JSTOR Seeklight: Community-Driven AI for Archives

About This Project

Summary

JSTOR Seeklight is a generative AI-powered tool co-designed with archivists and librarians to unlock hidden collections at scale. By accelerating collection processing while ensuring expert oversight, it fast-tracks materials for discovery and impact. Once processed, collections can be published on JSTOR for global awareness and preserved long-term— transforming dormant knowledge into engaging resources for teaching, research, and cultural storytelling.

Goals & Objectives

Partner with the community to create a human-supervised AI product for digital collections that accelerates processing, reclaims staff time, lowers costs, and makes collections visible faster—broadening access, raising awareness, and deepening engagement in education, art, and culture.

Results & Impact

Available in July 2025, JSTOR Seeklight now enables 26 institutions to cut processing from weeks to hours, reclaim staff time, and unlock hidden collections. Case studies show global reach via JSTOR — and the roadmap will expand ethical access and engagement even further.