AI-Driven Personalization of Music Education for Remote Learners

Thesis Abstract

The rapid growth of remote learning environments has underscored the need for personalized instructional approaches in music education, especially given the diverse learning styles and skill levels of contemporary students. Despite the proliferation of digital platforms, many remote learners encounter challenges in receiving tailored feedback and developing technical proficiency without direct instructor supervision. This study aims to develop and evaluate an AI-driven personalized music education system that adapts instructional content and feedback to individual learners’ progress and preferences. Specifically, the research seeks to (1) design an intelligent platform integrating machine learning algorithms for real-time adaptive feedback, (2) assess its effectiveness through empirical testing, and (3) identify factors influencing learners' engagement and skill acquisition within this context. To achieve these aims, the research adopts a mixed-methods approach, combining quantitative experimental design with qualitative exploration. The quantitative component involves a quasi-experimental research design with a sample of 150 remote music students enrolled across three online music institutions. Participants are randomly assigned to either the experimental group, which utilizes the AI-driven adaptive learning platform, or the control group, which follows conventional online instructional methods. Data collection instruments include pre- and post-intervention musical skill assessments, learner engagement questionnaires, and system usability scales. Qualitative data are gathered through semi-structured interviews with 20 participants to explore user experiences and perceptions. The primary analytical methods include regression analysis to evaluate the relationship between system usage and learning outcomes, paired t-tests to compare pre- and post-test scores, and thematic analysis to interpret interview data, ensuring comprehensive insights into usability and learner perceptions. The study anticipates that learners engaging with the AI-driven system will demonstrate statistically significant improvements in technical proficiency, as evidenced by higher post-test scores (p < 0.01), and report increased engagement and motivation levels relative to control participants. Additionally, the findings are expected to reveal critical factors—such as system responsiveness and personalized feedback quality—that influence the efficacy of AI-supported instruction. Theoretically, the research adapts Vygotsky’s Zone of Proximal Development (ZPD) and Keller’s ARCS model of motivation to explain how adaptive feedback can facilitate skill development and sustain learner motivation in a remote setting. This study contributes to the existing body of knowledge by providing empirical evidence on the effectiveness of AI-driven personalization in music pedagogy, expanding theoretical understanding of adaptive learning in arts education, and offering practical guidelines for designing scalable, learner-centered digital tools for remote music instruction. Furthermore, it bridges a crucial gap in literature by focusing on the integration of artificial intelligence techniques within music education frameworks, an area that remains underexplored. In conclusion, the research is expected to demonstrate that AI-enabled personalized music education significantly enhances learner outcomes and engagement in remote contexts. The recommendations will include best practices for implementing intelligent adaptive systems, strategies for optimizing feedback mechanisms, and considerations for broadening access to personalized music instruction through technology. The findings underscore the potential of artificial intelligence to transform remote music pedagogy by fostering more inclusive, effective, and engaging learning environments, thus informing future innovations in digital arts education and advocating for the integration of intelligent systems into mainstream remote learning programs.