AI or nay? How students balance the benefits and limitations of generative AI in higher education

Hands, Caroline ORCID: 0000-0002-0898-8258, Barton, Charlotte, Coulby, Ceridwen, Qi Saw, Qi, Farrell, Amy, Stevens, Catherine, Ellison, Claire, Saunders, Samuel ORCID: 0000-0002-7515-9214 and Lindsay, Rob (2026) AI or nay? How students balance the benefits and limitations of generative AI in higher education Developing Academic Practice, 2026 (Specia). pp. 115-128. ISSN 2732-5725, 2732-5725

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Abstract

The rapid growth in awareness of artificial intelligence (AI) technologies since late 2022 has significantly impacted academia, with generative AI (GenAI) tools becoming increasingly prevalent in educational settings. Universities are struggling to keep up with the rapid changes in GenAI capabilities, creating an environment where staff and students do not have the necessary support and understanding to use these tools responsibly. The present study aims to develop understanding of how students are engaging with GenAI within the university context. Seven students (five undergraduate and two postgraduate), recruited as part of a wider questionnaire study, participated in an online interview to discuss their experiences of using GenAI in an educational context. Deductive thematic analysis was used to identify and analyse the following five themes: i) usage and perception of GenAI tools, ii) academic support and learning, iii) academic integrity and policy, iv) ethical use, and v) creativity and inspirational uses. It was found that the prevalence of GenAI use is often based on previous experience with such tools. However, this is mediated by local and wider-level guidance. Ethical concerns surrounding data use and academic integrity reduce student use. The findings shed light on student experience of GenAI, highlighting the opportunities and challenges in an academic setting. This has implications for the development of university and departmental level policies relating to the use of GenAI by students. This article was published open access under a CC BY licence: https://creativecommons.org/licenses/by/4.0/ .

Item Type:	Article
Uncontrolled Keywords:	3903 Education Systems, 3904 Specialist Studies In Education, 39 Education, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), 8.3 Policy, ethics, and research governance, 4 Quality Education
Divisions:	Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Population Health Faculty of Health & Life Sciences > Inst. Population Health > Psychology
Depositing User:	Symplectic Admin
Date Deposited:	09 Jun 2026 07:20
Last Modified:	09 Jun 2026 07:20
DOI:	10.3828/dap.2026.16
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3198840
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