Artificial Intelligence in Music Education: A Scoping Review of Practices, Strategies, and Challenges

Abstract

This review uses the PRISMA framework for a scoping review, including 27 empirical studies on artificial intelligence (AI) in music education from 2015 to 2025, establishing current research trends, challenges in AI-assisted teaching, and implementation. The research shows that AI is primarily used to improve the effectiveness of performance assessment, optimize music theory teaching, and facilitate scaffolded self-learning. The study focuses on Asia, particularly China, and is mainly student-centered, with lower emphasis on teacher dimensions and long-term learning outcomes. The findings also highlight three main ways in which AI contributes to educational innovation: personalized feedback, standardized assessment, and immersive engagement. With these technologies embedded in the classroom, the role of teachers is also transforming, shifting from direct teaching to promoting learning and interpreting technical feedback. Despite the significant potential of AI in improving teaching efficiency and student engagement, challenges remain in terms of technology, equity issues, and teaching ethics. Overall, this review comprehensively summarizes the current research and emphasizes the need to view AI as a partner in music education rather than a substitute for human professional knowledge.