Exploring student-AI interaction in assessment through the lens of learner agency: case studies in UK higher education
File(s)
Published version (ahead of print)
Author(s)
O’Dea, Xianghan
Chiu, Yuan-Li Tiffany
Bale, Richard
Rossiter, Monika
Type
Journal Article
Abstract
This study explores students-AI interaction, specifically in assessment, through the lens of learner agency, in the context of UK higher education. Using self-regulated learning as the theoretical foundation, this paper used a qualitative approach, namely focus groups, to collect data. Ten focus groups, with forty-two students, were conducted in two Russell Group universities. The findings suggest that even though some have begun moving towards challenging AI outputs and engaging in reflection on the responses provided, the current usage of AI among students appears to be mainly for information retrieval, and/or for accelerating certain routine tasks in their learning. This level of usage therefore only strengthens lower-level thinking skills. The significance of the paper lies in two areas. Firstly, we propose a framework showing the dimensions of student-AI interaction and the connection with self-regulated learning. This framework will influence policy, practice and guidance on AI integration in learning and teaching in higher education. And secondly, this paper serves as guidance on how policy makers and educators can encourage and support effective AI usage among students throughout their learning process.
Date Issued
2026-07-17
Date Acceptance
2026-07-02
Citation
Studies in Higher Education, 2026
ISSN
0307-5079
Publisher
Informa UK Limited
Journal / Book Title
Studies in Higher Education
Copyright Statement
© 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Publication Status
Published online
Date Publish Online
2026-07-17