Artificial Intelligence in Libraries

A Multifaceted Analysis of Integration, Impact and Collaboration Dynamic

Abstract

This study investigates the centrality and density of the ‘Artificial Intelligence’ cluster within interdisciplinary research networks, contrasting it with thematic clusters like ‘prediction’ and ‘library.’ The research aims to assess AI’s impact on education and services compared to other thematic clusters. To achieve this, we employed Python analysis tools on a dataset of 587 articles from the Web of Science Core Collection, examining centrality measures and collaboration dynamics. The methodology includes systematic data collection, centrality analysis, and advanced visualisation techniques. The findings indicate AI’s high centrality and moderate density, underscoring its pivotal role in driving interdisciplinary research. Comparative analysis reveals AI’s broader application potential and influence compared to other clusters. Additionally, insights into country collaborations and centrality measures within clusters illuminate network dynamics and key nodes. Visualisations, such as scatter and box plots, offer comprehensive insights into centrality distribution and relationships within the collaboration network. These results contribute significantly to understanding AI’s role in interdisciplinary research, informing strategic planning and resource allocation for future advancements. The broader implications of findings suggest potential practical applications and directions for future research in leveraging AI’s influence across various domains.