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<p>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.</p>

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Artificial intelligence applications and pedagogical challenges in music education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Chamil Arkhasa Nikko Mazlan,
  • Hafizul Fahri Hanafi,
  • Muhammad Ridhwan Sarifin,
  • Ahmad Rithaudin Md Noor,
  • Saule Altynbayevna Sadykova,
  • Riyan Hidayatullah,
  • Surasak Jamnongsarn

摘要

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.