Objective: This study aims to examine how developers' cognitive trust and self-efficacy influence their attitudes toward the use of AI coding assistants.

Background: AI-based coding assistants are increasingly integrated into software development workflows, supporting tasks such as code generation, debugging, and productivity enhancement. As these tools become more common, understanding the psychological factors that shape developers' acceptance is essential. Cognitive trust and self-efficacy, known to affect technology adoption, may play significant roles in shaping attitudes toward AI-assisted development.

Method: An online survey was conducted with 283 software developers. Measures included perceived usefulness, perceived ease of use, ability-based trust, consistency-based trust, self-efficacy, and attitude toward AI coding assistants. The collected data were analyzed using partial least squares structural equation modeling (PLS-SEM) to validate the research model and test the hypothesized relationships.

Results: Self-efficacy positively influenced perceived usefulness, perceived ease of use, ability-based trust, and consistency-based trust. In explaining developers' attitudes, perceived usefulness, perceived ease of use, and ability-based trust were significant predictors, collectively accounting for 49.3% of the variance. Consistency-based trust did not significantly affect attitude.

Conclusion: Developers' perceived competence in effectively using AI coding assistants is a key determinant of technology acceptance. Ability-based trust plays a more critical role than consistency-based trust in shaping positive attitudes.

Application: These findings provide practical implications for designing AI assistant systems that foster trust and usability, as well as for developing training programs that enhance developers' confidence and skill in using AI-based development tools.