Intelligent scaffolding for musical practice: a systematic review of AI and digital support for the self-regulated learning cycle
- 1School of Music and Dance, China West Normal University , ,
- 2Faculty of Education Sciences, University of Seville , ,
Abstract
Introduction
Independent music practice depends on learners' self-regulated learning (SRL), yet how digital tools and artificial intelligence (AI) support the forethought, performance, and self-reflection phases of the SRL cycle, and when such support may undermine learner autonomy, remains unclear.Methods
Following PRISMA 2020, we searched Web of Science, Scopus, ERIC, and PsycINFO through March 2026. Sixteen empirical studies met the inclusion criteria and were appraised with the Mixed Methods Appraisal Tool. Because of methodological heterogeneity, we conducted a thematic synthesis rather than a meta-analysis.Results
Three categories emerged: traditional digital tools, discriminative AI, and generative AI. Traditional tools supported planning and reflection; discriminative AI enabled real-time error detection; and generative AI may help learners manage task difficulty and translate expert feedback into actionable steps. Study quality was uneven, and several findings came from small-sample or cross-sectional designs, which limits causal inference. Two potential risks, over-scaffolding that may diminish autonomy and visual feedback that may divert attention from sound, were insufficiently studied.Discussion
Digital and AI tools appear to scaffold different phases of SRL, but learner autonomy should remain at the core. Fading explicit support and pedagogical guidance may help reduce dependence. Longitudinal studies are required to test these mechanisms.
Related
- Intelligent educational agent-based collaborative scaffolding for STEM interdisciplinary project-based learning: a multi-dimensional framework and empirical evaluation
- Psychometric properties of the Persian version of the self-regulated learning scale in clinical nursing practice for nursing students: A methodological study
- Machine Learning, Deep Learning, and IoT for Waste Classification and Intelligent Waste Management: A PRISMA-Based Systematic Review
- Metacognitive training and AI-based Socratic scaffolding in pre-service teachers' reflective practice: a dual-study experimental design.
- Bridging the Gap Between Research and Practice: A Systematic Literature Review of IoT and Machine Learning Applications in Mental Healthcare Systems
BibTeX
@article{li2026intelligent,
title = {{Intelligent scaffolding for musical practice: a systematic review of AI and digital support for the self-regulated learning cycle}},
author = {Xiuran Li and Mar Galera-Núñez and Yao Zhang},
journal = {Frontiers in psychology},
year = {2026},
month = oct,
doi = {10.3389/fpsyg.2026.1947824},
url = {https://europepmc.org/article/PMC/PMC13717558},
note = {CC BY},
}
RIS
TY - JOUR
TI - Intelligent scaffolding for musical practice: a systematic review of AI and digital support for the self-regulated learning cycle
AU - Xiuran Li
AU - Mar Galera-Núñez
AU - Yao Zhang
PY - 2026
DA - 2026/10/15
JO - Frontiers in psychology
DO - 10.3389/fpsyg.2026.1947824
UR - https://europepmc.org/article/PMC/PMC13717558
AB - Introduction Independent music practice depends on learners' self-regulated learning (SRL), yet how digital tools and artificial intelligence (AI) support the forethought, performance, and self-reflection phases of the SRL cycle, and when such support may undermine learner autonomy, remains unclear.Methods Following PRISMA 2020, we searched Web of Science, Scopus, ERIC, and PsycINFO through March 2026. Sixteen empirical studies met the inclusion criteria and were appraised with the Mixed Methods Appraisal Tool. Because of methodological heterogeneity, we conducted a thematic synthesis rather than a meta-analysis.Results Three categories emerged: traditional digital tools, discriminative AI, and generative AI. Traditional tools supported planning and reflection; discriminative AI enabled real-time error detection; and generative AI may help learners manage task difficulty and translate expert feedback into actionable steps. Study quality was uneven, and several findings came from small-sample or cross-sectional designs, which limits causal inference. Two potential risks, over-scaffolding that may diminish autonomy and visual feedback that may divert attention from sound, were insufficiently studied.Discussion Digital and AI tools appear to scaffold different phases of SRL, but learner autonomy should remain at the core. Fading explicit support and pedagogical guidance may help reduce dependence. Longitudinal studies are required to test these mechanisms.
SN - 1664-1078
ER -