Teachers’ Perceptions of AI-Assisted Music Composition Tools in Music Education: A Thematic Critical Review With Implications for Ghanaian Colleges of Education - Yeboah, Richmond Amoh; Arkhurst, Kow (Harmonia : Journal of Music and Arts, 2026)
AI tools support music learning tasks such as composition scaffolding, automated feedback, and adaptive practice. Evidence on teacher-facing use remains uneven, and recent studies give limited attention to African teacher-education settings. This review synthesises 2024–2025 research on teachers’ perceptions of AI-assisted music composition tools, focusing on perceived benefit, effort, risk, ethics, authorship, and assessment integrity, and draws evidence-bounded implications for music teacher education in Ghanaian Colleges of Education. A structured thematic synthesis was conducted using only the studies listed in the provided annotated bibliography. Screening retained sources that (a) addressed AI tools used for composition or composition-related analysis in formal education and (b) reported teacher, teacher-educator, or pre-service teacher perspectives, alongside systematic reviews and educator-facing conceptual scholarship. Data extraction captured publication year, setting, participant group, AI tool type, learning task, and reported perception constructs. Deductive coding grouped findings under instructional value, usability and effort, creative support and dependency, ethics and authorship, assessment integrity, and institutional readiness. Across the source set, studies report perceived gains in feedback speed, practice efficiency, and learner autonomy, alongside concerns about overreliance, plagiarism risk, blurred authorship, and weak assessment governance. Teacher AI literacy and readiness recur as constraints. Implications include targeted AI literacies in teacher education, assessment redesign, and ethical decision-making support. Limits include a restricted source set and the absence of Ghana-based empirical studies within the included literature.