Artificial intelligence applications and pedagogical challenges in music education

Bibliographic Data

ID22437160
AuthorsChamil Arkhasa Nikko Mazlan (0000-0002-0088-3849, Sultan Idris Education University, corresponding author), Hafizul Fahri Hanafi (0000-0002-3205-0956, Sultan Idris Education University), Muhammad R Sarifin (0000-0002-1565-6120, Sultan Idris Education University), Ahmad Rithaudin Md Noor (Universiti Teknologi MARA System), Saule Sadykova (0000-0003-3825-7680), Saule Altynbayevna Sadykova (Aktobe Regional State University named after K.Zhubanov), Riyan Hidayatullah (0000-0001-7382-229X, Lampung University), Surasak Jamnongsarn (0000-0002-1050-8639, Srinakharinwirot University)
Year2026
Volume5
Issue1
Publication date2026-01-29
Open AccessYes
TypeARTICLE
VenueDiscover Education (JOURNAL)
Journal identifiersISSN: 2731-5525 • E-ISSN: 2731-5525
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s44217-026-01127-3
OpenAlexW7126118076
LanguageEN
Citations received2
References cited137

This mini review synthesizes recent advancements in the integration of artificial intelligence (AI) within instrumental music education, emphasizing both computational methods and pedagogical frameworks. Drawing from the top 50 highly cited Scopus-indexed documents, the review identifies dominant AI techniques such as deep learning, transformer architectures, and generative models. These technologies enhance practice efficiency, personalize instruction, and improve assessment objectivity. However, challenges persist, including dataset bias, limited cultural sensitivity, and constraints in expressive feedback. Thematic and technical analyses reveal a strong focus on composition and performance domains, with creativity and feedback as key pedagogical impacts. The review integrates pedagogical models such as TPACK, SAMR, and Bloom’s taxonomy to contextualize AI adoption. Findings suggest that hybrid models combining AI analytics with human instruction offer the greatest educational value. Future research should prioritize culturally adaptive systems, ethical transparency, and inclusive design to ensure equitable and meaningful integration of AI in music pedagogy.

Applications of artificial intelligence · Computational Creativity · Creativity · Generative grammar · Music and artificial intelligence · Music education · Diverse Music Education Insights · Music Technology and Sound Studies · Neuroscience and Music Perception

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