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How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments

Volk, Sophia Charlotte; Schäfer, Mike S; Lombardi, Damiano; Mahl, Daniela; Yan, Xiaoyue (2025). How generative artificial intelligence portrays science: Interviewing ChatGPT from the perspective of different audience segments. Public Understanding of Science, 34(2):132-153.

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.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Department of Communication and Media Research
Dewey Decimal Classification:070 News media, journalism & publishing
Scopus Subject Areas:Social Sciences & Humanities > Communication
Social Sciences & Humanities > Developmental and Educational Psychology
Social Sciences & Humanities > Arts and Humanities (miscellaneous)
Uncontrolled Keywords:generative artificial intelligence, large language models, human–machine communication, representations of science, science communication, segmentation analysis, talking with machines
Language:English
Date:1 February 2025
Deposited On:20 Dec 2024 09:50
Last Modified:09 Jul 2025 08:16
Publisher:Sage Publications
ISSN:0963-6625
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1177/09636625241268910
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  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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