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

Minbo Li, Yunyi Zhao, Xin Shan, Xiaofei Du

Abstract read

In one paragraph

Article in Journal of Intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Minbo LiCollege of Music, Yunnan Arts University, Kunming 650504, China.

Yunyi ZhaoCollege of Music, Yunnan Arts University, Kunming 650504, China.

Xin ShanThe Graduate School Arts and Culture, Sangmyung University, Seoul 03016, Republic of Korea.ORCID 0009-0000-8840-7312

Xiaofei DuCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 (

Indexed as

artificial intelligence assistanceboundary conditionscreativityhigher-order cognitionmotivationmusic educationself-regulated learningthree-level meta-analysis

Identifiers

PMID42505937

DOI10.3390/jintelligence14070126

PMCPMC13413095

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.