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
- 1Faculty of Education, Hanoi University of Science and Technology, Hanoi, Vietnam
Abstract
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.
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BibTeX
@article{nguyen2026perceived,
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}},
author = {Hanh Van Nguyen and Mai Thi-Thuy Duong},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0359549},
url = {https://doi.org/10.1371/journal.pone.0359549},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - 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
AU - Hanh Van Nguyen
AU - Mai Thi-Thuy Duong
PY - 2026
DA - 2026/09/28
JO - PLOS One
DO - 10.1371/journal.pone.0359549
UR - https://doi.org/10.1371/journal.pone.0359549
AB - 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.
SN - 1932-6203
ER -