The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study
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.
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Cites
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ChatGPT for good? On opportunities and challenges of large language models for education
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“However, the popularity of GAI has also raised a number of ethical issues in education, especially the growing concerns about academic integrity and students’ ability to learn independently (Kasneci et al., 2023; Michel-Villarreal et al., 2023).”
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Metadata
- Authors
- Wang, G., Tian, L., & Xing, X.
- Venue
- Interactive Learning Environments
- Publisher
- Informa UK Limited
- Published
- 2025-10-08
- Volume
- 34
- Issue
- 5
- Pages
- 3358-3372
- ISSN
- 1049-4820
- 1744-5191
- Cited by
- 1
- References
- 51
- Language
- en
- Licence
- doi.org/10.1080/policies-text-data-mining (tdm)
- Funders
- Educational Science Research Fund of Henan Normal University (2023JK12)
- Research Fund for Educational Strength of Henan Province in Philosophy and Social Sciences (2025JYQS0222)
- Key Research Fund of Higher Education Institutions in Henan Province (25B880002)
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- Wang, G., Tian, L., & Xing, X. (2026). Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study. Interactive Learning Environments. https://doi.org/10.1080/10494820.2025.2565684
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
