The Relationship Between AI Self-Efficacy and AI Trust of College Students
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Abstract
This study investigates the relationship between AI self-efficacy and AI trust among college students, employing a quantitative research strategy with a non-experimental correlational approach. Data were collected from 372 participants using a Google Forms questionnaire designed with modified items assessing AI self-efficacy and AI trust, structured on a 5-point Likert scale. The analysis utilized various statistical techniques to ensure the validity and reliability of measurement models, including Average Variance Extracted (AVE) for convergent validity, the Heterotrait-Monotrait Ratio (HTMT) for discriminant validity, and Cronbach’s alpha for internal consistency. Results indicated an R-square value of 0.329, suggesting that AI self-efficacy explains 32.9% of the variance in AI trust, thereby demonstrating a moderate explanatory power. The adjusted R-square value of 0.327 further confirms the absence of overfitting in the model. These findings highlight the significance of AI self-efficacy in fostering trust in AI among students, while suggesting the potential influence of other unexamined factors.
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