Evidence map›Paper›PMID 42535018›Full record

ArticleFrontiers in psychology2026

Research on factors affecting trust formation of generative AI agents in human-AI interaction contexts.

Li Gong, Yaoming Gong

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.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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

2 authors.

Li GongCollege of Publishing, University of Shanghai for Science and Technology, Shanghai, China.
Yaoming GongCollege of Publishing, University of Shanghai for Science and Technology, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As generative artificial intelligence (GenAI) becomes deeply embedded in cognitive workflows, its inherent probabilistic generation mechanisms and the possibility of machine hallucinations may complicate the stability of semi-technical users' trust. This study examined how semi-technical users' interaction trust toward generative AI agents is associated with perceived explainability, perceived intention alignment, and perceived sense of agency, while also considering the moderating roles of perceived task complexity and domain self-efficacy. Methods: Survey data from 312 semi-technical users with experience in AI-assisted development were analyzed using structural equation modeling. Results: The results indicated that perceived explainability and perceived intention alignment were strongly and positively associated with perceived reliability, whereas perceived sense of agency showed only a small positive association with perceived reliability. Perceived reliability, in turn, was positively associated with human-agent trust. Notably, perceived reliability significantly mediated the associations between these two predictors and human-agent trust, whereas the indirect association involving perceived sense of agency approached but did not reach the conventional significance threshold and was therefore interpreted only as suggestive evidence. Furthermore, perceived task complexity positively conditioned the relationship between perceived explainability and perceived reliability. Domain self-efficacy also showed a significant positive moderation pattern in the relationship between perceived intention alignment and perceived reliability, which was opposite to the hypothesized negative direction. Discussion: These findings suggest that transparent and intention-aligned interaction designs may be especially important for supporting reliability-based trust formation in generative AI-assisted development, while user agency may require additional mechanisms beyond perceived reliability to account for its role in trust formation.

Indexed as

generative AIhuman-AI collaborationperceived reliabilitysemi-technical usersstructural equation modelingtrust formation

Identifiers

PMID42535018
PMCPMC13422554

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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.