Evidence map›Paper›PMID 41919114›Full record

ArticleFrontiers in psychology2026

The impact of university students' AI attitudes on AI-assisted creativity: the mediating role of AI usage motivation and the moderating role of AI dependency.

Jianfeng Yin, Tingting Yuan, Yueying Zhang, Guizhen Yang, Xinghua Wang

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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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

5 authors.

Jianfeng YinSchool of Education Science, Qingdao University, Qingdao, China.
Tingting YuanBoya International College, Yantai Institute of Science and Technology, Yantai, China.
Yueying ZhangSchool of Education Science, Qingdao University, Qingdao, China.
Guizhen YangNormal College, Ningbo University, Ningbo, China.
Xinghua WangFaculty of Applied Sciences, Macao Polytechnic University, Macao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the growing use of artificial intelligence (AI) in higher education, understanding how students develop creativity in AI-assisted learning environments has become increasingly important. This study examines how students' attitudes toward AI influence their AI-assisted creativity, focusing on the mediating role of AI usage motivation and the moderating role of AI dependency. The research is informed by the Theory of Planned Behavior and Self-Determination Theory. Methods: Data were collected through a questionnaire survey of 347 university students. Structural equation modeling and bootstrap methods were used to test the relationships among AI attitudes, usage motivation (intrinsic motivation, identified regulation, and external regulation), AI dependency, and AI-assisted creativity. Results: The results show that AI attitudes have a significant positive effect on AI-assisted creativity, and this relationship is fully mediated by the three types of usage motivation. Among them, identified regulation serves as the strongest mediator. The moderating role of AI dependency differs across motivation types. Higher AI dependency weakens the positive effect of intrinsic motivation on creativity but strengthens the effect of external regulation, while the pathway through identified regulation remains largely stable. Discussion: These findings highlight the importance of motivation internalization in promoting creativity in AI-assisted learning contexts. The results suggest that encouraging more self-endorsed forms of motivation may help students use AI tools more creatively, offering theoretical insights and practical guidance for integrating AI into higher education to support student creativity.

Indexed as

AI-assisted creativityAI attitudesAI dependencyexternal regulationidentified regulationintrinsic motivation

Identifiers

PMID41919114
PMCPMC13034473

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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.