Artificial Intelligence in Historical Research: a Virtual Assistant or a Generator of Quasi-Knowledge?

Cover Page

Cite item

Full Text

Abstract

The article examines the current issues of the application of artificial intelligence (AI) methods and technologies in historical research. It outlines two waves of AI development, with the second wave focusing on artificial neural networks, machine learning (including deep learning), generative AI, and large language models (LLMs). Two areas of AI use by historians are explored: the recognition and transcription of handwritten and early-printed historical texts, and the integration of large language models, chatbots, and generative neural networks into research practices. The article highlights the methodological and ethical challenges that arise when testing generative AI in historical research. A brief overview of relevant research is provided, covering areas such as the virtual reconstruction of lost (fully or partially) cultural heritage sites and the attribution of historical texts

About the authors

L. I. Borodkin

Lomonosov Moscow State University

Author for correspondence.
Email: lborodkin@mail.ru

doctor of Historical Sciences, corresponding Member of the Russian Academy of Sciences, head of the Department of Historical Informatics at the Faculty of History

Russian Federation, Moscow

References

  1. Borodkin, L.I. Artificial Intelligence Methods: New Horizons of Historical Knowledge. Information Bulletin of the Commission on the Application of Mathematical Methods and Computers in Historical Research at the History Department of the Russian Academy of Sciences. 1992, No. 5, pp. 4–8.
  2. Borodkin, L.I. Data Science and Artificial Intelligence Technologies: Potential and Limi-tations in Historians’ Research. Proceedings of the Department of Historical and Philolog-ical Sciences. Yearbook. Vol. 11. Ed. V.A. Tishkov. RAS Publ. Moscow: 2022, pp. 145–168.
  3. Borodkin, L.I. Historian in the World of Neural Networks: The Second Wave of Artificial Intelligence Technologies Application. Historical Informatics. 2025, No. 1, pp. 83–94.
  4. Bazarova T.A., Proskuryakova M.E. Autographs of Peter the Great: Reading with Artifi-cial Intelligence Technologies and Creating an Electronic Archive // Historical Informatics. 2022. No. 4. pp. 179-190.
  5. Assael Y., Sommerschield T., Shillingford B. et al. Restoring and attributing ancient texts using deep neural networks // Nature. 2022. 603, 280–283.
  6. Yumasheva Yu. Yu. On the Application of Artificial Intelligence in Historical Research (No. 1, 2025) Historical Informatics. 2025. No. 1 (51). Pp. 95-121.
  7. Yumasheva Yu. Yu. Historical Science, Archives, Libraries, Museums, and Artificial In-telligence: What Does the Future Hold? // Document. Archive, History. Modernity. 2021. Pp. 247-279.
  8. Yumasheva Yu. Yu. Historical Science, Archives, Libraries, Museums, and Artificial In-telligence: A Year Later // Document. Archive, History. Modernity. 2022. Pp. 217-241.
  9. Anthropological Forum, 2024, no. 60, p. 12.
  10. Meadows, R. Darrell, Sternfeld, Joshua. Artificial Intelligence and the Practice of History: A Forum // The American Historical Review. 2023. Vol. 128. Issue 3.
  11. Westenberger, P. and Farmaki, D. (2025) Artificial intelligence for cultural heritage re-search: the challenges in UK copyright law and policy. Eur. J. Cult. Manag. Policy 15:14009.
  12. Malandina, T.V. Using artificial neural networks in solving problems of virtual recon-struction of historical estate interiors // Historical Journal: Scientific Research. 2025. no. 3, pp. 45-60.
  13. Razgulin E.R., Zherebyatyev D.I. Virtual reconstruction of the temple complex of St. Nicholas the Wonderworker on Myasnitskaya Street in Moscow at the end of the 19th century // Historical informatics. 2025. No. 2. pp. 90-126.
  14. Latonov V.V., Latonova A.V. — Determination of the authorship of "Notes of the De-cembrist I.I. Gorbachevsky using machine learning methods // Historical informatics. 2025. No. 3. pp. 141-150.
  15. Kuznetsov A.V. Automatic information extraction from an ego-document: A comparative analysis of the effectiveness of large language models using the diary of K.A. Berezkin as an example // Historical Informatics. 2025. No. 3. pp. 99-127.
  16. Kotov A.S. Further training of a model based on the Transformer architecture for normali-zation of a corpus of medieval texts in German from the 14th-15th centuries from the Prussian Order // Historical Informatics. 2025. No. 3. pp. 128-140.
  17. Wiener N. The Creator and the Robot. Moscow, 1966.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2026 Borodkin L.I.

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).