Optimizing Metadata Workflows with AI: Insights from UCF Libraries

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

Rights Statement

All Rights Reserved

Audience

Librarians; Faculty

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May 30th, 10:15 AM May 30th, 10:40 AM

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.