Epistemic Trust in Generative AI as an Information Source: Development and Validation of the Trust in AI-Epistemic Scale (TAI-E)

As generative AI increasingly mediates everyday information, users must judge not only whether a claim is true but whether the system conveying it is a legitimate source of belief. This article develops and validates the Trust in AI-Epistemic Scale (TAI-E), measuring epistemic trust in generative AI across three dimensions: attribution of epistemic authority, perceived epistemic reliability, and suspension of critical judgment. In a survey of part-time higher-education students (N = 412; ages 18–63), the three-factor structure was replicated across a calibration–validation split (CFI = 0.944; RMSEA = 0.085; subscale α = 0.84–0.87). Granting a system epistemic authority and suspending critical judgment toward it behaved as partly opposing stances, so the scale captures a configuration rather than a single trust score. Self-reported reflective processing accompanied higher authority and reliability attributions together with greater critical engagement. Most strikingly, self-reported reflectiveness barely tracked behavioural cognitive reflection (r = 0.14), and only the self-report predicted trust—suggesting it reflects epistemic self-image as much as reflective capacity. Relations among constructs are reported as construct-validity associations rather than causal effects. The TAI-E provides a psychometrically grounded tool for studying epistemic trust, AI literacy, and well-founded reliance on AI.