Differentiated Empowerment and Boundary Effects of AI-Assisted Music Learning: A Mixed-Methods Study of Learning Motivation, Self-Regulated Learning, and Creative Performance

Although artificial intelligence (AI) is reshaping music education, outcome-oriented quantitative syntheses remain relatively limited. This mixed-methods review examined the effects of AI-assisted music learning on learning motivation, self-regulated learning (SRL), and creative performance, while identifying learner-, task-, and time-related boundary conditions and clarifying how AI support is implemented and experienced. A three-level meta-analysis was used for quantitative integration, complemented by a qualitative synthesis of implementation pathways and learner experiences. Results showed positive trends across all three core domains, with different levels of statistical support and substantial heterogeneity. Learning motivation showed the most robust evidence (g = 1.28), creative performance showed a larger but highly heterogeneous effect (g = 1.21), and SRL showed a preliminary positive trend (g = 0.57). The task complexity × prior ability interaction provided tentative, directional evidence for learner–task fit, mainly for motivational outcomes. Dose-related analyses suggested a possible asynchronous pattern: motivational gains may emerge rapidly in the short term, whereas gains in higher-order cognition may become more evident under sustained intervention. Qualitative synthesis identified three AI implementation pathways—evaluative feedback, generative support, and adaptive personalization—suggesting that effectiveness depends less on technological complexity itself than on aligning AI roles with task demands and learner needs. Future research should strengthen long-term designs, deepen SRL-related evidence, and examine the adaptive effects of different AI roles across diverse music learning contexts.