Same trust, different paths: how older adults reach similar AI trust through use and non-use

As AI systems become increasingly prevalent, older adults engage with them through diverse approaches, ranging from active use to deliberate non-use. But how does trust in AI differ across these orientations? Our explanatory sequential mixed-methods study, including a survey (n = 217) and interviews (n = 20), reveals a paradox: older adults who actively use AI and those who intentionally choose not to report similarly moderate levels of trust, yet arrive at that trust through fundamentally different pathways. Among self-identified users, greater AI familiarity predicts higher trust, mediated by growing confidence through hands-on experience. Among non-users, the pattern reverses: the most AI-familiar individuals report the lowest trust, as knowledge without direct engagement fosters skepticism rather than confidence. Interview findings unpack the sense-making behind these patterns, revealing two distinct logics: practice-based trust, built through iterative use, error calibration, and continuous verification; and delegated trust, maintained by routing AI-related decisions through accountable human intermediaries while personally opting out. We discuss implications for inclusive AI design and literacy that support older adults’ distinct pathways to trust, validating non-use as an informed decision rather than a deficit.