2026-10-01T15:57:00Z https://dcpapers.dublincore.org/oai

oai:dcpapers.dublincore.org:952672956 2026-09-10 dcmi-2026 openaire

Are AI Models Getting Better at Cataloging? - Evidence from a Two-Point Comparative Study Han, Myung-Ja (MJ) K Heng, Greta Lampron, Patricia Kudeki, Deren This study examines how the cataloging performance of four AI models, ChatGPT, Copilot, DeepSeek, and Gemini, evolved over eight months when tasked with extracting bibliographic information from scanned images across seven items of varying publication types and subject domains. Using four prompt variations and a consistent methodology established in an earlier round of testing, the second round revealed meaningful overall improvement in the accuracy and completeness of cataloging records, with models more consistently acknowledging missing information, providing inline justification for decisions, and exhibiting behaviors aligned with Explainable AI (XAI) and Retrieval-Augmented Generation (RAG) principles. Persistent challenges remained in controlled subject headings and URI accuracy, and a new concern emerged around balancing prompt over- and under-specification. These findings support a human-in-the-loop approach to AI-assisted cataloging and highlight the value of continued longitudinal monitoring. Dublin Core Metadata Initiative 2026-09-10 info:eu-repo/semantics/conferenceObject Text application/pdf text/html https://doi.org/10.23106/dcmi.952672956 https://dcpapers.dublincore.org/article/952672956 Dublin Core Metadata Initiative Conference Proceedings eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/