Digital Doping for Historians: Can History, Memory, and Historical Theory Be Rendered Artificially Intelligent?

ABSTRACT Artificial intelligence is making history, literally. Machine learning tools are playing a key role in crafting images and stories about the past in popular culture. AI has probably also already invaded the history classroom. Large language models such as GPT‐3 are able to generate compelling, non‐plagiarized texts in response to simple natural language inputs, thus providing students with an opportunity to produce high‐quality written assignments with minimum effort. In a similar vein, tools like GPT‐3 are likely to revolutionize historical studies, enabling historians and other professionals who deal in texts to rely on AI‐generated intermediate work products, such as accurate translations, summaries, and chronologies. But present‐day large language models fail at key tasks that historians hold in high regard. They are structurally incapable of telling the truth and tracking pieces of information through layers of texts. What's more, they lack ethical self‐reflexivity. Therefore, for the time being, the writing of academic history will require human agency. But for historical theorists, large language models might offer an opportunity to test basic hypotheses about the nature of historical writing. Historical theorists can, for instance, have customized large language models write a series of descriptive, narrative, and assertive histories about the same events, thereby enabling them to explore the precise relation between description, narration, and argumentation in historical writing. In short, with specifically designed large language models, historical theorists can run the kinds of large‐scale writing experiments that they could never put into practice with real historians.