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Research on factors affecting trust formation of generative AI agents in human-AI interaction contexts
1College of Publishing, University of Shanghai for Science and Technology, Shanghai, China.
Frontiers in Psychology
|July 31, 2026
Summary
For generative AI (GenAI) in development, perceived explainability and intention alignment are key to building user trust. These factors significantly boost perceived reliability, which in turn enhances human-agent trust.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence Ethics
- Cognitive Science
Background:
- Generative artificial intelligence (GenAI) is increasingly integrated into professional workflows.
- The probabilistic nature of GenAI raises concerns about user trust due to potential machine hallucinations.
- Semi-technical users' trust in GenAI agents is crucial for effective adoption and collaboration.
Purpose of the Study:
- To investigate the factors influencing semi-technical users' trust in generative AI agents.
- To examine the roles of perceived explainability, intention alignment, and sense of agency in trust formation.
- To explore the moderating effects of task complexity and domain self-efficacy on these relationships.
Main Methods:
- Survey data collected from 312 semi-technical users with AI-assisted development experience.
- Structural equation modeling (SEM) was employed for data analysis.
- The study focused on interaction trust, perceived reliability, and related user perceptions.
Main Results:
- Perceived explainability and intention alignment strongly predicted perceived reliability.
- Perceived reliability significantly mediated the relationship between explainability/intention alignment and human-agent trust.
- Perceived task complexity and domain self-efficacy moderated the associations, with domain self-efficacy showing an unexpected positive effect.
Conclusions:
- Transparent and intention-aligned GenAI designs are vital for fostering reliability-based trust.
- User agency's role in trust formation may extend beyond perceived reliability, necessitating further investigation.
- Understanding these factors is critical for designing trustworthy GenAI systems in development contexts.
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