I have believed since 2022, and still believe, that generative AI models have considerable potential as tools for augmenting traditional humanistic research. Two use cases that deserve special attention: 1) classifying, sorting, and otherwise extracting metadata from large corpora of public domain historical sources (one example, appropriate to the season: you can provide an LLM with a book-length religious text in 17th century Latin, and ask it to output a classification of every demon and djinn referenced therein). 2) automated transcription of manuscript documents (but only if this is done by historians trained in paleography and able to spot check the results).
Importantly, these two use cases of LLMs are not just enormously helpful for historians. They also suggest new pathways for teaching a new set of broadly useful skills in humanities classes. Learning how to apply novel tools to sorting and processing data — while also, crucially, being able to critically analyze the limits of that data, such as the biases built into it and the negative space around it — has applications far beyond writing a term paper. Historians and other humanists thus have a rare opportunity to contribute in a meaningful way to the development and teaching of a new set of skills directly relevant to their discipline but also applicable to the world beyond it.