Mapping the Landscape of Artificial Intelligence Research: A Bibliometric Approach

Abstract

This research study employs a comprehensive bibliometric approach, enhanced by the utilization of VOS viewer, to map the expansive landscape of artificial intelligence (AI) research. Through the meticulous collection, pre-processing, and analysis of a diverse dataset, this study uncovers the multifaceted dimensions that define AI research. The analysis encompasses publication trends, authorship dynamics, citation patterns, and emergent research themes. The integration of VOS viewer’s visualization capabilities enriches the exploration by offering intuitive representations of collaboration networks, citation maps, and thematic clusters. The results highlight the growth trajectory of AI research, the collaborative networks among researchers and institutions, the influence of seminal works, and the emergence of thematic trends. Moreover, the study contextualizes the findings, discussing their implications for interdisciplinary collaboration, ethical considerations, and the societal impact of AI research. Ultimately, this research contributes to a comprehensive understanding of AI research dynamics, guiding future exploration, collaboration, and innovation within this rapidly evolving domain.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.