Uncovering AI-related risks in education: evidence from archival incident reports

Lee, Carmen Kar Hang, Ho, G. T. S., Tsang, Y.P. and Tse, Ying Kei ORCID: https://orcid.org/0000-0001-6174-0326 2026. Uncovering AI-related risks in education: evidence from archival incident reports. Presented at: 7th International Conference on Information Technology and Education Technology, Hiroshima, Japan, 5-7 June 2026. Proceedings of 2026 7th ITET. IEEE, pp. 180-184. 10.1109/itet70052.2026.11650905

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Abstract

The rapid development of artificial intelligence (AI) is transforming the education sector. While the benefits of AI adoption in education have been widely discussed, it is increasingly recognised that such technological advances also entail costs and risks. Yet, existing research on AI-related risks in education remains fragmented and predominantly conceptual, with limited empirical evidence drawn from real-world incidents. This study addresses this gap by analysing AI-related incident reports in the education sector using topic modelling techniques. The findings reveal that AI-related risks arise across both pedagogical functions, such as performance assessment and academic integrity enforcement, and non-pedagogical functions, including admissions decisions and security monitoring. These results highlight that AI risks in education extend beyond classroom-level applications and are embedded in broader institutional decision-making processes. This study contributes to the responsible AI literature, offering insights for educators, institutions, and policymakers seeking to better anticipate, govern, and mitigate AI-related risks in education.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Business (Including Economics)
Publisher:	IEEE
ISBN:	9798331564742
Last Modified:	24 Aug 2026 11:33
URI:	https://orca.cardiff.ac.uk/id/eprint/189149

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