THE RELIANCE ON ARTIFICIAL INTELLIGENCE AS A PREDICTOR OF ACADEMIC INTEGRITY AMONG HIGHER EDUCATION STUDENTS

Abstract

The rapid proliferation of generative artificial intelligence (GenAI) has profoundly transformed teaching, learning, and assessment practices, creating unprecedented challenges for academic integrity in higher education. This study investigates the relationships between the level of AI usage, AI literacy, academic stress, and reliance on AI, as well as the implications of AI reliance for academic integrity awareness among pre-service teachers. Adopting a quantitative cross-sectional survey design, data were collected from 350 students enrolled in teacher education programs and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The proposed model further examined the mediating roles of critical thinking and academic self-efficacy in the relationship between AI reliance and academic integrity awareness. The findings indicate that academic stress and the level of AI usage significantly increase students' reliance on AI, whereas AI literacy significantly reduces such dependence. Furthermore, reliance on AI exerts a significant negative effect on academic integrity awareness. The mediation analysis demonstrates that critical thinking and academic self-efficacy significantly mitigate the adverse effects of AI reliance on integrity awareness. The results suggest that AI reliance extends beyond instrumental use and increasingly functions as a normative reference and epistemic authority, contributing to the emergence of phenomena such as delegated ethics and the illusion of competence. This study concludes that strengthening critical AI literacy, implementing authentic assessment practices, and reinforcing human agency are essential for preserving intellectual autonomy, ethical accountability, and professional identity among future educators in the digital era.