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ZENODO
Preprint . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
ZENODO
Preprint . 2026
License: CC BY
Data sources: Datacite
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L'histoire au révélateur de l'intelligence artificielle. Ce que les grands modèles de langue font aux pratiques historiennes. Version 1.0, preprint

Authors: rygiel, philippe;

L'histoire au révélateur de l'intelligence artificielle. Ce que les grands modèles de langue font aux pratiques historiennes. Version 1.0, preprint

Abstract

Ce document est la version préliminaire d'un article dont la publication est prévue dans la revue Le Mouvement Social en 2027. L'article propose une réflexion épistémologique sur les effets de l'intelligence artificielle générative, et plus particulièrement des grands modèles de langue (LLMs), sur les pratiques de la recherche historique. Il examine les transformations des opérations de collecte, de traitement, d'organisation, d'analyse et de production des connaissances historiques, en les replaçant dans une histoire plus longue des technologies intellectuelles et de la formalisation des pratiques savantes. S'appuyant sur un large état de la littérature internationale ainsi que sur plusieurs expérimentations conduites par l'auteur, le texte analyse les usages actuels de l'IA dans les sciences historiques : transcription automatisée des archives, extraction d'information, graphes de connaissances, architectures RAG, simulation historique, assistance à l'écriture scientifique et production d'arguments. Il défend l'idée que ces évolutions invitent moins à une rupture qu'à un réexamen des cadres épistémologiques élaborés depuis plusieurs décennies dans l'histoire quantitative, les humanités numériques et l'informatique appliquée à l'histoire. Ce dépôt correspond à la version auteur diffusée avant publication. La version définitive, une fois parue dans Le Mouvement Social, pourra comporter des modifications éditoriales, stylistiques et bibliographiques. Pour toute citation académique, il est recommandé de se référer à la version publiée dès qu'elle sera disponible.

This repository contains the preprint version of an article accepted for publication in the French journal Le Mouvement Social (expected publication: 2027). The article examines the epistemological implications of generative artificial intelligence—and particularly Large Language Models (LLMs)—for historical research and scholarly practice. Rather than treating these technologies as an isolated technological disruption, it situates them within the longer history of intellectual technologies and the progressive formalisation of historical methods. Drawing on an extensive international literature as well as a series of original experiments conducted by the author, the paper discusses the emerging use of AI throughout the historical research workflow, including handwritten text recognition, information extraction, knowledge graph construction, Retrieval-Augmented Generation (RAG), historical simulation, scholarly writing, translation, and argumentation. It argues that these developments require historians to reconsider long-standing epistemological questions concerning evidence, uncertainty, interpretation, and the production of historical knowledge. This repository contains the author's preprint. The published version may differ following peer review and editorial revision. Readers are encouraged to cite the final published version once it becomes available.

Keywords

historiography, llm, intelligence artificielle, histoire, epistemology, large language models, history, histoire computationelle, computational history, historiographie, artificial intelligence, historical methods

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average