I am writing to announce the CFP for a special issue of the Journal of Digital History focused on how AI reshapes historical research. The CFP is below and submissions should be submitted via the JDH website no later than May 31, 2025: https://
With the emergence of large language models predicated upon transformer architecture (2017) and image generation systems, generative artificial intelligence has demonstrated considerable potential for diverse research applications across the humanities and social sciences, as well as strong ethical challenges.
Following the introduction of ChatGPT (2022), scholarly publications dealing with the human past, including history and memory studies, have undertaken examinations of generative AI’s applications within these disciplines ( Kansteiner 2022 ). The American Historical Review dedicated a forum ( Meadows, Sternfeld 2023 ) which highlights both the achievements of AI in history (particularly its ability to utilize large collections of primary sources) and the risks that this usage poses to historical research, primarily the reproduction of certain biases, notably but not exclusively those of gender and race. Thus, as the introduction to this forum reminds us: ‘To ponder the effects of artificial intelligence on the field of history quite often requires interrogating fundamental concepts such as truth, evidence, and authenticity.’ In a similar vein, Memory Studies Review dedicated a special issue to investigating the implications of generative platforms for collective memory formation and evolution ( Gensburger, Clavert 2024 ). Holocaust studies scholars are investigating which narrative chatbots based on AI are putting forward ( Makhortykh 2023 ). Significant discourse has emerged regarding AI implementation in archival institutions confronting exponentially increasing document volumes ( Jaillant, Caputo 2022 ). Photographic archives, presenting distinct methodological considerations, have garnered particular scholarly attention ( Dentler, Jaillant, Foliard, et al. 2024 ; Arnold, Tilton 2023 ). Digital historians are currently trying to figure out what AI ‘knows’ about the past ( Hutchinson 2022 ). Furthermore, generative AI holds the potential to fundamentally transform routine historical methodologies ( Clavert, Muller 2024 ), independent of major digital history initiatives. This proliferation of AI within historical and other past-related scholarship portends substantial modifications to methodology.
Nevertheless, this ostensible paradigm shift in historical investigation builds upon decades of computational advancement, considering the foundational work of early computer science pioneers ( Turing 1950 ; Turing 1937 ; von Neumann 1945 ) and even earlier, if we include the contributions of Charles Babbage and Ada Lovelace. While historians must examine the disciplinary implications of increasingly prevalent AI-enabled software, it is imperative to contextualize this recent technological integration within a broader historical framework, our primary objective of this Journal of Digital History call for papers.
Abstract submissions may address any implementation of AI in studies of the human past while maintaining historical and hermeneutical perspectives to appropriately situate contemporary generative AI developments within their historical context. Additionally, submissions should examine these practices within the broader evolution of computational methods (including antecedent mechanographic systems) in humanities and social sciences research since the post-war period (for instance: François Furet, Adeline Daumard 1959 ). Submissions must adhere to the journal’s established multilayered article format (narrative/hermeneutic/code and data). Prospective contributors are strongly advised to review the Author Guidelines prior to submission.
The submission process comprises two distinct phases: initial abstract submission via the journal’s digital platform, followed by complete manuscript submission. Issue coordinators will subsequently contact authors of selected abstracts to discuss manuscript feasibility and requisite writing environment parameters.