Artificial intelligence is redefining the landscape of musical learning, offering increasingly sophisticated tools for performance analysis, performance support and musical composition. However, the rapid development of these technologies raises questions about their actual durability and the pedagogical implications of their integration into educational pathways. Analysis of current technologies shows how AI-based applications, including Kena.AI, Yousician, Chordify, Moises, AIVA and Amper Music, have reached a high level of sophistication, enabling advanced personalization of music teaching. The use of AI in music education offers numerous advantages. With the ability to process large amounts of data, these tools enable the creation of adaptive learning paths, customized to the needs of each student. AI allows students to practice independently, receiving immediate feedback on various performance aspects, contributing to greater technical and interpretative awareness.

However, several critical issues emerge. As highlighted in the scientific literature, while AI can enhance certain aspects of music learning, it cannot fully replace the role of a human teacher, whose sensitivity, empathy, and adaptability are essential for students’ artistic and interpretative development. Additionally, the constant technological evolution of these platforms leads to frequent updates and changes in teaching tools, raising questions about their long-term stability and the pedagogical impact of their integration into formal curricula. This aspect presents a challenge for the structured adoption of AI in music education, particularly at advanced levels.

AI can thus be considered a pedagogical support tool, assisting educators in performance analysis and the personalization of learning paths while preserving the human connection essential for musical growth. In introductory courses, applications such as Yousician and Kena.AI can facilitate the acquisition of fundamental skills, whereas at intermediate and advanced levels, tools like Moises and AIVA can support musical analysis and composition, encouraging the exploration of complex harmonic and structural solutions.

This study aims to analyze the role of AI in music education, evaluating its potential and limitations in relation to international guidelines for music teaching. Specifically, it will examine the principles established by the Associated Board of the Royal Schools of Music (ABRSM) and UNESCO. The ABRSM provides a structured framework for musical skill development, emphasizing the importance of technical proficiency, music theory, and artistic interpretation. UNESCO, through its recommendations on music education, promotes an inclusive and holistic approach that values creativity, cultural diversity, and social interaction in musical training. By analyzing these regulatory frameworks, this study will assess whether and how AI-based technologies can be effectively integrated into music learning pathways without compromising fundamental aspects related to creativity, artistic sensitivity, and the teacher-student relationship.

Keywords: Music, artificial intelligence, pedagogy, education, innovation.