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Designing Personalized Music Instructional Programs in Higher Education With Artificial Intelligence Support

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  • Yuanyuan Lu

    (Baise University, China)

Abstract

Traditional higher music education, bound by uniform curricula, struggles to meet diverse student needs. Existing artificial intelligence tools remain fragmented, lacking integration into a comprehensive web-based personalized system. In this study, the author designed and validated an artificial intelligence-enhanced closed-loop framework—diagnosis, customization, implementation, evaluation—embedded in a digital learning environment. Leveraging speech recognition, big data analytics, and adaptive content delivery, the system enables dynamic individualized instruction across vocal and instrumental domains. A semester-long trial with 120 music majors showed that the experimental group scored nearly 10 points higher in composite skills, engaged 284% more with personalized content, and reduced teacher grading time by 46%. The work demonstrates how web-based intelligent technologies can reshape human–machine collaboration in arts education, offering a scalable model for personalized technology-mediated pedagogy in higher education.

Suggested Citation

  • Yuanyuan Lu, 2026. "Designing Personalized Music Instructional Programs in Higher Education With Artificial Intelligence Support," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global Scientific Publishing, vol. 21(1), pages 1-16, January.
    • Handle: RePEc:igg:jwltt0:v:21:y:2026:i:1:p:1-16
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