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

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.

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Publication Details

Journal
International Journal of Human-Computer Interaction
Published
2025-02-18
DOI
https://doi.org/10.1080/10447318.2025.2462083
Citations
10
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
16.94
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article

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

Moritz Jörling, Leonhard Reiter, ROBERT M. BOHM, Christoph Fuchs
10 citations
International Journal of Human-Computer Interaction
Online Learning and Analytics
16.94
article

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

Moritz Jörling, Leonhard Reiter, ROBERT M. BOHM, Christoph Fuchs
article en
10 citations

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.

International Journal of Human-Computer InteractionVol. 41(19)
Vienna University of Economics and Business (AT), University of Copenhagen (DK), University of Vienna (AT), École de management de Lyon (FR)
Openalex Percentile: Top 1%
Online Learning and Analytics
16.94
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