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Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study.

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Title: Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study.
Authors: Wang, Guohua1,2 (AUTHOR) wgh19892008@126.com, Tian, Lianghao1 (AUTHOR), Xing, Xueru1 (AUTHOR)
Source: Interactive Learning Environments. Jul2026, Vol. 34 Issue 5, p3358-3372. 15p.
Subjects: Academic fraud, Student cheating, Overpressure (Education), Planned behavior theory, Generative artificial intelligence, College students, Mixed methods research, Self-efficacy
Abstract: The emergence of Generative Artificial Intelligence (GAI) has provided college students with tools to mitigate academic stress but has also facilitated AI-assisted cheating. However, existing research offers limited insight into how academic stress influences cheating behaviours and the underlying psychological mechanisms, leaving a significant gap in understanding. This study aims to develop and validate a theoretical model explaining how academic stress influences college students' intentions to engage in AI-assisted cheating. Grounded in the Theory of Planned Behaviour (TPB), this research employed a mixed-methods approach. Quantitative data were analysed to explore relationships among academic stress, academic self-efficacy, attitudes toward AI-assisted cheating, and cheating intentions. A survey analyzed 458 valid questionnaires from students, while follow-up qualitative interviews with 21 students who admitted to using AI to assist in cheating provided in-depth analysis. Findings indicate that academic stress directly influences students' AI-assisted cheating intentions and indirectly does so through academic self-efficacy and attitudes as mediators. Qualitative interviews reinforced these findings by offering contextual explanations. This study reveals both direct and indirect pathways linking academic stress to AI-assisted cheating, contributing to a deeper theoretical and practical understanding of academic misconduct in the GAI era. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The emergence of Generative Artificial Intelligence (GAI) has provided college students with tools to mitigate academic stress but has also facilitated AI-assisted cheating. However, existing research offers limited insight into how academic stress influences cheating behaviours and the underlying psychological mechanisms, leaving a significant gap in understanding. This study aims to develop and validate a theoretical model explaining how academic stress influences college students' intentions to engage in AI-assisted cheating. Grounded in the Theory of Planned Behaviour (TPB), this research employed a mixed-methods approach. Quantitative data were analysed to explore relationships among academic stress, academic self-efficacy, attitudes toward AI-assisted cheating, and cheating intentions. A survey analyzed 458 valid questionnaires from students, while follow-up qualitative interviews with 21 students who admitted to using AI to assist in cheating provided in-depth analysis. Findings indicate that academic stress directly influences students' AI-assisted cheating intentions and indirectly does so through academic self-efficacy and attitudes as mediators. Qualitative interviews reinforced these findings by offering contextual explanations. This study reveals both direct and indirect pathways linking academic stress to AI-assisted cheating, contributing to a deeper theoretical and practical understanding of academic misconduct in the GAI era. [ABSTRACT FROM AUTHOR]
ISSN:10494820
DOI:10.1080/10494820.2025.2565684