Journal of Library Metadata

AI Implementation in Library Metadata

The emergence of artificial intelligence (AI) technologies is reshaping how libraries manage, create, and enhance metadata and technical services. From automated subject indexing and authority control to machine learning-driven cataloging tools, AI offers opportunities to improve efficiency, scalability, and discovery in library workflows. Yet, these developments also raise important questions regarding data quality, transparency, ethics, and the evolving role of metadata professionals.

This special issue of the Journal of Library Metadata aims to explore current practices, innovations, and critical perspectives on AI implementation in library metadata and technical services. We welcome research articles, case studies, and conceptual pieces that examine the promises, challenges, and real-world impacts of integrating AI into metadata work.

Topics:
Recommended topics include, but are not limited to:

• Use of AI tools (e.g., machine learning, NLP, LLMs) in metadata creation, enhancement, and remediation
• Integrating AI into integrated library systems (ILS) or library services platforms (LSPs)
• Ethical and professional considerations in AI-generated metadata
• Evaluating the accuracy and reliability of AI-generated subject metadata/keywords or descriptions
• Impact of AI tools on labor, staffing, and training in technical services
• Case studies on institutional implementations of AI in metadata workflows
• Linked Data and AI intersections in library metadata
• Data governance and quality control in AI-assisted metadata systems
• Collaborations between libraries, vendors, and researchers on AI initiatives