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

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Bibliographic Details
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]
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Database: Psychology and Behavioral Sciences Collection
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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]
ISSN:10447318
DOI:10.1080/10447318.2025.2462083