The Emerging AI Divide in Higher Education

Artificial intelligence (AI) is supposed to democratise education. The reality, as we are beginning to witness across higher education, is considerably more uncomfortable.

As academics situated across two very different institutional contexts, one in the United Kingdom and one at an Australian university campus in Malaysia, we have followed with close interest how AI is entering classrooms across higher education in uneven and sometimes contradictory ways. What we observe is not simply a divide in access to technology. It is something subtler and, we would argue, more consequential: a divide shaped by institutional readiness, academic culture, staff confidence, and policy priorities.

This distinction matters. Many students can already access generative AI through free or low-cost platforms. But access alone does not create meaningful capability. AI systems themselves are not neutral. They reflect the assumptions, language norms, and cultural contexts of their developers, which may advantage some learners more than others from the outset. Indeed, the trajectory of AI in education has long pointed towards personalised, competency-based learning, yet realising that potential equitably remains an unresolved challenge (Dastane et al., 2024).

Across universities globally, responses to AI are unfolding at very different speeds. Some institutions are integrating AI into teaching, assessment, and curriculum design. Others are proceeding cautiously because of concerns around academic integrity, reliability, or governance. A student studying in an institution with clear AI guidance, thoughtfully redesigned assessments, and staff trained in AI-supported pedagogy is likely to develop a fundamentally different kind of academic confidence from a student where AI remains largely unspoken or inconsistently regulated. This unevenness in institutional adoption is not a new observation; systematic reviews of AI applications in higher education have documented significant variation across institutions and disciplines for some years (Zawacki-Richter et al., 2019), and the gap appears to be widening rather than closing.

Increasingly, success in higher education may depend not only on subject knowledge, but on what we might call AI literacy: the ability to work with AI tools thoughtfully, question outputs critically, and integrate them into learning without becoming dependent on them. This is not a peripheral skill. It is becoming central to how knowledge work itself operates. Bearman and Ajjawi (2023) describe this as learning to work with the black box, arguing that pedagogy must now equip students to engage critically with AI outputs rather than simply consume them. That framing resonates strongly with what we observe across institutional contexts.

Here lies an uncomfortable tension, and one we feel is worth naming directly.

Universities have historically acted as institutions of careful validation. Academic systems move deliberately because quality assurance, fairness, and intellectual standards matter. AI, however, evolves at a pace that sits uneasily alongside these slower institutional rhythms. Policies often take years to stabilise; AI tools change within months.

Many educators are trying to teach on moving ground.

We have seen this play out in small but telling ways. A colleague redesigns an assessment to encourage genuine critical engagement with AI outputs, only to find that institutional policy has not yet caught up with what she is attempting. A student experiments independently with AI tools for an assignment, uncertain whether doing so crosses a line that has never been clearly drawn. These are not dramatic failures. They are the quiet, everyday signs of a system under pressure to adapt faster than its structures comfortably allow.

Some faculty members have institutional support, training, and time to experiment with AI-enhanced teaching. Others are expected to respond to rapid technological change without sufficient guidance or resources. The result is that students receive mixed messages: use AI cautiously; engage with it, but do not rely on it; develop AI capabilities, but avoid crossing unclear boundaries that shift depending on the course, the lecturer, or the institution.

This inconsistency risks producing a new kind of educational inequality, one not immediately visible through league tables or infrastructure, but through differing levels of preparedness for AI-mediated professional life.

Importantly, this divide does not follow geography in any simple sense. Within the same country, some universities are moving rapidly while others remain hesitant. Within the same institution, disciplines often take markedly different approaches. Business and computing programmes may integrate AI extensively, while humanities and social sciences continue to debate its legitimacy in assessment altogether. These are not unreasonable differences of perspective. But they do raise questions about whether students are graduating with comparable readiness for workplaces where AI is already present.

The deeper policy question is whether higher education systems are treating AI primarily as a threat to academic integrity or as a genuine transformation in how learning itself occurs.

Concerns about misuse are valid and should not be dismissed. AI can encourage superficial learning if used uncritically. Students with stronger prior knowledge, higher digital confidence, and greater familiarity with the language and cultural assumptions embedded within AI systems are likely to benefit more. Algorithmic bias and cultural mismatch may therefore reinforce existing inequalities rather than reduce them. But restriction alone is unlikely to solve this. It may simply drive usage underground, without the guidance that makes it educationally meaningful.

This is why institutional policy now matters enormously. Universities need frameworks that move beyond the binary of allowing or banning. Students need clearer expectations around when AI use is appropriate, how it should be acknowledged, and what forms of learning still need to happen independently. Emerging guidance from sector bodies, including the QAA and TEQSA, reflects a growing recognition that responsible integration requires practical frameworks rather than purely restrictive approaches. At a global level, UNESCO (2023) guidance for generative AI in education makes precisely this case, calling for policy responses that are contextually sensitive, ethically grounded, and oriented towards equity rather than restriction alone. That direction of travel is encouraging, even if implementation remains uneven.

The risk, as we see it, is not that universities will ignore AI entirely. The risk is that adaptation will happen unevenly, and that this unevenness will quietly shape whose ways of thinking, communicating, and learning are most readily supported by the AI systems graduates will encounter in professional life. Some students will leave university already fluent in working alongside these tools. Others will leave with strong disciplinary knowledge but limited experience of how AI is reshaping the environments they are about to enter.

Higher education has long been understood as a mechanism for reducing inequality through access to knowledge. In the AI era, that mission may need to extend further: towards ensuring equitable access not only to knowledge, but to the conditions in which AI-augmented learning can happen responsibly, critically, and well.

The question for universities and policymakers is not whether to engage with AI. It is whether higher education systems can shape that engagement thoughtfully and equitably, before the divides that are quietly forming become very difficult to close.

References

Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160–1173. https://doi.org/10.1111/bjet.13337

Dastane, O., Turner, J., & Nankervis, A. (2024). The trajectory of artificial intelligence for competency-based personalised learning: Past, present and future. The International Journal of Information and Learning Technology, 41(5), 473–489. https://doi.org/10.1108/IJILT-11-2023-0187

UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://doi.org/10.54675/PCSP7350

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0

About the Authors

Ria Sidhu SFHEA is a member of the Academic Partnerships & TNE team at Bath Spa University, UK, where she leads Assessment Quality Assurance across MSc Global Business, MSc Project Management, the undergraduate Business Management suite (all pathways), and the postgraduate MBA programmes for UK and UAE partners. Drawing on 15 years in UK higher education, her expertise spans higher education policy and governance, quality assurance, transnational education, and EDI-driven pedagogy. Correspondence: r.sidhu@bathspa.ac.uk

Omkar Dastane^* is a Senior Lecturer in Marketing and Director of Postgraduate Studies at the School of Business, Monash University Malaysia. His research examines how emerging technologies are reshaping consumer value hierarchies and redefining marketplace dynamics. A passionate education leader, Dr. Omkar champions immersive learning through AR, VR, and AI, and actively shapes institutional policy on the responsible integration of artificial intelligence in higher education. Correspondence: omkar.dastane@monash.edu

^* Corresponding author

Cite this article

Sidhu, R., & Dastane, O. (2026). The emerging AI divide in higher education. Society and AI. https://societyandai.org/perspectives/the-emerging-ai-divide-in-higher-education/

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