Journal article
The AI turn in science: a reflexive thematic analysis of scientists’ commentaries and educational implications
- Abstract:
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Background
Artificial intelligence (AI) is increasingly embedded in scientific research, reshaping how knowledge is produced, evaluated, and legitimised. While much existing research examines AI as a technical innovation or pedagogical tool, less attention has been paid to how scientists themselves articulate its role within science.
Purpose
This study examines how scientists discuss AI in Nature and Science commentaries andconsiders implications for the nature of science (NOS) and science education.
Sample
The dataset comprises AI-related commentaries published in Nature (N = 242) and Science (N= 226) between 2021 and 2024, covering scientific discourse both prior to and following thepublic release of ChatGPT in late 2022.
Design and methods
Data were analysed using reflexive thematic analysis (RTA), with the Family ResemblanceApproach to NOS (FRA–NOS) as analytical orientations. This study is among the first tocombine RTA with FRA–NOS to identify cross-domain patterns across epistemic,methodological, social, institutional, political, and value-laden dimensions of science. Suchcombination is methodologically resonant with Wittgenstein’s notion of family resemblance,foregrounding overlapping patterns of meaning rather than discrete categories.ResultsAI is not framed as a discrete tool but as a cross-cutting condition that reconfigures epistemicauthority, infrastructure, scientific labour, governance, and public trust.
Conclusion
The analysis points to the importance of approaching AI in science education not merely asan add-on topic or technical skill, but as an opportunity to engage learners with science as asocially embedded, institutionally governed, and value-laden practice. Concrete examples areprovided through educational applications of FRA-NOS. Methodologically, the studyillustrates how combining reflexive thematic analysis with the engagement of FRA supportsthe identification of cross-domain NOS patterns that may be less visible in domain-specific orreliability-driven analyses.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1080/02635143.2026.2669549
Authors
- Publisher:
- Taylor & Francis
- Journal:
- Research in Science and Technological Education More from this journal
- Publication date:
- 2026-05-08
- Acceptance date:
- 2026-04-29
- DOI:
- EISSN:
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1470-1138
- ISSN:
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0263-5143
- Language:
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English
- Keywords:
- Pubs id:
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2413713
- Local pid:
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pubs:2413713
- Deposit date:
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2026-05-02
- ARK identifier:
Terms of use
- Copyright holder:
- Chan and Erduran
- Copyright date:
- 2026
- Rights statement:
- © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License(http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any med-ium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this articlehas been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
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