Evidence map›Paper›PMID 41179846›Full record

ArticlePNAS nexus2025

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

Esther Boissin, Thomas H Costello, Daniel Spinoza-Martín, David G Rand, Gordon Pennycook

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Intelligent Systems, Vulnerable Minds: A Framework for Radicalization to Violence in the Age of AI.Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc · 2026
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Esther BoissinDepartment of Psychology, Cornell University, Ithaca, NY 14853, USA.ORCID https://orcid.org/0000-0001-6485-7466
Thomas H CostelloDepartment of Psychology, American University, Washington, DC 20016, USA.ORCID https://orcid.org/0000-0002-5188-3881
Daniel Spinoza-MartínDepartment of Psychology, Cornell University, Ithaca, NY 14853, USA.ORCID https://orcid.org/0000-0003-3993-1593
David G RandSloan School of Management, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.ORCID https://orcid.org/0000-0001-8975-2783
Gordon PennycookDepartment of Psychology, Cornell University, Ithaca, NY 14853, USA.ORCID https://orcid.org/0000-0003-1344-6143

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

artificial intelligencebelief changeconspiracy beliefsdebunking

Identifiers

PMID41179846
PMCPMC12578366

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.