What happened
A Carnegie Mellon, MIT, and Cornell research team posted two randomized case studies on August 6 testing whether short AI conversations could counter conspiracy beliefs while facts about major events were still emerging.
The researchers analyzed 472 US adults who expressed conspiratorial views after the July 2024 attempt to assassinate Donald Trump and 1,035 after the September 2025 assassination of Charlie Kirk. Participants were randomly assigned to a tailored debunking dialogue, an unrelated AI conversation, or a static list of contemporaneous facts.
The dialogue groups completed at least five exchanges with Gemini 1.5 Pro in the first experiment and Gemini 2.5 Pro in the second. Both models received a curated fact base that separated confirmed information, debunked claims, and unresolved questions; the second model could also search the web only to verify factual information.
On a 0-to-100 measure of belief in each participant's own stated theory, the tailored dialogues produced reductions of 6.95 points relative to the unrelated-chat control in the first experiment and 7.56 points in the second. Differences from the static fact sheet were 5.60 and 6.56 points. The paper reports standardized effects of roughly 0.32 to 0.38 for those comparisons.
Read the primary source: Costello and colleagues' research paper on arXiv ↗
Why it matters
The result suggests that a conversational system can do more than repeat a correction: it can adapt facts, questions, and uncertainty to the specific reason a person gives for a belief.
That distinction is useful during fast-moving events, when verified evidence is incomplete and a generic fact sheet may not address the claim a person actually finds persuasive. The paper's strategy analysis found that the model leaned more on credible sources, open questions, and caution when little was known, then used more direct evidence when the second event had a larger factual record.
The authors also report limited evidence of later spillover. Two months after the first experiment, participants assigned to the dialogue were less likely than pooled controls to endorse two conspiratorial interpretations of a subsequent event. In the second follow-up, direct treatment assignment did not significantly reduce beliefs about a later shooting, although a separate preregistered persistence analysis and a broader conspiracy measure suggested smaller carry-over effects.
The study does not justify automatically deploying persuasive bots into public conversations. A system instructed to change beliefs holds unusual power, and the authors note that similar techniques can also increase belief in false claims. Any real service would need transparent authorship, reliable event grounding, safeguards against political targeting, and independent tests of accuracy and unintended effects.
What to watch next
Watch for peer review, replication beyond two US political crises, and field evidence showing whether people choose to use such a tool without being recruited into a study.
This is a new preprint built around two case studies. The first experiment was not preregistered, the second was, and both measured self-reported beliefs immediately after a short online interaction rather than real-world sharing behavior, voting, or long-term trust.
The analyzed samples included only participants whose open responses were classified as conspiratorial or uncertain by GPT-4o, although the authors report similar patterns under alternate classification rules. Both studies recruited US adults through CloudResearch, so the findings may not transfer to other countries, languages, events, or populations.
The models were not relying on unaided knowledge: researchers supplied carefully assembled fact bases and explicit persuasion instructions. Future work should test who maintains those facts under deadline pressure, how errors are corrected, whether opposing viewpoints are treated consistently, and when a system should preserve uncertainty instead of trying to persuade.
