ArticleScientific reports2024
Conversational presentation mode increases credibility judgements during information search with ChatGPT.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Conversational AI increases political knowledge as effectively as self-directed internet search.PNAS nexus · 2026Article
- What Makes an AI Persuasive? The Interaction Effect of AI Shopping Assistants and Algorithmic Recommendation Cues on Consumers' Organic Food Purchase Intention.Foods (Basel, Switzerland) · 2026Article
- Chatbots reduce health-related conspiracy beliefs not because of but despite being perceived as AI.Scientific reports · 2026Article
- Responsible use of generative AI in Chinese vocational education: eye-tracking evidence on source verification and immediate near-transfer across three information-seeking conditions.Frontiers in psychology · 2026Article
- A longitudinal analysis of declining medical safety messaging in generative AI models.NPJ digital medicine · 2025Article
- Unlocking the potential of ChatGPT in detecting the XCOScientific reports · 2025Article
- Interacting with fallible AI: is distrust helpful when receiving AI misclassifications?Frontiers in psychology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
People increasingly use large language model (LLM)-based conversational agents to obtain information. However, the information these models provide is not always factually accurate. Thus, it is critical to understand what helps users adequately assess the credibility of the provided information. Here, we report the results of two preregistered experiments in which participants rated the credibility of accurate versus partially inaccurate information ostensibly provided by a dynamic text-based LLM-powered agent, a voice-based agent, or a static text-based online encyclopedia. We found that people were better at detecting inaccuracies when identical information was provided as static text compared to both types of conversational agents, regardless of whether information search applications were branded (ChatGPT, Alexa, and Wikipedia) or unbranded. Mediation analysis overall corroborated the interpretation that a conversational nature poses a threat to adequate credibility judgments. Our research highlights the importance of presentation mode when dealing with misinformation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.