Deep Integration of Artificial Intelligence and University Physics Teaching: Exploring Innovative Teaching Models

Abstract

The rapid development of Artificial Intelligence (AI) is profoundly influencing the teaching models and methods in higher education, particularly demonstrating broad application prospects in the field of physics teaching. Traditional university physics teaching faces certain limitations in content delivery, experimental teaching, and learning assessment. The introduction of AI technology can effectively enhance teaching quality, optimize learning paths, and promote the development of personalized education. This paper first analyzes the current situation and challenges of university physics teaching and explores the application trends of AI in education and its potential value in empowering physics teaching. Then, it systematically discusses the innovative approaches of AI-driven university physics teaching from the perspectives of teaching model transformation, interdisciplinary integration, and teacher capability enhancement. Finally, the paper proposes corresponding optimization strategies for the potential challenges encountered in the application of AI in physics teaching and anticipates the future development trends of AI in university physics education.