AIF Insights No. 28 (2026) | AI, Gettier, and the Illusion of Knowing

This article explores the epistemological challenges posed by generative AI by applying Edmund Gettier’s classic problem—where a belief is true purely by accident or based on flawed evidence—to machine-generated outputs. The author argues that while AI can rapidly produce fluent, persuasive, and accurate answers, linguistic plausibility does not guarantee a defensible link between claims and underlying evidence. Across fields like medicine and history, AI risks creating the “illusion of knowing” when correct conclusions stem from irrelevant variables, faulty citations, or linguistic refinement of flawed reasoning. Consequently, the paper emphasizes that AI verification requires evaluating the reliability of the underlying process rather than just checking factual truth, calling for an educational shift from basic AI literacy to critical epistemic literacy that enables humans to discern when an answer is genuinely justified.