<![CDATA[User trust in artificial intelligence (AI) is shaped by a complex interaction of cognitive and experiential factors, yet empirical evidence on how these psychological determinants jointly predict trust remains limited. This study investigates a multi-factor psychological model of user trust using secondary data from 6,678 participants interacting with various AI systems. Five predictors—cognitive load, trust history, prior AI exposure, time pressure, and cultural factors—were examined using correlational and multiple regression analyses. The results show that trust history is the strongest positive predictor of user trust (β = .485, p < .001), while cognitive load significantly reduces trust (β = –.151, p < .001). Other variables did not exhibit significant effects. The model explains 25.7% of the variance in latent trust scores, demonstrating meaningful predictive power for psychological factors alone. These findings highlight the central role of cognitive ease and positive experiential familiarity in shaping trust in AI. The study contributes to Human–AI Interaction research by clarifying which psychological processes most strongly influence trust and by offering implications for designing AI systems that foster reliable, low-friction user experiences.]]>
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