Abstract

With the rapid advancement of artificial intelligence technology, its applications in the educational sector have become increasingly widespread. Higher vocational institutions, as pivotal centers for cultivating applied talent, are undergoing profound transformations in their teaching methods and students' learning approaches. Artificial intelligence, through intelligent learning systems and online education platforms, provides students with more personalized and flexible learning resources and pathways, significantly enhancing their potential for autonomous learning. However, despite the numerous opportunities presented by artificial intelligence, the self-directed learning abilities of students in higher vocational institutions are influenced by various factors. These include individual differences among students, the role of teachers, and the support provided by the learning environment and available resources. This paper aims to explore the multiple factors affecting the autonomous learning capabilities of students in higher vocational institutions within the context of artificial intelligence, offering theoretical support and practical guidance for optimizing teaching strategies and enhancing students' self-directed learning abilities. By reviewing existing literature and analyzing empirical research, it reveals the actual effects of artificial intelligence in education and its specific impact on students' autonomous learning capabilities.