Although artificial intelligence is fundamentally reshaping the ecology of music learning, existing research has disproportionately emphasized performance outcomes while underexamining psychological mechanisms, leaving the tension between technological empowerment and cognitive dependence theoretically underarticulated. Following PRISMA 2020, we systematically searched four databases and included 21 empirical studies to examine how three AI tool types—assessment-oriented AI, generative AI, and Comprehensive/adaptive AI—differentially shape learners’ self-beliefs and cognitive agency in music education. The evidence base remains geographically and developmentally concentrated: most studies were conducted in China and in higher education, while early childhood settings were absent. Using thematic analysis, we conducted cross-type comparisons and synthesized psychological pathways. Assessment-oriented AI most consistently strengthened ability beliefs via objectified, visualized feedback and positioned cognitive agency around self-monitoring, self-reactiveness, and self-reflectiveness. Generative AI tended to enhance value–attitude beliefs and intentionality by lowering technical barriers and reconfiguring learners’ creative roles toward aesthetic decision-making and output curation. Comprehensive/Adaptive AI more often supported forethought and sustained engagement by dynamically maintaining alignment between task challenge and learner capability. Across studies, psychological empowerment manifested as increased perceived competence and control, heightened motivation and engagement, and visible self-regulated learning behaviors. Cognitive dependence, however, emerged through outsourcing evaluative authority, score-driven goal distortion, algorithm-accommodating self-censorship, and attributional shifts that tether confidence to technological support. Developmental differences were also observed regarding dependence mechanisms: primary learners tended to perceive AI as a restrictive “scoring referee,” whereas higher education students demonstrated strategic agency in orchestrating AI assistance. Specifically, a critical construct–tool mismatch was identified: while assessment AI consistently supports self-reflectiveness, generative AI currently lacks sufficient evidence for fostering learners’ forethought. In light of the identified construct–tool mismatch, future research should prioritize addressing the paucity of evidence on how generative and adaptive AI foster forethought and intentionality, thereby clarifying whether such technologies ultimately reconstruct or erode learners’ cognitive agency.

This study investigates the relationship between artificial intelligence (AI) tools and the creative abilities of music practitioners within the context of globalization and technological advancements that are transforming creative industries. Through a quantitative analysis, the study assesses how AI tool usage influences creative output. By surveying music practitioners from diverse backgrounds, it captures their experiences and perceptions of AI technologies in music creation. Grounded in cognitive science and diffusion of innovation theories, the research also empirically examines the relationship between AI technology acceptance and creativity perception, while considering the role of socioeconomic factors. Regression analysis was used to explore the relationships between key variables, ensuring robust and reliable results. The results suggest that AI technology acceptance is significantly correlated with creative performance, particularly among individuals with formal music education and experience using music composition software. However, socioeconomic factors such as age, gender, and professional background also influence how extensively AI is utilized in the creative process. These findings provide new insights into AI’s role in creative industries and offer data to inform music education and technology training policies.

Metacognition and self-regulated learning (SRL) are widely recognized as key mechanisms for academic achievement and skill development, yet in music education they have rarely been examined through explicit instructional interventions to enable causal testing and effect evaluation. To address this gap, this study followed PRISMA guidelines and conducted a systematic review of 31 studies (including seven for meta-analysis) to identify intervention types and mechanisms, and to quantify their overall effects and moderating factors. Results indicate the following: (1) the intervention ecology is grounded in structured learning support (SLS), frequently combined with strategy teaching (ST) or technology-enhanced interventions (TEI), with full integration concentrated at the university level. (2) The mechanisms operate primarily along four pathways: structure facilitates a “plan–practice–reflection” loop, strategy instruction makes tacit experience explicit, technological feedback provides a third-person perspective, and teacher support stabilizes motivation. (3) The meta-analysis revealed a significant positive medium effect overall. (4) Intervention structure moderated outcomes, though not as a single or stable determinant. (5) Effects followed a U-shaped pattern across educational stages, strongest in secondary school, followed by university, and weaker in preschool and primary. Future research should employ proximal, task-aligned measures, conduct parallel multi-indicator assessments within the same stage, and expand evidence for multi-mechanism integration in primary and secondary school contexts. Experimental designs manipulating levels of SLS are needed to test whether ST + TEI remain effective under low-structure conditions, thereby identifying the minimum structural threshold. Extending samples to informal and professional music learners would further enhance robustness and generalizability.

This study examines the association between listening to music at home and subjective well-being, using data from 14,162 respondents in the China General Social Surveys conducted in 2015, 2017, and 2021. Among the respondents, the average happiness score was 3.958. Regression analyzes indicate that frequent music listening is significantly associated with higher happiness levels, with coefficients of 0.384 in the baseline model and 0.570 in the model with control variables. Robustness checks performed across different models support these findings. Instrumental variable analysis, using Mandarin proficiency, yielded a coefficient of 0.212, indicating a robust association despite a slight reduction in magnitude. Heterogeneity analyzes showed consistent associations across genders, religious beliefs, and regions, with slightly stronger associations observed for females and non-religious individuals. Mediation analysis identified mental health and class identity as significant mediators, contributing to a total association of 0.146. These results highlight the positive correlation between music listening and well-being, suggesting the potential value of integrating music resources into well-being strategies.

Despite neuroscientific evidence indicating that music training remodels brain structure, behavioral findings concerning its transfer effects on children's executive functions (EF) remain characterized by significant controversy and heterogeneity. To resolve this ambiguity, this study strictly adhered to PRISMA guidelines, conducting a comprehensive systematic search across three major databases (Scopus, Web of Science, and PsycINFO). We synthesized 29 empirical studies (N = 3265) from five continents using Robust Variance Estimation (RVE) to evaluate intervention effects and examine the potential moderating role of macro-cultural context and instructional organization. The analysis revealed a significant, medium-sized facilitative effect of music education on overall EF (g = 0.62). Among subdomains, working memory showed the most substantial benefit, and the preschool period (ages 3-6) was identified as a developmental stage showing larger EF-related gains. Notably, macro-level cultural context (Individualism vs. Collectivism) did not independently moderate outcomes, challenging prior assumptions of a collectivistic advantage. However, at the micro-level, a significant "Culture × Instructional Structure" interaction emerged for working memory, suggesting a possible context-dependent pattern: children in individualistic cultures benefited more from high-structured (skill-oriented) training, whereas those in collectivistic cultures gained significantly more from low-structured (experience-oriented) activities. These findings suggest that the effects of music education on children's EF may vary as a function of instructional organization and broader cultural-educational context. However, the culture × instructional-structure pattern should be interpreted as hypothesis-generating, given the uneven subgroup evidence, variation in control-condition strength, and coarse country-level cultural coding. Future research should test this pattern under stronger active-control designs and more developmentally appropriate EF measurement before translating it into broad instructional recommendations.

Previous research on digital music education has often relied on single-factor perspectives or linear models, which limits the ability to capture complex causal relationships in shaping students' noncognitive outcomes. This study addresses this limitation by adopting a set-theoretic approach that combines Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Drawing on survey data from 329 undergraduates in China, the analysis first shows that no single course design dimension constitutes a necessary condition for high self-esteem (SE) or an internal locus of control (IE). Instead, the results identify multiple equifinal and asymmetric pathways that jointly account for strong noncognitive outcomes. For SE, affective-activation bundles that emphasize instructional attractiveness, interaction design, and community functions are more common. For IE, ability-enabling bundles that stress content quality and resource continuity are more prominent. These findings extend the literature by demonstrating that digital music education should be understood as the outcome of conjunctural causation rather than isolated levers, and they highlight the need for construct-sensitive instructional design. Practically, the study suggests designing for bundles rather than parts, building sustained resource ecosystems, and structuring inclusive interaction to support both self-beliefs and self-regulation. Limitations include the cross-sectional design and reliance on self-reports, which future research can address through longitudinal and behavioral approaches.