Discrepancies Between Self-Reported and Peer-Attributed Frequencies of AI-Assisted Academic Cheating Among Undergraduate Students

Generative artificial intelligence (GenAI) has intensified concerns about academic integrity, yet little is known about discrepancies between students’ self-reported frequencies in AI-assisted academic cheating and the frequencies they attribute to their peers. Using an anonymous cross-sectional survey, this study collected responses from 863 undergraduates at a science and technology university in Vietnam. Participants rated ten AI-assisted academic cheating behaviors from two perspectives: the frequency they attributed such behaviors to their classmates, referred to as perceived peer cheating (PPC), and the frequency they reported such behaviors for themselves, referred to as self-reported cheating (SRC). Split-plot ANOVAs were conducted to compare PPC and SRC ratings and to examine whether the magnitude of the discrepancy varied by gender and student seniority. The results show that PPC ratings were significantly higher than SRC ratings for all ten behaviors, with partial eta-squared values ranging from 0.060 to 0.196. Cheating perspective × gender interactions and cheating perspective × student seniority interactions were each significant for three behaviors; however, all interaction effects were small (ηp2 ≤ 0.025). Overall, the study reveals a consistent discrepancy between self-reported and peer-attributed frequencies of AI-assisted academic cheating. This discrepancy highlights the limitations of using either direct self-reports or peer-attributed frequencies alone to estimate the actual prevalence of AI-assisted academic cheating.