The Mechanism of Artificial Intelligence-Empowered Personalized Learning Systems on University Students’ STEM Learning Motivation and Academic Achievement: An Educational Psychology Perspective

Abstract

With the continuous advancement of artificial intelligence (AI) technology, personalized learning systems are increasingly applied in higher education. Particularly within STEM (Science, Technology, Engineering, and Mathematics) education, AI demonstrates significant advantages through adaptive learning pathways, instant feedback, and individualized resource allocation. However, current research predominantly focuses on the technical architecture and application effectiveness of such systems, with insufficient exploration of how AI-enabled personalized learning systems influence university students’ learning motivation and academic achievement through educational psychological mechanisms. This paper adopts an educational psychology perspective to construct a causal mechanism model linking “learning motivation–learning behavior–academic achievement.” Findings indicate that AI-powered personalized learning systems enhance learning autonomy, boost self-efficacy, and optimize feedback mechanisms. These effects collectively stimulate university students’ learning motivation in STEM disciplines, thereby promoting academic achievement. Building upon empirical research, this paper proposes implications for educational practice and policy formulation, emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.