Dialogues with large language models reduce conspiracy beliefs even when the AI is perceived as human.

ABSTRACT: Although conspiracy beliefs are often viewed as resistant to correction, recent evidence shows that personalized, fact-based dialogues with a large language model (LLM) can reduce them. Is this effect driven by the debunking facts and evidence, or does it rely on the messenger being an AI? In other words, would the same message be equally effective if delivered by a human? To answer this question, we conducted a preregistered experiment (N = 955) in which participants reported either a conspiracy belief or a nonconspiratorial but epistemically unwarranted belief and interacted with a LLM that argued against that belief using facts and evidence. We randomized whether the debunking LLM was characterized as an AI tool or a human expert and whether the model used human-like conversation

SUBMITTER: Boissin E 

PROVIDER: S-EPMC12578366 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature