Optimizing Metadata Workflows with AI: Insights from UCF Libraries
Title
Optimizing Metadata Workflows with AI: Insights from UCF Libraries
Subject
Description
The University of Central Florida Libraries are leveraging Artificial Intelligence (AI), specifically the OpenAI API, to transform their metadata workflows. By automating the assignment of Faceted Application of Subject Terminology (FAST) headings and keywords to their digital and traditional collections, they enhance discoverability and user experience. This approach involves exploring various term reconciliation methods, such as utilizing the OCLC service or building a custom FAST vector database. Through rigorous testing and evaluation, including comparisons with Alma's AI Metadata Assistant, the Libraries are refining their AI-driven metadata practices, paving the way for an enriched library experience for their users.
Creator
Deng, Sai (Sai Sophie)
Piascik, Jeanne
Zhang, Ying
Publisher
University of Central Florida Libraries
Date
2025-05-30
Format
application/pdf
application/vnd.openxmlformats-officedocument.presentationml.presentation
application/vnd.openxmlformats-officedocument.presentationml.presentation
Language
eng
Type
Text; Presentation
Alternative Title
Optimizing Metadata Workflows with Artificial Intelligence (AI): Insights from University of Central Florida Libraries
Position: 1048 (72 views)
Files
Collection
Citation
Deng, Sai (Sai Sophie), Piascik, Jeanne, and Zhang, Ying, “Optimizing Metadata Workflows with AI: Insights from UCF Libraries,” CALASYS - CALA Academic Resources & Repository System, accessed April 28, 2026, https://www.ir.cala-web.org/items/show/1533.