Artificial Intelligence (AI) and Historical Research

A special issue of Histories (ISSN 2409-9252). This special issue belongs to the section "Digital and Computational History".

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 3637

Special Issue Editors


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Guest Editor
1. Max Planck Institute for the History of Science, Boltzmannstr. 22, 14195 Berlin, Germany
2. The Cohn Institute for the History and Philosophy of Science and Ideas, Faculty of Humanities, Tel Aviv University, P.O. Box 39040, Ramat Aviv, Tel Aviv 6139001, Israel
Interests: history of knowledge; knowledge economy; history of science and technology; digital humanities; machine learning and artificial intelligence

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Guest Editor
Austrian Academy of Sciences, Institute for Habsburg and Balkan Studies, Vienna, Austria
Interests: history of knowledge and science; media and book history; digital humanities

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into various disciplines is reshaping traditional methodologies, and the field of history is no exception. Historians are increasingly utilizing AI not only as a supportive tool but also as an autonomous investigative partner, prompting a reevaluation of historical research and writing practices. This Special Issue seeks to explore the multifaceted implications of AI in history writing, including methodological transformations, ethical considerations, and the future trajectory of the discipline.

Key Questions for Exploration:

  • Role of AI in Historical Disciplines: How is AI currently being employed in historical research and writing? What benefits and challenges does it present? For instance, AI has been used to restore and attribute ancient texts, enhancing our understanding of historical documents.
  • Transformation of Historical Methodologies: In what ways might the integration of AI alter traditional historical methodologies? Could AI-driven analyses lead to new historical interpretations or narratives? The use of AI in analyzing large datasets can reveal patterns and connections previously unnoticed, potentially transforming historical scholarship.
  • Levels of AI Integration: To what extent can AI be integrated into historical research? Is it merely a supportive tool, or can it function as a co-researcher? AI’s role in writing history is evolving, with discussions on its potential to assist in drafting and editing historical narratives.
  • Data-Driven History: Will the incorporation of AI and big data analytics transform history into a fully data-driven science? What are the implications of such a shift? This evolution highlights the need for interdisciplinary collaboration between the humanities and computer science to balance quantitative and qualitative methods.
  • Ethical Considerations: What ethical guidelines should govern the use of AI in history writing? How can historians ensure transparency regarding AI contributions in their work? The ethical implications of AI in research are complex, necessitating new guidelines to address issues such as bias, authorship, and the integrity of historical narratives.
  • Development of AI for Historical Research: Should AI technologies be tailored specifically to meet the unique needs of historical research? What collaborations are necessary between historians and AI developers? Customizing AI tools for historical research could enhance their effectiveness, but requires interdisciplinary collaboration.

Prof. Dr. Matteo Valleriani
Dr. Doris Gruber
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Histories is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence (AI)
  • history writing
  • history of science and technology
  • digital humanities
  • computational history

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Published Papers (1 paper)

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Research

17 pages, 1150 KB  
Article
Minimal Computing and Weak AI for Historical Research: The Case of Early Modern Church Administration
by Christoph Sander
Histories 2025, 5(4), 59; https://doi.org/10.3390/histories5040059 - 28 Nov 2025
Viewed by 360
Abstract
This paper introduces an AI-assisted human-centered and minimalist software stack and data model to structure and store early modern serial sources related to early-modern Catholic Church administration. The Vatican Archive preserves vast quantities of documents recording its administrative history. To date, the sheer [...] Read more.
This paper introduces an AI-assisted human-centered and minimalist software stack and data model to structure and store early modern serial sources related to early-modern Catholic Church administration. The Vatican Archive preserves vast quantities of documents recording its administrative history. To date, the sheer volume and technical character of these Latin manuscripts have made systematic study appear nearly impossible. The multinational project GRACEFUL17 unfolds seventeenth-century Church governance on a large scale with the help of AI. It leverages simple but efficient NLP (NER, span categorizer, fuzzy searches) and classifier (gradient boost) techniques that run fast, reliably, and reproducibly to allow for multi-user offline work environments, as well as quick but controlled data modelling in a knowledge graph. By documenting this workflow, the paper enhances replicability and provides a rationale for specific design decisions beyond technical documentation. This paper advocates the use of “weak AI” on several grounds. Functionally, non-LLM pipelines offer stricter controllability and avoid many of the semantic biases introduced by large language models. They also require fewer training overheads and run locally with ease. Methodologically, the combination of simple AI models and symbolic reasoning underscores the indispensable role of human expertise: only experts can provide the ground truth necessary for models to reproduce and formalize complex semantic concepts and phenomena, rather than outsourcing this interpretive work to foundation models. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Historical Research)
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