Normalizing Deviance: A Statistical Analysis of Peer Interaction and the Perceived Usefulness of Generative AI in Academic Cheating
Keywords:
academic cheating, artificial intelligence, peer interaction, perceived ussefulnes, differential assoiciation theory.Abstract
The rapid integration of artificial intelligence (AI) in higher education has created ethical dilemmas, blurring the line between utilizing technology as a learning tool and committing academic integrity violations. Consequently, this study investigates how peer interaction and the perceived usefulness of generative AI contribute to academic dishonesty among students. Employing a quantitative approach, data were collected from 81 sociology students selected via stratified random sampling to ensure proportional representation across academic batches using a 10-point rating scale questionnaire and analyzed through multiple linear regression. The results show that both independent variables simultaneously have a significant effect on academic cheating, with the perceived usefulness of AI acting as the most dominant predictor. Theoretically, this research validates differential association theory, illustrating the social learning of deviant behavior, indicating that academic cheating is a social behavior normalized by peer environments and strongly rationalized by the efficiency offered by AI. To address this normalizing deviance and ethically integrate AI into education, institutions must establish explicit guidelines regarding the boundaries of AI usage, redesign assessments to prioritize human critical thinking, and actively foster a strong culture of academic integrity.
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