Ce que l’IA fait à l’écriture scientifique : ethnographie participative d’un hackathon interdisciplinaire
Ethnographie participative d’un hackathon interdisciplinaire
DOI:
https://doi.org/10.31468/dwr.1173Keywords:
écriture scientifique ; grands modèles de langage (LLM) ; ethnographie participante ; collaboration interdisciplinaireAbstract
This article explores the way large language models (LLMs) are transforming scientific writing practices. The approach adopted combines ethnography and participatory science. The ethnographic work took place during the hackathon “What AI Does to the Practice of Science” (IXXI and CBPsmn, ENS Lyon, July 8–9, 2025), and was led by an ethnographer who co‑constructed data collection and analysis protocols with the participants who actively contributed to the production and the interpretation of the data. Drawing on the perspective of distributed cognition (Hutchins, 1995) as well as frameworks from participatory research (Houillier & Merilhou-Goudard, 2016), we show that every stage of the scientific writing becomes a negotiated space between humans and the machine: the collective crafting of prompts, critical iteration on generated texts, and the participatory validation of resulting final presentation. Far from automating the process, LLMs redefine collaborative dynamics, forms of acculturation, and regimes of scientificity, giving rise to a mediated, co-produced reflexivity.
References
Alan Turing Institute. (2022). Review of digital research infrastructure requirements for AI. https://www.turing.ac.uk/sites/default/files/2022-09/ukri-requirements-report_final_edits.pdf
Altmäe, S., Sola-Leyva, A., et Salumets, A. (2023). Artificial intelligence in scientific writing: A friend or a foe? Reproductive BioMedicine Online, 47(1), 3–9.
https://doi.org/10.1016/j.rbmo.2023.04.009
Atlan, H., Bourgain, C., Chneiweiss, H., Eisinger, F., Vidal, C., et al. (2025). Recommandations de bonnes pratiques suite à l’analyse des questions éthiques soulevées par l’utilisation de l’Intelligence Artificielle dans la recherche à l’Inserm.
Guidedebonnespratiquesdel’IntelligenceArtificielleàl’Inserm/Février 2025,2025. inserm-04975393
Bazerman, C. (1988). Shaping written knowledge: The genre and activity of the experimental article in science. University of Wisconsin Press.
Basso Fossali, P. (2025). Rationalités correctives et intelligence artificielle assistée : Les doubles contraintes des humanités numériques. Semiotica, 2025(262), 71–109.
https://doi.org/10.1515/sem-2024-0189
Benbouzid, B. (2025). Écrire à l’université à l’heure des IA génératives : égalité instrumentale, inégalité structurelle. AOC. https://aoc.media/analyse/2025/05/07/ecrire-a-luniversite-a-lheure-des-ia-generatives-egalite-instrumentale-inegalite-structurelle-2-2/
Bommasani, R., Hudson, D. A., Adeli. et al. (2021). On the opportunities and risks of foundation models (arXiv:2108.07258). arXiv. https://doi.org/10.48550/arXiv.2108.07258
Bouchard, A. (2025). #WorkInProgress : IA générative et outils de recherche de littérature académique. URFISTinfo.
https://hal.archives-ouvertes.fr/hal-04960003
Bourdieu, P. (1984). Homo academicus. Les Éditions de Minuit.
Calenda. (2025). Actes du colloque Ethnographie et recherches participatives. https://calenda.org/1088968
Cardon, D. (2015). À quoi rêvent les algorithmes ? Nos vies à l’heure du big data. Seuil.
Cartwright, N. (2023). Getting serious about statistics: Scientific method and the reproducibility crisis. Cambridge University Press.
Certeau, M. (de) (1980). L’invention du quotidien. Union générale d’éditions.
Chauveau, H. (2024). Accompagner l’acculturation entre acteurs associatifs et apprentis chercheurs : l’expérience de la Boutique des Sciences de Lyon en tant que tiers‑veilleur. Actes du colloque Ethnographie et recherches participatives. https://ethno-rech part.sciencesconf.org/data/pages/Actes_colloque_Ethnographie_et_recherches_participatives.pdf
Chen, N., HuiKai, A. L., Wu, J. et al. (2025). XtraGPT: LLMs for human-AI collaboration on controllable academic paper revision (arXiv:2505.11336). arXiv. https://doi.org/10.48550/arXiv.2505.11336
Cobley, P. & Stjernfelt, F. (2015). Scaffolding Development and the Human Condition. Biosemiotics, 8 (2), 291-304. https://doi.org/10.1007/s12304-015-9238-z
Cummings, L. (2023). Writing processes in the digital age: A networked interpretation. In O. Kruse, C. Rapp, C. M. Anson, K. Benetos, E. Cotos, A. Devitt, & A. Shibani (Eds.), Digital writing technologies in higher education: Theory, research, and practice (pp. 485–497). Springer. https://doi.org/10.1007/978-3-031-36033-6_30
Dorotic, M., Stagno, E., & Warlop, L. (2024). AI on the street: Context-dependent responses to artificial intelligence. International Journal of Research in Marketing, 41(1), 113–137.
https://doi.org/10.1016/j.ijresmar.2023.08.010
Ethnographie et Recherches Participatives. (2025). Recherche participative en santé ou urbanisme : ethnographes et citoyens collectent et analysent ensemble des récits, cartographies, pratiques pour produire des solutions adaptées aux territoires. Université Lyon 2. https://www.univ-lyon2.fr/recherche/agenda-scientifique/ethnographie-et-recherches-participatives
Eyraud, B. (2024). L’auto-ethnographie coopérative et coresponsable dans la pratique et la gouvernance d’une démarche de recherche citoyenne : L’exemple de la démarche Capdroits. Actes du colloque Ethnographie et recherches participatives. https://ethno-rech part.sciencesconf.org/data/pages/Actes_colloque_Ethnographie_et_recherches_participatives.pdf
Goody, J. (1977). The domestication of the savage mind. Cambridge University Press.
Goody, J. (1986). The logic of writing and the organization of society. Cambridge University Press.
Granjon, F. (2022). Classes populaires et usages de l’informatique connectée : Des inégalités sociales-numériques. Presses des Mines.
Grogan, K. E. (2020). Writing science: What makes scientific writing hard and how to make it easier. The Bulletin of the Ecological Society of America, 102(1), e01800. https://doi.org/10.1002/bes2.1800
Haibe-Kains, B., Adam, G.A., Hosny, A. et al. (2020). Transparency and reproducibility in artificial intelligence. Nature, 586, E14–E16. https://doi.org/10.1038/s41586-020-2766-y
Hosseini, M. & Horbach, S.P.J.M. (2023) Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review. Res Integr Peer Rev 8, 4. https://doi.org/10.1186/s41073-023-00133-5
Houillier, F., et Merilhou-Goudard J-B., (2016). Les sciences participatives en France : États des lieux, bonnes pratiques et recommandations, rapport.
Hutchins, E. (1995). Cognition in the wild. Cambridge. MIT Press.
Kobak, D., González-Márquez, R., Horvát, E.-Á., et Lause, J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. arXiv.
https://doi.org/10.48550/arXiv.2406.07016
Lahire, B. (2021). Culture écrite et inégalités scolaires. Sociologie de l’« échec scolaire » à l’école primaire. Presses universitaires de Lyon.
Lang, M., Drake, S., et Olson, J. (2006). Discourse and the new didactics of scientific literacy. Journal of Curriculum Studies, 38(2), 177–188. https://doi.org/10.1080/00220270500122539
Latour, B., et Woolgar, S. (1986). Laboratory life: The construction of scientific facts (2nd ed.). Princeton University Press.
Liang, W., Izzo, Z., Zhang, Y., Lepp, H., Cao, H., et al. (2024). Monitoring AI-Modified Content at Scale : A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews. arXiv:2403.07183. https://doi.org/10.48550/arXiv.2403.07183
Lin, Z. (2023). Why and how to embrace AI such as ChatGPT in your academic life. Royal Society Open Science, 10, 230658 https://doi.org/10.1098/rsos.230658
Lin, C. W., et Zhu, W. (2025). Divergent llm adoption and heterogeneous convergence paths in research writing. arXiv:2504.13629.
Luo, Z., Yang, Z., Xu, Z., Yang, W., et Du, X. (2025). LLM4SR: A survey on large language models for scientific research. arXiv:2501.04306. https://doi.org/10.48550/arXiv.2501.04306
Marvin, G., Hellen, N., Jjingo, D., et Nakatumba-Nabende, J. (2024). Prompt Engineering in Large Language Models. In I. J. Jacob, S. Piramuthu, & P. Falkowski-Gilski (Éds.), Data Intelligence and Cognitive Informatics, 387‑402. Springer Nature. https://doi.org/10.1007/978-981-99-7962-2_30
Pal, S., Bhattacharya, M., Islam, M. A., et Chakraborty, C. (2024). AI-enabled ChatGPT or LLM: a new algorithm is required for plagiarism-free scientific writing. International Journal of Surgery, 110(2), 1329-1330.
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., et Tekade, R. K. (2021). Artificial intelligence in drug discovery and development. Drug Discovery Today, 26(1), 80–93. https://doi.org/10.1016/j.drudis.2020.10.010
Quemener, E., et Corvellec, M. (2013). Sidus—the solution for extreme deduplication of an operating system. Linux Journal, 2013(235), 3–9.
https://dl.acm.org/doi/abs/10.5555/2555789.2555792
Reif, J. A., Larrick, R. P., et Soll, J. B. (2025). Evidence of a social evaluation penalty for using AI. PNAS Proceedings of the National Academy of Sciences of the United States of America, 122(19). https://doi.org/10.1073/pnas.2426766122
Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2, 420. https://doi.org/10.1007/s42979-021-00815-1
Skopec, M., Issa, H., Reed, J., et Harris, M. (2020). The role of geographic bias in knowledge diffusion: A systematic review and narrative synthesis. Research Integrity and Peer Review, 5, 1–14. https://doi.org/10.1186/s41073-019-0088-0
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551.
https://doi.org/10.1016/j.ijhcs.2020.102551
Strathern, M. (2004). Partial connections. Rowman & Littlefield Publishers.
Suchman, L. A. (2007). Human-machine reconfigurations: Plans and situated actions (2nd ed.). Cambridge University Press.
Tang, X., Duan, X., et Cai, Z. G. (2024). Large language models for automated literature review: An evaluation of reference generation, abstract writing, and review composition (arXiv 2412.13612). arXiv. https://doi.org/10.48550/arXiv.2412.13612
The Royal Society. (2024). Science in the age of AI: How artificial intelligence is changing the nature and method of scientific research. https://royalsociety.org/-/media/policy/projects/science-in-the-age-of-ai/science-in-the-age-of-ai-report.pdf
Yi, Y. (2021). Establishing the concept of AI literacy: Focusing on competence and purpose. JAHR-–European Journal of Bioethics, 12 (2), 353–368.
Zamora, J. (2017). I'm sorry, Dave, I'm afraid I can't do that: Chatbot perception and expectations. In Proceedings of the 5th International Conference on Human Agent Interaction, 253–256. https://doi.org/10.1145/3125739.3125766
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nathan Ferret, Patrice Abry, Rémy Cazabet, Philippe Gabriel, Lucie Gournay, Jean-Philippe Magué, Emmanuel Quemener, Julien Thiburce

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
If this article is selected for publication in Discourse and Writing/Rédactologie, the work shall be published electronically under the terms of Creative Commons Attribution-ShareAlike (CC BY-SA 4.0). This license allows users to adapt and build upon the published work, but requires them to attribute the original publication and license their derivative works under the same terms. There is no fee required for submission or publication. Authors retain unrestricted copyright and all publishing rights, and are permitted to deposit all versions of their paper in an institutional or subject repository.