Tuning Music Education: AI-Powered Personalization in Learning Music

TL;DR

This paper addresses the challenge of engaging diverse learners in music education by moving beyond one-size-fits-all curricula toward personalized, interest-driven instruction. It presents two AI-powered case studies: RealEarTrainer, which generates custom ear-training exercises from students' favorite tracks using Automatic Chord Recognition and beat detection, and an adaptive piano method-book prototype that uses Automatic Music Transcription to tailor simplified arrangements and exercises to individual goals. The contributions include a practical demonstration of how recent AI tooling can connect formal practice with real-world musical contexts, enabling scalable, low-cost personalization and augmentation of teachers. While promising for increasing access and motivation, the work also discusses limitations, calls for rigorous effectiveness evaluations, and highlights privacy, cultural bias, and equity considerations in deploying AI-powered musical education.

Abstract

Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences are continuously evolving challenges in music education. Here we present two case studies using such advances in music technology to address these challenges. In our first case study we showcase an application that uses Automatic Chord Recognition to generate personalized exercises from audio tracks, connecting traditional ear training with real-world musical contexts. In the second case study we prototype adaptive piano method books that use Automatic Music Transcription to generate exercises at different skill levels while retaining a close connection to musical interests. These applications demonstrate how recent AI developments can democratize access to high-quality music education and promote rich interaction with music in the age of generative AI. We hope this work inspires other efforts in the community, aimed at removing barriers to access to high-quality music education and fostering human participation in musical expression.

Figures (2)

• Figure 1: Overview of RealEarTrainer. (a) The student can select custom audio tracks. AI modules detect beats and identify chords within these tracks. The app generates personalized exercises using snippets sourced from the selected audio tracks. Calibrating exercises based on learning history, goal specification, and current performance could be achieved with another AI module. (b) RealEarTrainer interface to select preferred music from a list of available tracks. (c) During the exercise, the app plays a snippet from one of the selected tracks and prompts the student to identify the chords being played. The sound icon on each chord in the options plays a synthesized piano version that can be used by the student to make the harmonic content salient.
• Figure 2: Re-imagined piano method books. (a) We obtain the score for bars 30 - 34 of Comptine D'un Autre Été L'après using Piano2Notes as our transcriber. (b) A mix of procedural and ACR AI modules is used to distill the piece by removing ornamentations and providing block chords that make the core idea easy to follow and play on the piano. (c) Modules to suggest scales over the block chord changes serve to build the related skills while maintaining a connection to the original piece.