Artificial Intelligence in Music Education in Higher Education

Exploring Applications and Pedagogical Considerations

Bibliographic Data

ID21446570
AuthorsZhang You (0000-0003-1253-9786, University of Puerto Rico System, corresponding author)
Year2026
Volume61
Issue3
Publication date2026-09-01
Open AccessYes
TypeARTICLE
VenueEuropean Journal of Education (JOURNAL)
Journal identifiersISSN: 0141-8211 • E-ISSN: 1465-3435
PublisherWiley (PUBLISHER • GB)
DOI10.1111/ejed.70691
OpenAlexW7162661826
LanguageEN
References cited74

The integration of Artificial Intelligence (AI) into instrumental music education has rapidly progressed. However, the scholarly landscape remains fragmented and underdeveloped. Current research mainly focuses on algorithm‐driven performance and context‐specific teaching methods. There has been little effort to connect these advancements to established educational frameworks or to examine their wider implications. This systematic review addresses this gap by providing a thorough synthesis of the field, analyzing 46 highly cited publications indexed in Scopus. This review goes beyond mere listing. It critically examines how leading AI methods, such as deep learning models, transformer‐based systems, and generative frameworks, serve as meaningful tools for instrumental learning. Drawing on established frameworks such as the Technological Pedagogical Content Knowledge (TPACK) model, the Substitution‐Augmentation‐Modication‐Redefinition (SAMR) taxonomy, and Bloom's revised taxonomy, the study offers a well‐grounded interpretation of how AI changes teaching design, student engagement, and cognitive growth. The findings show that AI technologies improve practice efficiency, enable personalized learning paths, and create more consistent, data‐driven assessment methods. However, the review also highlights major limitations that were overlooked in previous research. These include algorithmic bias, lack of cultural and stylistic sensitivity, difficulties in capturing expressive musical elements, and the lack of clear models for integrating pedagogy with technology. To guide future research and practice, the review urges the creation of culturally responsive AI systems, the establishment of strong ethical and governance structures, and the use of interdisciplinary design approaches that connect technology with educational values. This establishes a critical, forward‐looking foundation for viewing AI not just as a tool but as a transformative influence in instrumental music education

Cognition · Educational research · Foundation (evidence) · Generative grammar · Higher education · Interpretation (philosophy) · Music and artificial intelligence · Musical · Diverse Music Education Insights · Diverse Musicological Studies · Music Technology and Sound Studies

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