Document Type

Poster

Conference Name

ALA Annual Conference 2026

Publication Date

6-28-2026

Keywords

AI, artificial intelligence, large language models, cataloging, metadata

Abstract

This poster will disentangle AI hype from AI reality by providing a systematic review and meta-analysis of the current empirical research on AI applications for cataloging. Since the introduction of ChatGPT in November 2022, AI has been proposed as a tool for increasing efficiency and freeing up librarians' time across all areas of librarianship, especially in the area of cataloging and metadata. There is often a gap, however, between current AI realities and the requirements of highly structured library metadata. Surveys show that many catalogers are already using Large Language Model (LLM) tools, with human intervention, to assist in tasks such as translation, subject analysis, and summary creation. As many institutions rush to embrace—or at least adapt to—the presence of AI in the academic landscape, some metadata professionals may feel pressure to adopt AI tools as a means of increasing efficiency. In this context, there is a timely need for reliable data on the efficacy of LLMs for metadata production. Drawing on a range of evidence-based studies, including a recent study on AI-generated summaries conducted by the presenters, this poster session will examine the available empirical data on the efficacy of AI for cataloging tasks such as subject heading assignment, classification, MARC encoding, and summarization, and will provide recommendations that catalogers can use to make data-driven decisions about the advantages and disadvantages of LLM tools for their own work.

Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 International License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 International License.

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