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Factors affecting accounting students’ misuse of chatgpt: an application of the fraud triangle theory

Hashem Alshurafat, Mohannad Obeid Al Shbail, Allam Hamdan, Ahmad Al-Dmour and Waed Ensour

Journal of Financial Reporting and Accounting, 2023, vol. 22, issue 2, 274-288

Abstract: Purpose - This study aims to explore the factors that contribute to student academic dishonesty through an examination of the misuse of AI language models. Using the fraud triangle theory, which posits that opportunity, rationalization and pressure are key factors for fraudulent behavior, this study investigates how these elements interact and contribute to academic dishonesty among students. Design/methodology/approach - In this study, data on how accounting students used ChatGPT to cheat was acquired from 279 accounting students in Jordanian public universities over the course of two months, from January 2023 to March 2023, through previously tested and validated questionnaires. The main tool for gathering data was a questionnaire distributed online using Microsoft Forms. Findings - The results show that all of the fraud triangle factors are significant determinants of student academic dishonesty and student misuse of ChatGPT. The findings of this research can be used to guide the development of technology-based preventative measures. Originality/value - This study provides valuable insights into the motivations and factors that drive students to engage in academic dishonesty and sheds light on the broader issue of technology-assisted academic dishonesty and its impact on the educational system. This study’s contribution is significant, as it sheds light on a pressing issue in education and provides valuable information for educators and policymakers to address the problem and improve academic standards.

Keywords: Academic dishonesty; AI language models; Fraud triangle theory; Technology-assisted cheating; Academic integrity; ChatGPT (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jfrapp:jfra-04-2023-0182

DOI: 10.1108/JFRA-04-2023-0182

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