institutions must establish clear, inclusive policies around GenAI use that balance innovation with ethics (Schofield & Zhang, 2024). These policies should be communicated transparently and reviewed regularly, informed by diverse student feedback and ongoing evaluation of impact.
  • Adopt a human-centred approach to GenAI integration: Following UNESCO’s human-centred approach to AI in education (UNESCO, 2024; 2025), GenAI should be used to enhance, not replace the human elements of teaching and learning. While GenAI can support personalisation and reduce administrative burdens, the presence of academic and pastoral staff remains essential. By freeing staff from routine tasks, GenAI can enable them to focus more fully on this high-impact, relational work, such as mentoring, guidance, and personalised support that WP students often benefit from most.

Conclusion

Generative AI alone will not determine the future of equity in higher education, our actions will. Without intentional, inclusive strategies, GenAI risks amplifying existing digital inequalities, further disadvantaging WP students. However, by proactively addressing access barriers, delivering inclusive and flexible training, building supportive digital communities, embedding ethical policies, and preserving meaningful human interaction, GenAI can become a powerful tool for inclusion. The digital divide doesn’t close itself; institutions must embed equity into every stage of GenAI adoption. The time to act is not once systems are already in place, it is now.

Dr Lei Fang is a Senior Lecturer in Digital Transformation at Queen Mary University of London. Her research interests include AI literacy, digital technology adoption, the application of AI in higher education, and risk management. lei.fang@qmul.ac.uk

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