In this survey, we present a focused analysis of recent advances in Artificial Intelligence (AI) for music education, structured around four key pedagogical domains: learning and practicing, assessment, creation, and teaching. First, for learning and practicing, we examine AI-driven tools for personalized skill acquisition, including intelligent instruments and adaptive systems that provide real-time feedback. Second, for assessment, we review progress in the automated assessment of musical performance, moving beyond pitch and rhythm to more nuanced expressive qualities. Third, for creation, we investigate innovations in AI-augmented composition, where generative models act as collaborative partners and creative catalysts for students. Finally, for teaching, we explore systems for teacher empowerment, such as automated resource generation and learning analytics dashboards. The analysis highlights the transformative role of Deep Learning (DL) and Generative AI (GAI) in each domain while critically discussing persistent technical limitations and emerging ethical concerns. By synthesizing interdisciplinary developments, this survey aims to chart the current frontier and inform future research at the intersection of AI technology and music pedagogy.
Yang et al. (Fri,) studied this question.