ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
Volume 16 - 2025 | doi: 10.3389/fpsyg.2025.1675762
This article is part of the Research TopicArtificial Intelligence for Technology Enhanced LearningView all 4 articles
Exploring the Impact of AI-Assisted Practice Applications on Music Learners' Performance, Self-Efficacy, and Self-Regulated Learning
Provisionally accepted
- 1Faculty of Social Sciences and Humanities,NOVA University of Lisbon, Lisbon, Portugal, Lisbon, Portugal
- 2Yunnan Minzu University, Kunming, China
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Despite significant advancements in artificial intelligence (AI) applications across various disciplines, research on AI's psychological impacts in music learning contexts remains limited. This study explores the effects of AI-assisted practice apps on violin students' self-efficacy, performance outcomes and Self-Regulated Learning (SRL). A four-month quasi-experimental study was conducted with 40 violin majors from a conservatory in South China. All participants received identical classroom instruction and maintained equivalent daily practice time, but the experimental group (n=20) used AI-assisted practice app while the control group (n=20) practiced using regular practice methods. Mixed-effects modelling revealed differentiated impacts on self-efficacy dimensions: while the control group experienced natural decline in Music Learning Self-Efficacy (MLSE) as task difficulty increased, AI intervention enabled the experimental group to maintain stable learning confidence. More notably, the experimental group achieved significant improvements in Music Performance Self-Efficacy (MPSE) with large effect sizes, indicating that AI-assisted practice app possesses distinct advantages in enhancing performance confidence. In terms of performance outcomes, the experimental group demonstrated significant improvement while the control group showed a declining trend. Thematic analysis revealed that AI-assisted practice apps support self-regulated learning (SRL) across three critical phases: providing goal-setting and strategic planning support during the forethought phase, facilitates self-monitoring and self-control during the performance phase, and enabling objective evaluation and strategic adjustment during the self-reflection phase. This study enriches understanding of self-efficacy theory in AI technology-enhanced learning environments and demonstrates AI technology's educational value in instrumental music learning.
Keywords: AI-assisted app, Musicians' Self-Efficacy, Musicians' Performance, self-regulated learning (SRL), quasi-experimental study
Received: 29 Jul 2025; Accepted: 25 Sep 2025.
Copyright: © 2025 Ou, Nogueira and Qin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chao Qin, qinchao@ymu.edu.cn
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