Generative artificial intelligence in music education and creativity: A review of current developments and future directions

Abstract

Generative artificial intelligence (AI) is transforming the way music is generated, accompanied, harmonized, pitched, transposed, and generally transformed, created, taught, studied, and evaluated. This is an integrative review that has been structured and synthesized 75 sources from the databases, namely Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Education Resources Information Center (ERIC), APA PsycINFO, Google Scholar, citation searches, and authoritative institutional reports. The literature published between January 1, 2021, and July 7, 2026, was prioritized, while earlier legal, ethical, historical, and theoretical literature was retained when needed for conceptual grounding. The directness of the evidence was coded separately from the evidential weight of the evidence, which included findings from other contiguous technologies, such as automated feedback, intelligent tutoring systems, learning analytics, augmented reality/virtual reality, and online digital audio workstations. The synthesis suggests that activities that are teacher-mediated, process-assessed, culturally responsive, and ethically governed may open up opportunities for generative AI to assist in composition, songwriting, music-theory learning, certain types of feedback on practice, improvisation, and collaborative music-making. Yet, there is a scarcity of direct, controlled, and longitudinal evidence, and issues remain regarding originality, authorship, assessment integrity, cultural bias, data privacy, equitable access, teacher autonomy, and student agency. The review contributes to systematic innovation by connecting the iterative processes of generation, musical evaluation, revision, implementation, and reflection to pedagogical, assessment, and governance frameworks. Generative AI is therefore best understood as a pedagogical and creative tool that is used by a human teacher, not to replace musicianship or the teacher’s judgment.

Keywords

Generative artificial intelligence / Music education / Musical creativity / Artificial intelligence-assisted composition / Artificial intelligence literacy / Human–artificial intelligence collaboration

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