Evidence map›Paper›PMID 41006315›Full record

ArticleScientific reports2025

AI acceptance and Chinese EFL learners' behavioral engagement with mediating effects of motivation.

Qiufen Wang, Mansour Amini, Ziqiang Fu

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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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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

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

3 authors.

Qiufen WangSchool of Languages, Literacies and Translation, Universiti Sains Malaysia, Penang, 11800, Malaysia.
Mansour AminiSchool of Languages, Literacies and Translation, Universiti Sains Malaysia, Penang, 11800, Malaysia. Mansour@usm.my.
Ziqiang FuAnhalt University of Applied Sciences, Saxony-Anhalt, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence (AI) technologies become increasingly integrated into education, understanding how they influence learners' engagement is essential for effective pedagogy. This study examines the motivational mechanisms by which Chinese EFL learners' acceptance of AI tools influences their behavioral engagement. Grounded in Self-Determination Theory and the Unified Theory of Acceptance and Use of Technology, a cross-sectional survey was conducted among 654 university students. Structural equation modeling revealed that AI acceptance positively predicted motivation (β = 0.628, p < .001), which in turn significantly predicted engagement (β = 0.517, p < .001). A direct effect of AI acceptance on engagement (β = 0.253, p < .001) was also observed. Bootstrap mediation analysis revealed significant indirect effects of AI acceptance on behavioral engagement, particularly via self-satisfaction and self-efficacy. These findings highlight the importance of fostering autonomous motivation to translate positive perceptions of AI into active learning behaviors. The study provides empirical support for the integration of learner-centered AI and suggests that motivationally supportive design and pedagogy are crucial for maximizing AI's educational potential.

Indexed as

Artificial IntelligenceLearningMotivationAdolescentAdultChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleSelf EfficacyStudentsSurveys and QuestionnairesUniversitiesYoung AdultAI technology acceptanceEFL learners’ behavioral engagementMotivationStructural equation modeling (SEM)

Identifiers

PMID41006315
PMCPMC12475470

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.