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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]
Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Guohua%22">Wang, Guohua</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wgh19892008@126.com</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Lianghao%22">Tian, Lianghao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xing%2C+Xueru%22">Xing, Xueru</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Interactive+Learning+Environments%22">Interactive Learning Environments</searchLink>. Jul2026, Vol. 34 Issue 5, p3358-3372. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Academic+fraud%22">Academic fraud</searchLink><br /><searchLink fieldCode="DE" term="%22Student+cheating%22">Student cheating</searchLink><br /><searchLink fieldCode="DE" term="%22Overpressure+%28Education%29%22">Overpressure (Education)</searchLink><br /><searchLink fieldCode="DE" term="%22Planned+behavior+theory%22">Planned behavior theory</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22College+students%22">College students</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+methods+research%22">Mixed methods research</searchLink><br /><searchLink fieldCode="DE" term="%22Self-efficacy%22">Self-efficacy</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Interactive Learning Environments is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/10494820.2025.2565684
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 3358
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      – SubjectFull: Academic fraud
        Type: general
      – SubjectFull: Student cheating
        Type: general
      – SubjectFull: Overpressure (Education)
        Type: general
      – SubjectFull: Planned behavior theory
        Type: general
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: College students
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      – SubjectFull: Mixed methods research
        Type: general
      – SubjectFull: Self-efficacy
        Type: general
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      – TitleFull: Mechanisms of academic stress affecting AI-assisted cheating behaviour in college students: a mixed methods study.
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            NameFull: Wang, Guohua
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            NameFull: Tian, Lianghao
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            NameFull: Xing, Xueru
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            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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