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A Methodical Approach to Evaluating AI-Generated Metadata: Outcomes of a First-Year Charter

Home / Topics / Artificial Intelligence / A Methodical Approach to Evaluating AI-Generated Metadata: Outcomes of a First-Year Charter

March 25, 2026

Jeremy Thompson
Digital Processing Archivist
University of Texas

Mirko Hanke
Head of Preservation and Digital Stewardship
University of Texas 

The University of Texas Libraries (UTL) entered into a charter program with ITHAKA in the fall of 2025 with the objective of testing Seeklight, JSTOR’s artificial intelligence (AI)-based metadata generation tool. The goal for the first year of the charter was to test the quality of Seeklight’s metadata output and its compatibility with multilingual and complex records. UTL evaluated the quality and changes over time over several months of testing, using born-digital and digitized records sourced from UTL’s Benson Latin American Collection and Alexander Architectural Archive. The rubric used during this testing period is a four-point, subjective rating scale, which was developed by UTL’s AI Metadata Creation Working Group during earlier tests with out-of-the-box LLMs. This presentation will highlight our initial impressions of Seeklight’s metadata output on a field-by-field basis. We will explore UTL’s continued testing of Seeklight and its current ability to provide the subject knowledge and enhanced discoverability needs for archival records.

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Filed Under: Artificial Intelligence, CNI Spring 2026 Project Briefing, Emerging Technologies, Metadata, Project Briefing Pages
Tagged With: cni2026spr, Project Briefings & Plenary Sessions

Last updated:  Wednesday, March 25th, 2026

 

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