Information about the IMLS-funded Project ORAL-AI
ORAL-AI: High Efficacy Content Representation and User Engagement of Oral Digital Collections with Multimodal LLMs", Co-PI: Jiangping Chen, 2026–2029.
The University of North Texas, in collaboration with the University of Illinois Urbana-Champaign, seeks $490,397 for a 3-year Applied Research grant project that aims to expand meaningful public access to large-scale oral history collections by developing and evaluating a responsible, privacy-aware multimodal AI framework that supports cross-modal semantic retrieval, evidence-grounded question answering, and contextual narrative summarization.
By strengthening libraries’capacity to responsibly integrate emerging AI technologies into digital collections, the project advances NLGL Goal 2.1 (Promote broad public engagement) and Goal 3.2 (Promote access to museum and library collections). The project will pilot and evaluate the framework across three nationally significant collections—the Densho Digital Repository (Densho collection), the Library of Congress Civil Rights History Project (Civil Rights collection), and the Veterans History
Project (Veterans History collection)—representing diverse historical contexts, multimedia formats, and narrative complexity.
Building on the team’s prior empirical studies of oral history collections and large language model evaluation, the project will investigate three research questions:
RQ1: What are the capabilities, limitations, and risks of current multimodal large language models in oral digital collections?
RQ2: How can multimodal retrieval and generation systems be designed to mitigate identified limitations while preserving interpretive integrity and professional stewardship?
RQ3: How does AI-assisted multimodal access affect user engagement, comprehension, and professional practice compared to traditional discovery systems?
