Alternative Title
Optimizing Metadata Workflows with Artificial Intelligence (AI): Insights from University of Central Florida Libraries
Contributor
University of Central Florida. Faculty Center for Teaching and Learning; University of Central Florida. Division of Digital Learning; Teaching and Learning with AI Conference (2025 : Orlando, Fla.)
Location
Gold Coast I/II
Start Date
30-5-2025 10:15 AM
End Date
30-5-2025 10:40 AM
Publisher
University of Central Florida Libraries
Keywords:
Metadata optimization; AI integration; Workflow automation; User experience enhancement; Term reconciliation
Subjects
Artificial intelligence--Library applications; Cataloging--Automation; Research libraries--Automation; Technical services (Libraries)--Automation; Academic libraries--Technological innovations
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.
Language
eng
Type
Presentation
Format
application/pdf
Rights Statement
All Rights Reserved
Audience
Librarians; Faculty
Recommended Citation
Deng, Sai; Piascik, Jeanne; and Zhang, Ying, "Optimizing Metadata Workflows with AI: Insights from UCF Libraries" (2025). Teaching and Learning with AI Conference Presentations. 38.
https://stars.library.ucf.edu/teachwithai/2025/friday/38
Optimizing Metadata Workflows with AI: Insights from UCF Libraries
Gold Coast I/II
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.
Accessibility Statement
This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.