This timely report explores how metadata professionals—primarily from U.S. research universities—are integrating AI technologies into their workflows, cataloging practices, and metadata management.
The report is based on detailed survey responses from 47 metadata librarians, offering a unique window into the evolving role of artificial intelligence in academic libraries.
Key Findings from the Report Include:
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Growing Adoption of AI Tools:
Over 38% of respondents reported actively using AI tools in their cataloging or metadata work, with many citing tools like ChatGPT, MARCedit with AI plugins, and machine learning-based classification systems.
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Training & Skill Development:
51% of librarians indicated they had received some form of training or professional development related to AI, though only 19% felt “very confident” in applying AI tools effectively.
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Concerns About Accuracy and Ethics:
A significant 63% of respondents expressed concerns about the reliability and ethical implications of using AI in metadata creation, particularly regarding bias and transparency in automated subject classification.
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Impact on Workload and Efficiency:
44% of librarians noted that AI tools had helped reduce repetitive tasks, such as authority control and metadata enrichment, allowing more time for complex cataloging decisions.
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Institutional Support Varies Widely:
While 29% of respondents said their institutions actively encourage experimentation with AI, 36% reported little to no institutional guidance or support for AI integration.
This report is an essential resource for library administrators, metadata professionals, and academic technologists seeking to understand the current landscape and future potential of AI in library cataloging. The study gives highly specific data about the use of particular AI products and tracks current and expected future usage.