Research on the Influence Mechanism of AI Adaptive Learning Systems on Students' Self-Efficacy

This paper focuses on the influence mechanism of AI adaptive learning systems on students' self-efficacy. Firstly, it elaborates on the core connotations of AI adaptive learning systems, including being learner-centered, data-driven closed-loop mechanisms, and core objectives. Then, it analyzes the current application situations in higher education and vocational education, as well as the development trends of multi-modal learning support and in-depth industry verticalization. By exploring how AI adaptive learning systems influence students' self-efficacy through approaches such as personalized learning paths, instant feedback and dynamic adjustments, and precise delivery of learning resources, it reveals the underlying action mechanisms. This research helps to gain an in-depth understanding of the relationship between AI adaptive learning systems and students' self-efficacy, providing a theoretical basis for educational practice.