Evidence map›Paper›PMID 42293946›Full record

ArticleFrontiers in psychology2026

Beyond prompt engineering: the associations among creative self-efficacy, flow state, learner engagement, and psychological ownership in GenAI-assisted composition.

Tianyu Tong

Abstract read
In one paragraph

Article in Frontiers in psychology, 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

1 author.

Tianyu TongGraduate School, Anyang University, Anyang, Gyeonggi-do, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The integration of generative artificial intelligence (GenAI) into music education markedly lowers technical barriers for predominantly novice composers, but also raises concerns about a potential erosion of human creative agency. When learners rely on text prompts to produce music with minimal subsequent involvement, they may fail to develop a sense of psychological ownership over AI-assisted creations. Method: Drawing on the Theory of Psychological Ownership, this study examined the cognitive, affective, and behavioral processes through which perceived GenAI support relates to students' psychological ownership. Survey data were collected from 355 non-music-major undergraduates enrolled in a GenAI-assisted composition course that explicitly required iterative post-generation refinement of AI outputs. Structural equation modeling with bias-corrected bootstrapping was used to test a serial mediation model. Results and discussion: The results showed that perceived GenAI support was positively and significantly associated with psychological ownership, and that this relationship operated through a sequential pathway involving creative self-efficacy, flow state, and learner engagement. These findings suggest that GenAI does not inherently alienate learners; when positioned as a cognitive scaffold within a human-in-the-loop design, it is associated with creative confidence, optimal immersion, and active investment of effort. The study highlights the importance of deliberately incorporating productive friction into AI-supported learning activities to elicit an "IKEA effect," thereby transforming algorithmically generated material into personally appropriated creative artifacts.

Indexed as

creative self-efficacyflow stategenerative AIhuman-AI co-creationlearner engagementmusic educationpsychological ownershiptext-to-music

Identifiers

PMID42293946
PMCPMC13261905

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.