Frontiers in psychology · 2026
The impact of precise feedback in AI vocal courses on learning outcomes: a chain mediation effect of skill mastery and self-efficacy.
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Wu Ronghua, Wen Jie
Аннотация
INTRODUCTION: Policies such as the "New Generation Artificial Intelligence Development Plan" emphasize the application of AI across the educational process, while the multidimensional nature and psychological mechanisms of learning outcomes in vocal education remain insufficiently explored.
PURPOSE: This study explores the impact of precise feedback in AI vocal courses on learning outcomes and investigates the chained mediating roles of skill mastery and self-efficacy to uncover the underlying psychological mechanisms.
METHODS: A pre-post experimental design with a control group was implemented, recruiting 114 undergraduate music education majors who were randomly assigned to either an experimental group (receiving AI-based precise feedback courses) or a control group (receiving traditional instruction). Real-time feedback was delivered through the VocalCoach Pro 3.0 system, with data collection using the Multidimensional Vocal Learning Outcomes Scale, Skill Mastery Questionnaire, and Self-Efficacy Scale. Data analysis involved t-tests, correlation analysis, and Bootstrap mediation effect testing.
RESULTS: AI-powered precision feedback significantly improved vocal learning effectiveness (posttest M = 4.271 for the experimental group vs. M = 3.851 for the control group; Cohen's d = 1.638, p < 0.001). Both perceived skill mastery (mediation effect = 0.280) and self-efficacy (mediation effect = -0.145) significantly mediated the relationship, establishing the chained mediation pathway: "AI feedback → perceived skill mastery → self-efficacy → learning effectiveness" (effect size = -0.103, 95% CI [-0.310, -0.016]). The direct effects were not significant.
CONCLUSION: AI-driven precise feedback influences learning outcomes through a chained mediating mechanism involving perceived skill mastery and self-efficacy, emphasizing the importance of the "cognitive-motivational" sequential process. This study provides theoretical foundations and practical insights for optimizing AI-based vocal music courses.
PURPOSE: This study explores the impact of precise feedback in AI vocal courses on learning outcomes and investigates the chained mediating roles of skill mastery and self-efficacy to uncover the underlying psychological mechanisms.
METHODS: A pre-post experimental design with a control group was implemented, recruiting 114 undergraduate music education majors who were randomly assigned to either an experimental group (receiving AI-based precise feedback courses) or a control group (receiving traditional instruction). Real-time feedback was delivered through the VocalCoach Pro 3.0 system, with data collection using the Multidimensional Vocal Learning Outcomes Scale, Skill Mastery Questionnaire, and Self-Efficacy Scale. Data analysis involved t-tests, correlation analysis, and Bootstrap mediation effect testing.
RESULTS: AI-powered precision feedback significantly improved vocal learning effectiveness (posttest M = 4.271 for the experimental group vs. M = 3.851 for the control group; Cohen's d = 1.638, p < 0.001). Both perceived skill mastery (mediation effect = 0.280) and self-efficacy (mediation effect = -0.145) significantly mediated the relationship, establishing the chained mediation pathway: "AI feedback → perceived skill mastery → self-efficacy → learning effectiveness" (effect size = -0.103, 95% CI [-0.310, -0.016]). The direct effects were not significant.
CONCLUSION: AI-driven precise feedback influences learning outcomes through a chained mediating mechanism involving perceived skill mastery and self-efficacy, emphasizing the importance of the "cognitive-motivational" sequential process. This study provides theoretical foundations and practical insights for optimizing AI-based vocal music courses.