Exploration of adaptive teaching strategies for university education supported by artificial intelligence

The deep integration of artificial intelligence (AI) with higher education offers a new approach to resolving the conflict between large-scale and personalized teaching. This study explores the advantages and implementation strategies of AI-supported adaptive teaching in university education, highlighting how it achieves precise teaching through data-driven learner profiling, dynamic resource matching, and closed-loop feedback mechanisms.The study puts forward three strategies: personalized path recommendation based on learning behavior data, real-time q&A driven by intelligent teaching assistant, and dynamic difficulty adjustment of adaptive test, all of which are highly feasible based on the existing technical framework.