Music education in the age of AI: What educators and creators need to know

The integration of Artificial Intelligence (AI) in music education is transforming how students learn, create, and engage with music. This review explores the evolution and application of AI-driven technologies—from personalized learning systems and emotion-aware tutors to co-creative composition tools. By synthesizing recent empirical findings and theoretical models, this article highlights both the pedagogical potential and ethical complexities of AI in the music classroom. Despite notable advances in adaptive feedback, engagement, and accessibility, challenges such as cultural bias, data transparency, and teacher training remain unresolved. Future directions include building more inclusive datasets, enhancing co-creativity, and supporting educators through human-in-the-loop systems. This paper aims to equip educators, technologists, and researchers with a critical understanding of how AI is reshaping the landscape of music learning for the next generation.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-30
DOI
https://doi.org/10.5281/zenodo.17213669
Primary Topic
Nanoparticles: synthesis and applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Music education in the age of AI: What educators and creators need to know

Zenodo (CERN European Organization for Nuclear Research)
Nanoparticles: synthesis and applications
article

Music education in the age of AI: What educators and creators need to know

article en

Abstract

The integration of Artificial Intelligence (AI) in music education is transforming how students learn, create, and engage with music. This review explores the evolution and application of AI-driven technologies—from personalized learning systems and emotion-aware tutors to co-creative composition tools. By synthesizing recent empirical findings and theoretical models, this article highlights both the pedagogical potential and ethical complexities of AI in the music classroom. Despite notable advances in adaptive feedback, engagement, and accessibility, challenges such as cultural bias, data transparency, and teacher training remain unresolved. Future directions include building more inclusive datasets, enhancing co-creativity, and supporting educators through human-in-the-loop systems. This paper aims to equip educators, technologists, and researchers with a critical understanding of how AI is reshaping the landscape of music learning for the next generation.

Zenodo (CERN European Organization for Nuclear Research)
Instituto Superior de Gestão (PT)
Quality Education
Openalex Percentile: Top 59%
Nanoparticles: synthesis and applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.