Artificial Intelligence in Music: A Bibliometric and Systematic Review of Creation, Performance, and Education

Abstract The integration of artificial intelligence (AI) into the music domain has catalyzed a transformative shift in how music is composed, performed, and taught. This paper introduces and frames the concept of music intelligence and employs bibliometric and systematic review methodologies to comprehensively analyze music intelligence. Music intelligence encompasses the development and application of intelligent systems that not only automate or enhance traditional musical tasks but also foster new modes of creativity, interaction, and pedagogy. Tracing the evolution from early rule-based systems to modern deep learning and multimodal models, we examine how AI is increasingly embedded in musical workflows. We highlight applications ranging from generative composition and expressive performance interpretation to real-time accompaniment and personalized education. By positioning AI as an active collaborator rather than a mere tool, this study underscores the need for collaborative efforts among computer scientists, musicians, educators, and cognitive scientists to fully realize the potential of intelligent music systems. Our biblio-metric analysis indicates an annual growth rate of 14.92%, with China, the US, and the UK contributing 52.9% of global research output. The findings reveal a rapidly expanding interdisciplinary field characterized by increasing international collaboration, methodological diversification, and a growing focus on human-AI co-creativity. However, persistent gaps remain in cultural inclusivity, interpretability, and ethical governance.