2026-10-11T11:55:50Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34015734 2026-09-28T17:24:54Z category_13 category_734 category_61 category_64 category_106 category_122 portal_5 item_type_3 month_year_09_2026

<p>Model fit indices for the structural model.</p> Hanh Van Nguyen (25132545) Mai Thi-Thuy Duong (25132548) Genetics Biological Sciences not elsewhere classified Developmental Biology Cancer Science Policy Mental Health generative artificial intelligence confirmatory factor analysis students &# 8217 male students tending 863 undergraduate students reported cheating behaviors factor structure consisting made academic cheating assisted academic cheating stem students tending perceived factors associated also positively associated difficulty detecting ai stem students factors associated positively associated female students latent structure academic pressure significantly associated reported ai xlink "> technological affordances student seniority structural model results supported resulting constructs report survey rapid development peer comparison model accounted institutional guidance generated work findings suggest ethical ambiguity approximately 5 appc ). 622 ). 022 ). 008 ), 001 ), <div><p>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.</p></div> 2026-09-28T17:24:39Z Dataset Dataset 10.1371/journal.pone.0359549.t010 https://figshare.com/articles/dataset/_p_Model_fit_indices_for_the_structural_model_p_/34015734 CC BY 4.0