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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Using conversational AI to reduce science skepticism.

Matthew J Hornsey1, Aimee E Smith1, Samuel Pearson1

  • 1The University of Queensland, Queensland, Australia.

Current Opinion in Psychology
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) show promise in combating science misinformation. Research indicates that conversations with LLMs typically reduce skepticism and endorsement of misinformation, aiding science communication.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Science communication
  • Artificial Intelligence
  • Public trust in science

Background:

  • Widespread mistrust in scientific consensus on critical issues like climate change and vaccination hinders global threat response.
  • Generative AI, including large language models (LLMs), poses a risk of spreading misinformation at scale.
  • LLMs also present an opportunity to reduce misinformation and conspiracy theories, potentially transforming science communication.

Purpose of the Study:

  • To review empirical research on using conversational AI, like ChatGPT, to address mistrust in science.
  • To evaluate the potential of LLMs in defusing skepticism around contentious scientific topics.

Main Methods:

  • Summarization of the current empirical research landscape on conversational AI and science communication.
  • Analysis of studies investigating interactions between users and LLMs on scientific topics.

Main Results:

  • Empirical studies show negligible evidence of LLMs reproducing conspiracy theories or misinformation.
  • Conversations with LLMs generally decrease participants' science skepticism and belief in misinformation.
  • LLM interactions tend to foster greater trust in scientific consensus.

Conclusions:

  • Current LLMs show potential as a tool to enhance science communication strategies.
  • Safeguards are necessary to maintain informational integrity and public trust when using LLMs.
  • LLMs can be a valuable addition to efforts aimed at rebuilding public trust in science.