AI and the Transformation of Metadata Research and Practices: Global and Regional Perspectives (DCMI)

Description

This presentation reports the major findings from a large-scale, multilingual global survey conducted by the DCMI Education Committee AI Task Group, offering a comprehensive analysis of AI adoption in metadata practices from diverse global and regional perspectives. The research is framed by the Elaboration Likelihood Model (ELM) to understand the cognitive underpinnings of professional attitudes. The results indicate a strong consensus toward a positive outlook on AI's role in metadata tasks. The analysis reveals that perceived benefits are the most significant factor shaping this optimism. In contrast, the most pressing challenge identified across regions is a widespread lack of professional AI skills training. This challenge, in turn, emerges as the primary motivator for professionals seeking skills development, with core competencies focusing on a foundational understanding of AI principles and the critical evaluation of AI-generated outputs. The analysis further reveals that professional background, such as a specialization in Information Organization, serves as a significant moderating variable in shaping these attitudes. These empirical insights form the core of the forthcoming monograph, 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.