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The Role of Generative AI in Education: Questions, Reflections, and Provocations

Jonathan Jackson asks, if GenAI tools can both benefit and harm students’ learning, where does this leave us as educators?

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Let me open with a pair of deceptively simple questions. Firstly, can Generative Artificial Intelligence (GenAI) tools benefit students’ learning? Evidently, yes. (Kasneci et al., 2023) Secondly, can GenAI harm students’ learning? Also, yes. (Bastani et al., 2024) So where does this leave us as educators? Should we heed the clarion call to embrace GenAI and embed it everywhere we can, or should we reject it? Of course, reality isn’t as dichotomous.

To illustrate the point, if I reflect on the question of whether I am pro-GenAI or anti-GenAI, my answer is: both. It depends on the context. I have certainly drawn my own lines in the sand of what types of GenAI tools and usage I find personally acceptable, but I expect to continually redraw those lines as I creep forward through the sandstorm of contemporary higher education in the age of GenAI (hoping not to get too much grit in my eyes).

A key question that drives my teaching practice in relation to GenAI is: how can I maximise the potential benefits to students and minimise the potential harm? This is not a simple question, and I continue to grapple with the complexities behind it, but this won’t stop me asking it. It is my duty to ask it.

The potential benefits of GenAI for students can be highly subjective and will vary depending on who you ask. If we use the lens of employability, there is growing demand for graduates to join the workforce with AI literacy that helps them be more productive employees. This expectation is not only being set by the private sector but has been articulated clearly by the UK government as part of their vision of wider AI adoption “to boost economic growth, provide jobs for the future and improve people's everyday lives” (Department for Science, Innovation & Technology, 2025) and how education has an important part to play in supporting this (Department for Education, 2025).

The use of GenAI can, indeed, deliver productivity gains in various types of knowledge work (Dell’Acqua et al., 2023), but at what cost? We can’t be sure about the long-term cognitive or societal effects of overreliance on GenAI. Might we be risking a whole generation’s ability to develop higher-order thinking skills (HOTS) in exchange for (hoped) productivity and profitability? Impossible to know for sure. Important to think about regardless.

That said, I can only dwell on these questions for so long before an existential malaise threatens to envelop me. (AI Ethicists, I see you!) Focusing on smaller scale practical interventions tends to bring me back to a more positive mindset and, while beyond the scope of this article, I intend to continue sharing personal examples of GenAI usage that I believe to be net-beneficial, and I would encourage other educators to do the same. Tap into communities of practice inside and outside your own organisation to learn and share. Involve students in this collaborative endeavour as we will continue to learn a lot from them.

As educators, we have both a duty and an opportunity to explore the continually evolving frontiers of GenAI capabilities within the contexts of our disciplines and help minimise harms and maximise pedagogical benefits (Jackson, 2025). Might this seem daunting? Yes. Exciting? Also, yes.

References

Bastani, H. et al. (2024) ‘Generative AI Can Harm Learning’. Rochester, NY. Available at: https://papers.ssrn.com/abstract=4895486 (Accessed: 17 July 2024).

Dell’Acqua, F. et al. (2023) ‘Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality’. Rochester, NY. Available at: https://doi.org/10.2139/ssrn.4573321.

Department for Education (2025) Generative artificial intelligence (AI) in education, GOV.UK. Available at: https://www.gov.uk/government/publications/generative-artificial-intelligence-in-education/generative-artificial-intelligence-ai-in-education (Accessed: 17 June 2025).

Department for Science, Innovation & Technology (2025) AI Opportunities Action Plan. Available at: https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan (Accessed: 17 June 2025).

Jackson, J. (2025) ‘Higher order prompting: Applying Bloom’s revised taxonomy to the use of large language models in higher education’, Studies in Technology Enhanced Learning, 4(1). Available at: https://doi.org/10.21428/8c225f6e.0915c17e.

Kasneci, E. et al. (2023) ‘ChatGPT for good? On opportunities and challenges of large language models for education’, Learning and Individual Differences, 103, p. 102274. Available at: https://doi.org/10.1016/j.lindif.2023.102274.

About the author

Jonathan Jackson, Queen Mary University of London, London, United Kingdom

Jonathan Jackson is a Senior Lecturer in Software Engineering and Management with a background in industry and has Chartered IT Professional status from BCS. He is currently Programme Director for the Digital and Technology Solutions undergraduate and postgraduate degree apprenticeship programmes at Queen Mary University of London. Jonathan’s scholarly interests include Digitally Enhanced Teaching and Learning, Large Language Models (LLMs) in education, and authentic assessment through industry collaboration.

Email: jonathan.jackson@qmul.ac.uk

ORCID: https://orcid.org/0009-0005-3437-8081

BlueSky: https://bsky.app/profile/iamjonjackson.bsky.social

 

 

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