Evidence map›Paper›PMID 42495203›Full record

ArticleFrontiers in psychology2026

Divergent generative AI pathways in higher education: a parallel mediation analysis of autonomous use and human-machine synergy.

Yan Cheng, Xinran Liu, Haibo Liu

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. 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

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

1 citing paper in PubMed.

  1. Review
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.

Yan ChengSchool of General Education, Shandong Huayu University of Technology, Dezhou, China.
Xinran LiuSchool of Economics and Management, Shijiazhuang University, Shijiazhuang, China.
Haibo LiuDezhou Information Engineering Secondary Vocational School, Dezhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The integration of Generative Artificial Intelligence (GenAI) into higher education has raised important questions regarding its influence on student learning outcomes. Drawing on Self-Regulated Learning (SRL) theory, this study examines how learning motivation (LM) relates to knowledge application ability (KAA) through two parallel pathways: human-machine synergy (HMS) and autonomous learning technology (ALT) use. Methods: Data were collected from 760 Chinese university students and analyzed using structural equation modeling with bias-corrected bootstrapping. Results: The results indicate that LM has a significant positive direct effect on KAA. Among the two mediating pathways, ALT use demonstrates a small but significant partial mediating effect, whereas the HMS pathway is not statistically significant. Discussion: These findings suggest that motivated learners may benefit from using GenAI as a self-directed learning support tool, while deeper forms of human-AI collaboration do not necessarily generate stronger knowledge application outcomes. A possible explanation is that excessive cognitive offloading and insufficient algorithmic literacy may weaken the effectiveness of synergistic AI interaction. This study contributes to the literature by highlighting the conditional nature of AI-supported learning and emphasizing the importance of maintaining cognitive autonomy in AI-assisted educational environments.

Indexed as

autonomous learning technologygenerative artificial intelligencehuman-machine synergyknowledge application abilitylearning motivationparallel mediationself-regulated learning

Identifiers

PMID42495203
PMCPMC13391850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.