As archives continue to digitize vast quantities of historical material, institutions face a persistent challenge: how to efficiently and effectively process massive digital collections, particularly those composed of handwritten, unstructured, or inconsistent documents. At the same time, historians are increasingly being asked to grapple with large-scale digital projects—collections that promise new insights but often require technical skills in data wrangling, scripting, or computational analysis. Traditional transcription and data extraction methods remain resource-intensive, limiting the accessibility and usability of digital collections for scholars without technical training. This panel explores how AI-assisted technologies, particularly large language models (LLMs), are transforming this landscape.

Sponsored by the AHA Digital History Working Group and led by the team behind On the Books: AI-assisted Collections, this session brings together scholars, librarians, and technologists to demonstrate how AI can support the creation and use of “collections as data” in the humanities. Panelists will offer brief examples from current projects applying AI to tasks such as tagging historical photographs, structuring legal and handwritten texts, and identifying patterns in discriminatory legislation. Each case reveals how AI can enhance discoverability, accelerate processing, and support new forms of inquiry while centering ethical considerations and domain expertise.

The session will also invite active participation from the audience. What kinds of materials are hardest to work with? What support do scholars need to structure or analyze collections without deep technical knowledge? What would it take to make AI tools feel usable, trustworthy, and aligned with humanistic goals?

This panel marks the beginning of a collaborative design process to build open workflows, documentation, and infrastructure that make AI methods accessible to historians. Attendees will gain insight into emerging tools and strategies—and have the chance to shape the future of AI-supported research in archives and special collections.