Ethical uses of generative AI in assessment: student perceptions in UK contexts
File(s)
Author(s)
Type
Journal Article
Abstract
University students are increasingly turning to Generative Artificial Intelligence (GenAI) tools for help with their academic assessments, which has prompted major concerns relating to academic integrity. While Universities globally are building guidance on good practice in the use of GenAI, there is a lack of empirical understanding of student perceptions of what ethical and equitable use means to them. Developing insight into student understanding of GenAI is important in enabling institutions to offer more appropriate training and support to encourage good practice in assessment, not just from the perspective of avoiding malpractice, but in identifying ethical opportunities for integrating the technology as a useful tool. Using the GenAI literacy framework as the theoretical foundation, this paper analysed students’ GenAI usage in academic assessments within the UK context. Data were collected from 80 participants through focus groups conducted by four UK institutions. Our findings show that students are exploring the potential of GenAI as a tool and are beginning to understand where the ethical boundaries might sit. They are keen to use the technology to support their learning but have significant concerns about locating that boundary between good practice and that which exposes them to accusations of cheating, or which limits their own learning. The paper reinforces the importance of providing GenAI literacy training to university students, so they may develop a better understanding of how GenAI can support learning processes in an ethical way.
Date Issued
2025-11-17
Date Acceptance
2025-11-07
Citation
Evaluation Review, 2025
ISSN
0193-841X
Publisher
SAGE Publications
Journal / Book Title
Evaluation Review
Copyright Statement
© The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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Publication Status
Published online
Date Publish Online
2025-11-17