{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:59:44Z","timestamp":1790618384845,"version":"4.1.0"},"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hanoi University of Science and Technology","award":["T2024-PC-003"],"award-info":[{"award-number":["T2024-PC-003"]}],"id":[{"id":"https:\/\/ror.org\/04nyv3z04","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats: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\u2019 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 (\u03b2\u2009=\u20090.228, p\u2009&lt;\u20090.001), whereas APPC showed a small negative association (\u03b2\u2009= \u22120.148, p\u2009=\u20090.022). Gender was also positively associated with AICB (\u03b2\u2009=\u20090.142, p\u2009&lt;\u20090.001), with male students tending to report higher AICB scores than female students. Student seniority was not significantly associated with AICB (\u03b2\u2009=\u20090.018, p\u2009=\u20090.622). Field of study was positively associated with AICB (\u03b2\u2009=\u20090.104, p\u2009=\u20090.008), 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0359549","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:24:33Z","timestamp":1790616273000},"page":"e0359549","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Perceived factors associated with self-reported AI-assisted academic cheating among undergraduate students: Evidence from ethical ambiguity, technological affordances, institutional gaps, academic pressure, and peer comparison"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-4151","authenticated-orcid":true,"given":"Hanh Van","family":"Nguyen","sequence":"first","affiliation":[{"name":"Hanoi University of Science and Technology","place":["Hanoi, Vietnam"],"department":["Faculty of Education"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2232-7998","authenticated-orcid":true,"given":"Mai Thi-Thuy","family":"Duong","sequence":"additional","affiliation":[{"name":"Hanoi University of Science and Technology","place":["Hanoi, Vietnam"],"department":["Faculty of Education"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"issue":"7","key":"pone.0359549.ref001","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1080\/10508422.2020.1817746","article-title":"Examining the relationship between student attitude and academic cheating","volume":"31","author":"H Yu","year":"2020","journal-title":"Ethics Behav"},{"issue":"3","key":"pone.0359549.ref002","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1207\/S15327019EB1103_5","article-title":"Attitudes toward, and intentions to report, academic cheating among students in Singapore","volume":"11","author":"VKG Lim","year":"2001","journal-title":"Ethics Behav"},{"key":"pone.0359549.ref003","first-page":"2939","article-title":"Artificial intelligence: threat or asset to academic integrity? 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