While Artificial Intelligence (AI) is rapidly transforming educational practices, its role for the field of music education remains both promising and contested. This review paper attempts to map the currently evolving field of AI in music education by examining how conceptions of AI have emerged, diversified, and often polarized within the discipline. Drawing on bibliometric methodologies, the paper provides a systematic overview of the field’s development and identifies clusters of research, dominant conceptual frameworks, and evolving trends. The analysis reveals a growing, multidisciplinary intersection between music pedagogy, learning analytics, music cognition, and computational creativity. However, the field continues to encounter skepticism concerning the epistemological, ethical, and aesthetic implications of integrating algorithmic concepts into musical learning.In addition to mapping the scholarly landscape, the paper critically synthesizes recent use cases and empirical studies to explore how AI is currently applied in music education, ranging from intelligent tutoring systems and automated feedback tools to generative systems for music composition and performance. These examples illustrate both the pedagogical potential of AI and the legitimate reservations expressed by educators and researchers, including concerns about artistic authenticity, data privacy, learner agency, and over-reliance on corporate AI ecosystems. By balancing enthusiasm with critique, the paper seeks to understand the motivations driving AI adoption as well as the contextual limitations that demand careful navigation.Importantly, the review emphasizes constructive pathways forward by highlighting areas where AI can enrich music education, for example by providing adaptive learning support, enabling inclusive access for learners, and facilitating creative exploration. At the same time, it advocates for approaches that are transparent, user-controlled, and data-ethical, including open source or community-driven AI systems as viable alternatives to proprietary “big tech” models. Through this balanced and forward-looking perspective, the paper contributes to a nuanced understanding of AI’s role in music education, not as a replacement for human creativity or pedagogy, but as a set of evolving tools that can complement and expand musical learning when guided by reflective and ethical practice. Ultimately, it invites educators, developers, and researchers to collaborate in shaping a future for music education that is both innovative and human-centered.