Abstract
This study evaluates the effectiveness of consumer-level AI tools—ChatGPT Pro and Microsoft Copilot—for generating descriptive metadata in digital archival collections. Using a pilot project at the University at Buffalo's University Archives, researchers tested these tools on transcripts from the WBFO radio archives to create program descriptions of varying lengths. Findings demonstrate that both tools significantly reduced processing time for audio collections, with each exhibiting distinct advantages: ChatGPT Pro offered more adaptable outputs while Microsoft Copilot excelled in structured environments with superior privacy protections. The study explores prompt engineering strategies, examines limitations including AI hallucinations when identifying subject headings, and addresses privacy considerations in institutional contexts. This research provides a scalable methodology for implementing AI in archival workflows while maintaining necessary oversight and quality control, contributing to broader discussions on AI integration in digital archiving practices.
Recommended Citation
Dunbar, Hope and Axford, Ken
(2026)
"Leveraging Consumer-Level AI for Descriptive Metadata Creation in Archival Collections,"
Journal of Contemporary Archival Studies: Vol. 13, Article 4.
Available at:
https://elischolar.library.yale.edu/jcas/vol13/iss1/4
WBFO Reel-to-Reel Magnetic Media
DSC04855.jpg (4778 kB)
WBFO Program Guides to Recorded Audio