Research on Restructuring the Piano Lesson Teaching Model in the Context of Artificial Intelligence

With the vigorous development of artificial intelligence (AI) in education, traditional piano teaching faces challenges such as low personalization, delayed feedback, and underutilization of classroom resources. Based on a systematic review of traditional piano teaching models and AI applications in education, and informed by cognitive psychology and intelligent teaching theories, this study employs a combination of questionnaires, in-depth interviews, and teaching experiments to analyze the current state and problems of piano classrooms. On this basis, we construct a restructured teaching model that integrates an intelligent teaching platform, personalized learning paths, and adaptive feedback, and conduct practical implementation and effectiveness evaluation in representative schools and student groups. The results show that this model significantly enhances student engagement, optimizes classroom management, and improves practice efficiency, while also revealing limitations in platform maturity and teacher training requirements. Finally, we discuss the feasibility and considerations for wider adoption of the model and propose future research directions in multimodal interaction and interdisciplinary integration.