AI-Driven Personalized Learning for Beginner Musicians

Abstract

This paper presents the development and implementation of an AI-driven personalized learning platform designed specifically for beginner musicians. The platform leverages advanced machine learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to offer real-time feedback on musical performance, focusing on pitch recognition, rhythm accuracy, and note classification. By adapting to the individual progress of each learner, the system provides tailored exercises, personalized recommendations, and motivational elements, such as gamification, to enhance engagement and improve learning outcomes. The study evaluates the platform’s effectiveness through empirical testing, measuring improvements in performance accuracy and user satisfaction. Results indicate that the AI system significantly aids beginner musicians in overcoming common learning challenges, fostering confidence, and accelerating skill development. This research contributes to the growing field of AI in music education, addressing the gap in personalized learning tools for beginners and offering a scalable solution for music instructors and learners alike. The findings suggest that AI-driven platforms hold considerable potential to revolutionize music education by providing adaptive, interactive, and personalized learning experiences.