Understanding How Large Language Models Influence Student Motivation and Academic Performance: A Behavioral Framework for Sustainable Education

Large language models (LLMs) have been widely adopted in educational settings, particularly among university students. However, the behavioral mechanisms through which these systems influence academic outcomes remain insufficiently understood. This study develops and empirically tests a framework explaining how the technological attributes of LLMs—perceived usefulness, ease of use, system reliability, accessibility, and interface design—affect student motivation and personalization, which foster anthropomorphic perception and enhance self-efficacy and academic performance. Data were collected from university students in Saudi Arabia using a structured survey and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that technological attributes positively influence motivation and personalization, which strengthen anthropomorphism and subsequently improve self-efficacy and academic performance. The results provide practical insights into the effective application of LLMs in higher education and highlight the role of generative AI in supporting sustainable educational practices.