The rapid integration of Generative AI in higher education has transformed teaching and learning, yet limited research explores the factors driving its adoption and impact on academic performance. This study addresses the gap in understanding how ethical principles (fairness, accountability, transparency, accuracy, autonomy) and AI characteristics (perceived anthropomorphism, perceived intelligence) influence students’ use of Generative AI tools and their subsequent academic outcomes. The research aims to develop and test a theoretical model that integrates these factors to explain Generative AI adoption and its effect on perceived academic performance among university students. Data were collected through surveys from 318 students and analyzed via Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results revealed that accountability, transparency, accuracy, autonomy, perceived anthropomorphism, and perceived intelligence significantly drive Generative AI use, while fairness does not. Generative AI use, in turn, is positively associated with academic performance, explaining 49.9% of its variance. These findings advance technology adoption and educational technology research by highlighting the interplay of ethical and technical factors in AI adoption, offering practical insights for educators and developers to optimize AI tools for equitable and effective learning.
Al‐Emran et al. (Thu,) studied this question.