ArticlePublic understanding of science (Bristol, England)2025
How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments.
Article in Public understanding of science (Bristol, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Beyond text generation: a comprehensive evaluation of ChatGPT-5.5 for scientific narrative review writing on sleeve gastrectomy.Updates in surgery · 2026Article
- Generics in science communication: Misaligned interpretations across laypeople, scientists, and large language models.Public understanding of science (Bristol, England) · 2026Article
- Assessment of vaccine information accuracy across large language models.Frontiers in public health · 2026Article
- Evaluating AI-Generated Patient Education Guides: A Comparative Study of ChatGPT and Deepseek.Cureus · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence in general and ChatGPT in particular have risen in importance. ChatGPT is widely known and used increasingly as an information source for different topics, including science. It is therefore relevant to examine how ChatGPT portrays science and science-related issues. Research on this question is lacking, however. Hence, we simulate "interviews" with ChatGPT and reconstruct how it presents science, science communication, scientific misbehavior, and controversial scientific issues. Combining qualitative and quantitative content analysis, we find that, generally, ChatGPT portrays science largely as the STEM disciplines, in a positivist-empiricist way and a positive light. When comparing ChatGPT's responses to different simulated user profiles and responses from the GPT-3.5 and GPT-4 versions, we find similarities in that the scientific consensus on questions such as climate change, COVID-19 vaccinations, or astrology is consistently conveyed across them. Beyond these similarities in substance, however, pronounced differences are found in the personalization of responses to different user profiles and between GPT-3.5 and GPT-4.
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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.