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Student (Mis)Use of Generative AI Tools for University-Related Tasks.

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Title: Student (Mis)Use of Generative AI Tools for University-Related Tasks.
Authors: Reiter, Leonhard (AUTHOR), Jörling, Moritz (AUTHOR), Fuchs, Christoph (AUTHOR), Böhm, Robert (AUTHOR)
Source: International Journal of Human-Computer Interaction. Oct2025, Vol. 41 Issue 19, p12390-12403. 14p.
Subjects: Generative artificial intelligence, Technology Acceptance Model, College environment, Academic fraud, Higher education, Student assignments, Psychology of students
Abstract: Although Artificial Intelligence (AI) holds immense potential to enhance the educational experience, its use also presents challenges. This research examines the use and misuse of AI tools for university-related tasks. We surveyed 498 students from three faculties at a large European university to, first, identify factors driving their willingness to use AI tools for university-related tasks, and, second, estimate the prevalence of cheating behavior involving the unauthorized use of AI tools in examinations. Specifically, we tested and extended the Technology Acceptance Model 2 (TAM2) by identifying trust and perceived opportunity costs as additional determinants of using AI tools for university-related tasks. To estimate the proportion of students cheating during examinations, we applied a randomized response technique. We discuss the results with respect to the effective and appropriate implementation of AI tools in higher education. Our findings can help educators and policymakers to promote responsible AI use while mitigating its misuse. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction 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: Psychology and Behavioral Sciences Collection
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  Data: Student (Mis)Use of Generative AI Tools for University-Related Tasks.
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  Data: <searchLink fieldCode="AR" term="%22Reiter%2C+Leonhard%22">Reiter, Leonhard</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jörling%2C+Moritz%22">Jörling, Moritz</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fuchs%2C+Christoph%22">Fuchs, Christoph</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Böhm%2C+Robert%22">Böhm, Robert</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Oct2025, Vol. 41 Issue 19, p12390-12403. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Acceptance+Model%22">Technology Acceptance Model</searchLink><br /><searchLink fieldCode="DE" term="%22College+environment%22">College environment</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+fraud%22">Academic fraud</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Student+assignments%22">Student assignments</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology+of+students%22">Psychology of students</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Although Artificial Intelligence (AI) holds immense potential to enhance the educational experience, its use also presents challenges. This research examines the use and misuse of AI tools for university-related tasks. We surveyed 498 students from three faculties at a large European university to, first, identify factors driving their willingness to use AI tools for university-related tasks, and, second, estimate the prevalence of cheating behavior involving the unauthorized use of AI tools in examinations. Specifically, we tested and extended the Technology Acceptance Model 2 (TAM2) by identifying trust and perceived opportunity costs as additional determinants of using AI tools for university-related tasks. To estimate the proportion of students cheating during examinations, we applied a randomized response technique. We discuss the results with respect to the effective and appropriate implementation of AI tools in higher education. Our findings can help educators and policymakers to promote responsible AI use while mitigating its misuse. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction 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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        Value: 10.1080/10447318.2025.2462083
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      – Code: eng
        Text: English
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        PageCount: 14
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        Type: general
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      – SubjectFull: College environment
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      – SubjectFull: Higher education
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              Text: Oct2025
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