Developing an AI-Driven System for Personalized Music Learning and Feedback

Thesis Abstract

The rapid advancement of artificial intelligence (AI) technologies has transformed educational paradigms, yet music learning remains predominantly reliant on traditional teaching methods that often lack personalized feedback and adaptability to individual learners’ needs. This study addresses the critical challenge of enhancing music education through the development of an AI-driven system capable of providing real-time, personalized feedback to optimize learners’ musical skills acquisition. The central aim of the research is to design, implement, and evaluate an intelligent music learning platform that adapts to individual learner profiles, offering tailored instructional content and formative feedback to facilitate autonomous learning. Specific objectives include (1) identifying key pedagogical features required for personalized music instruction, (2) developing an AI model based on supervised learning algorithms to analyze learners’ performances, and (3) assessing the system’s effectiveness through empirical testing. The research adopts a mixed-methods approach, integrating quantitative experimental design with qualitative user experience evaluation. The quantitative component involves a quasi-experimental study with a sample of 150 beginner to intermediate music students drawn from conservatories and music schools, randomly assigned to experimental and control groups. The experimental group interacts with the AI system for a period of 12 weeks, while the control group uses conventional instructional methods. Data collection instruments include pre- and post-intervention performance assessments, standardized musical proficiency tests, and system usage logs. Qualitative data are gathered through semi-structured interviews and user feedback surveys to capture learner perceptions and system usability. Data analysis employs a combination of descriptive statistics, paired t-tests, and ANCOVA to evaluate performance improvements, alongside thematic analysis for qualitative feedback. The AI model is developed utilizing supervised learning techniques such as support vector machines (SVM) and convolutional neural networks (CNN) to analyze audio recordings, assess pitch accuracy, rhythm consistency, and expressive qualities, and generate personalized corrective feedback. The study further leverages the Technology Acceptance Model (TAM) and Experiential Learning Theory to frame user engagement and learning processes. Expected findings indicate that the AI-driven system significantly enhances learners’ musical accuracy, expressiveness, and self-regulated learning compared to traditional methods, with measurable improvements in performance scores. Learners are anticipated to report increased motivation, confidence, and satisfaction with the personalized feedback provided by the system. The research anticipates demonstrating that adaptive, AI-enabled instructional platforms can serve as effective supplementary tools in music education, fostering autonomous learning and supporting diverse learner needs. The study contributes to the existing knowledge by integrating cutting-edge machine learning techniques with pedagogical theories to create an innovative model of personalized music instruction. It advances understanding of how AI can be systematically employed to deliver tailored feedback, thereby bridging gaps in current music pedagogical practices and digital learning technologies. Furthermore, the research provides a framework for designing user-centered, adaptive learning environments in the arts domain. In conclusion, the findings advocate for the broader integration of AI-based tools in music education, emphasizing their capacity to complement traditional methods and democratize access to personalized instruction. Recommendations include further refinement of AI models through expanded datasets, exploration of multilingual user interfaces, and longitudinal studies to evaluate sustained learning outcomes. Future research should explore scalability across diverse musical genres and cultural contexts, as well as the incorporation of virtual reality (VR) for immersive learning experiences, to fully realize the potential of intelligent tutoring systems in the arts.