Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines

Liam Pond, Ichiro Fujinaga

2025

Abstract

This study evaluates the baseline capabilities of Large Language Models (LLMs) like ChatGPT, Claude, and Gemini to learn concepts in music theory through in-context learning and chain-of-thought prompting. Using carefully designed prompts (in-context learning) and step-by-step worked examples (chain-of-thought prompting), we explore how LLMs can be taught increasingly complex material and how pedagogical strategies for human learners translate to educating machines. Performance is evaluated using questions from an official Canadian Royal Conservatory of Music (RCM) Level 6 examination, which covers a comprehensive range of topics, including interval and chord identification, key detection, cadence classification, and metrical analysis. Additionally, we evaluate the suitability of various music encoding formats for these tasks (ABC, Humdrum, MEI, MusicXML). All experiments were run both with and without contextual prompts. Results indicate that without context, ChatGPT with MEI performs the best at 52%, while with context, Claude with MEI performs the best at 75%. Future work will further refine prompts and expand to cover more advanced music theory concepts. This research contributes to the broader understanding of teaching LLMs and has applications for educators, students, and developers of AI music tools alike.

Paper Citation

in Harvard Style

Pond L. and Fujinaga I. (2025). Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines. In Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSME; ISBN 978-989-758-746-7, SciTePress, pages 671-681. DOI: 10.5220/0013506100003932

in Bibtex Style

@conference{csme25,
author={Liam Pond and Ichiro Fujinaga},
title={Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines},
booktitle={Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSME},
year={2025},
pages={671-681},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013506100003932},
isbn={978-989-758-746-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSME
TI - Teaching LLMs Music Theory with In-Context Learning and Chain-of-Thought Prompting: Pedagogical Strategies for Machines
SN - 978-989-758-746-7
AU - Pond L.
AU - Fujinaga I.
PY - 2025
SP - 671
EP - 681
DO - 10.5220/0013506100003932
PB - SciTePress