TG2
TG2. Metadata and AI
Investigating the intersection of metadata practices and artificial intelligence, including global surveys, competency frameworks, and curriculum development.
Members
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Shuheng Wu — Queens College, The City University of New York, USA
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Magnus Pfeffer — Stuttgart Media University, Germany
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Yunhyong Kim — University of Glasgow, UK
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Sangeun Han — University of Toronto, Canada
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Julaine Clunis — Old Dominion University, USA
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Dunia Llanes Padrón — University of Zaragoza, Spain
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Shu-Jiun (Sophy) Chen — Academia Sinica, Taiwan
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Keping Wang — Shandong University of Technology, China
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Junzhi Jia — Renmin University of China
Scope & Objectives
Artificial intelligence is widely recognized as a transformative force poised to significantly impact metadata creation and management within libraries and information services. This transformation stems from AI's potential to automate time-consuming tasks like metadata extraction, tagging, classification, and quality improvement, leading to increased efficiency and accuracy in workflows. However, this shift is not without its complexities, as AI implementation brings significant challenges.
Initiatives
The Metadata and AI task group is engaged with the following initiatives:
1. Global Survey of the Impact of AI on Metadata Creation and Management
- AI and the Transformation of Metadata Research and Practices – Global and Regional Perspectives, co-edited by Ying-Hsang Liu, Marcia Lei Zeng and Alasdair MacDonald, to be published by Cambridge University Press & Assessment (under preparation).
2. Competency Framework Development
- Define the core AI-related competencies for metadata professionals.
3. Curriculum & Training Design
- Review current LIS syllabi to identify gaps in AI, data science, and ethics content.
4. Community Engagement & Resources
- Host webinars and present at conferences to share pilot results and gather practitioner feedback.
5. Ongoing Evaluation & Revision
- Set up metrics and review cycles to track AI tool performance.