Skip to content

Remixing Instruction and Access in Elementary Music with Artificial Intelligence Tools

Oct 2026 · ScholarWorks@UMassAmherst (University of Massachusetts Amherst)
Music Education and Analysis

Abstract

Since the release of ChatGPT in November of 2022, the role of generative artificial intelligence (GAI) systems in education has been a topic of discussion and debate at seemingly every level, be it international (UNESCO, 2024), national (Cardona et al., 2023), or school district (Seattle Public Schools, 2025). While the discussion has covered many areas of education, one area in particular that could benefit from the use of GAI tools is access (Cardona et al., 2023; Marino, 2023; McGee et al., 2025; UNESCO, 2024). This is especially important in music education; research has shown that 85% of music educators adapted goals and instruction for students with disabilities, but only 9% felt competent in doing so (McCord & Watts, 2010). This presents an opportunity for music educators to use GAI tools to improve the access of their teaching and increase their confidence, but will these tools have the impact on music education that some claim (National Association for Music Education, 2025)?In this dissertation, I explore how elementary music educators apply GAI tools to various areas of their teaching practice to increase access for students. I examine the effect of learning these tools on how teachers think about the role of access in their practice, how these tools affect educators’ confidence in adapting instruction, and the future role that educators believe these tools will play in their practice. Five elementary music educators learned to use three GAI tools: Suno, Nano Banana, and Gemini, and applied them to various areas of their teaching to increase student access using the framework of Universal Design for Learning. Results showed that educators found a wide variety of ways to use these tools in their classrooms, such as generating custom backing tracks for their ukulele students, generating choice boards, and having Gemini adopt a historical persona to help students learn about famous artists. After exploring these tools, all but one participant envisioned integrating these tools into their teaching practice to enhance, rather than reimagine, their current teaching, and to save time creating materials that increase student access. Participants also reported increased confidence in adapting instruction and an increased focus on access in their lesson planning and teaching.

View source

Similar papers

#artificial intelligence Conference Open access Jun 2018

Gibson Env: Real-World Perception for Embodied Agents

This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.

F. Xia, Amir Zamir, Zhi-Yang He et al. · 1,069 citations · ⚡112
#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9

Related blog posts

Hugging Face Blog Nov 10, 2026

Decision Models in Hugging Face

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.