Promote, prevent, and prosecute: a framework to counteract AI-based cheating in educational assessments

Guy J. Curtis and Joseph Clare

Chapter 10 in A Research Agenda for Artificial Intelligence and Academic Integrity, 2026, pp 140-155 from Edward Elgar Publishing

Abstract: This chapter presents an empirically and theoretically derived framework for counteracting cheating. Research from psychology, criminology, education, and economics indicates that there are individual differences among students that underlie their choice to cheat, or not cheat, on educational assessments. These individual differences mean that no single strategy can counteract cheating. Although educators and educational institutions do not know individual students’ motives for cheating or not cheating, they must recognize that multiple methods of cheating prevention are required because all students are different. Specifically, academic integrity must be promoted through policy, honour codes, and education; cheating must be prevented via assessment security; and detected breaches of academic integrity rules must be enforced (or prosecuted). The chapter focuses on applying this framework to counteracting AI-based cheating but is more broadly applicable to other forms of academic misconduct.

Keywords: Academic integrity; Academic misconduct; Generative AI; Contract cheating; Individual differences; Situational crime prevention (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035343638
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