AI Insight
This study surveyed 863 undergraduate students in Vietnam to identify factors associated with self-reported AI-assisted academic cheating. The research found that ethical ambiguity around AI use, technological affordances of AI tools, and institutional gaps in guidance were positively associated with cheating behaviors, while academic pressure and peer comparison showed a small negative association. Male students and non-STEM students reported higher rates of AI-assisted cheating, though the overall model explained only 5% of the variance in cheating behaviors.
Why it matters
The findings highlight the need for clearer institutional policies and ethical guidelines regarding acceptable AI use in academic settings as generative AI becomes more prevalent. Understanding these factors can help universities develop more effective interventions to maintain academic integrity while accommodating legitimate educational uses of AI technology.
Understand the Science
by Hanh Van Nguyen, Mai Thi-Thuy Duong
The rapid development of generative artificial intelligence (AI) has made academic cheating in higher education increasingly complex and difficult to regulate. Using a cross-sectional self-report survey of 863 undergraduate students at a university of science and technology in Vietnam, this study examined the latent structure of students’ perceptions of factors associated with AI-assisted academic cheating and the associations of the resulting constructs with self-reported cheating behaviors. Data were analyzed using exploratory factor analysis, confirmatory factor analysis, and covariance-based structural equation modeling. The results supported a three-factor structure consisting of AI-Assisted Academic Cheating Behaviors (AICB), Ethical Ambiguity, Technological Affordances, and Institutional Gaps (ETIG), and Academic Pressure and Peer Comparison (APPC). In the structural model, ETIG was positively associated with AICB (β = 0.228, p < 0.001), whereas APPC showed a small negative association (β = −0.148, p = 0.022). Gender was also positively associated with AICB (β = 0.142, p < 0.001), with male students tending to report higher AICB scores than female students. Student seniority was not significantly associated with AICB (β = 0.018, p = 0.622). Field of study was positively associated with AICB (β = 0.104, p = 0.008), with non-STEM students tending to report higher AICB scores than STEM students. The model accounted for approximately 5% of the variance in AICB, indicating modest explanatory power. Overall, the findings suggest that unclear boundaries around acceptable AI use, ease of access to AI tools and difficulty detecting AI-generated work, gaps in institutional guidance, and academic pressure and peer comparison may warrant further examination in relation to AI-assisted academic cheating.