Reimagining library FAQs with Generative AI: A case study in metadata optimization and user-centered design

This case study explores how the Goldey-Beacom College Library used Generative AI, specifically ChatGPT, to refine and restructure metadata in its FAQ system in SpringShare’s LibAnswers, which had become fragmented due to overly specific and siloed topic tags. The project transformed two hundred and 87 FAQ entries into a more cohesive and discoverable knowledge base through a systematic audit and iterative AI-human collaboration. The team significantly improved user navigation and cross-topic discoverability by consolidating redundant topics and applying broader thematic categories such as Library Resources & Access , Research & Academic Tools , and Digital & Technology Tools . The consistent use of high-level topic tags, “GBC Library” or “Newspapers,” further unified the system and enhanced institutional visibility. The outcomes include reduced metadata silos, increased FAQ engagement, and a more user-centric experience. Lessons learned highlight the importance of balancing AI efficiency with human oversight, maintaining metadata consistency, and iterating based on user feedback. This paper offers practical guidance for libraries seeking to adopt AI to improve technical services and enhance user access to information.