Decoding academic integrity policies: A corpus linguistics investigation of AI and other technological threats

Perkins, Mike; Roe, Jasper

Abstract

This study presents a corpus analysis of academic integrity policies from Higher Education Institutions (HEIs) worldwide, exploring how they address the issues posed by technological threats, such as Automated Paraphrasing Tools and generative-artificial intelligence tools, such as ChatGPT. The analysis of 142 policies conducted in November and December 2022, and May 2023 reveals a gap regarding the mention of AI and associated technologies in the available academic integrity policies. Despite the growing prevalence of these tools in the 6-month period since the release of ChatGPT, no HEIs had produced revised academic integrity policies. Content analysis of 53 guidance documents produced by HEIs suggests an overall positive focus of Gen AI tools, yet advises caution. This study suggests a modification to Bretag et al.’s (Int J Educ Integr 7, 2011) exemplary academic integrity model, introducing “Technological Explicitness” — emphasizing the need to include explicit guidelines about new technologies in academic integrity policies. These results underscore the urgent need for HEIs to revise their academic integrity policies, considering the evolving landscape of AI and its implications for academic integrity. This paper argues for a multifaceted approach to deal with the issues of integrating technology, education, policy reform, and assessment restructuring to navigate these challenges while upholding academic integrity.

Citation

Perkins, M., & Roe, J. (2024). Decoding academic integrity policies: A corpus linguistics investigation of AI and other technological threats. Higher Education Policy, 37, 633-653. https://doi.org/10.1057/s41307-023-00323-2

• Journal Article Type: Article
• Acceptance Date: Jun 1, 2023
• Online Publication Date: Jul 17, 2023
• Publication Date: 2024-09
• Deposit Date: Jan 29, 2025
• Journal: Higher Education Policy
• Print ISSN: 0952-8733
• Electronic ISSN: 1740-3863
• Publisher: Palgrave Macmillan
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Pages: 633-653
• DOI: https://doi.org/10.1057/s41307-023-00323-2
• Public URL: https://durham-repository.worktribe.com/output/3355727