<p>Artificial intelligence (AI) is rapidly transforming music education worldwide, yet its implications are not culturally neutral. This review focuses on East Asia, where music curricula have historically been shaped by Western colonial influence, later challenged by post-colonial localization efforts, and are now subject to renewed pressures from digital technologies. By synthesizing approximately ninety sources across music education, ethnomusicology, postcolonial studies, and AI research, this article identifies how AI may simultaneously expand access to creative tools and reinforce epistemic inequality. Popular platforms such as GarageBand or generative models like MusicGen and Jukebox provide powerful opportunities for composition and learning, but their training data overwhelmingly reflect Euro-American repertoires, limiting their ability to represent non-Western scales, tunings, and idioms. East Asian case studies illustrate both risks and opportunities. In Hong Kong and Japan, classroom use of commercial tools has nudged students toward Western styles, while in China and Taiwan, carefully designed AI applications have enhanced students’ engagement with traditional opera and ethnic repertoires. These findings highlight a double-edged dynamic: uncritical adoption of AI risks a subtle “digital recolonization” of curricula, but context-sensitive applications can revitalize cultural heritage. This article concludes that culturally responsive AI integration requires critical pedagogy, inclusive datasets, and collaborative design involving educators and ethnomusicologists. By situating AI’s technical biases within broader historical and social contexts, this review offers concrete pathways for ensuring that technology supports, rather than supplants, musical diversity in East Asia.</p>