The Challenge of Academic Integrity in the Age of Generative Artificial Intelligence

Abstract

Abstract This study aims to understand the factors influencing academic integrity in the age of generative artificial intelligence (GenAI) through the lens of the Theory of Planned Behavior (TPB). A seven-factor measurement model was developed, hypothesizing the relationship between TPB constructs – e.g., perceived behavioral control (PBC), past behavior (PB), moral obligations (MO), social norms (SN), information literacy (IL)—and GenAI adoption. It was tested using structural equation modeling (SEM). The findings showed that both PBC (β = 0.223, CR = 4.234, p=<0.05) and SN (β = 0.508, CR = 5.644, p=<0.05) significantly increased behavioral intention. MO (β = -0.273, CR = -4.234, p=<0.05) and IL (β = -0.253, CR = -3.386, p=<0.05) helped uphold academic integrity. A strong association was found between behavioral intention (BI) and GenAI adoption (AD) for academic dishonesty (β = 0.651, CR = 11.780, p = 0.000). The findings suggest that protecting academic integrity needs a multi-tiered approach, including implementing AI literacy programs, research, and critical thinking training, implementing strong plagiarism detection tools, developing and maintaining comprehensive and adaptable guidelines for the ethical use of GenAI, citation management training, and cultivating a culture valuing academic honesty and originality.

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