Knowledge Dynamics in AI-Driven Natural Science Research: A Bibliometric Review Using VOSviewer

Babawande Emmanuel Olawale

doi:10.24014/jnsi.v9i1.39385

Knowledge Dynamics in AI-Driven Natural Science Research: A Bibliometric Review Using VOSviewer

Babawande Emmanuel Olawale

Abstract

This study examines how artificial intelligence is reshaping knowledge production within the natural sciences. It maps research growth, collaborative structures, and thematic developments to clarify how AI-driven methods are being adopted across global scientific communities. A bibliometric review was conducted using data from Scopus-indexed publications from 2020 to 2025. The quantitative bibliometric approach was employed to examine the structural, thematic, and collaborative aspects of AI-driven research within the natural sciences, combining performance analysis to evaluate publication output, citation impact, and productivity trends with science mapping using VOSviewer to visualise co-authorship networks, keyword co-occurrences, and co-citation relationships. For this review, the Boolean search query that was employed includes "artificial intelligence" OR "machine learning" OR "deep learning" AND "natural science.” To ensure thorough coverage, this search was conducted across the database's title, abstract, and keywords fields. The dataset comprised 667 documents after screening and duplicate removal. VOSviewer and complementary analytical techniques were employed to assess publication trends, leading authors and institutions, country-level collaboration, co-citation structures, and thematic clusters derived from Author and index keywords. The results show rapid and sustained growth, with a compound annual rate of 31.55 per cent. China and the United States lead in productivity and citation impact. Several emerging research economies are becoming more visible. The analysis reveals strong international collaboration and distinct thematic areas, including AI-assisted diagnostics, environmental modelling, bioinformatics, and predictive analytics. Co-citation networks reveal a robust intellectual foundation, grounded in widely referenced methodological and field-specific studies. This study provides a recent bibliometric assessment of AI-driven research in the natural sciences. It provides a comprehensive overview of global productivity, research influence, and thematic development. This approach facilitates a deeper understanding of how AI is transforming the field of scientific inquiry.

Keywords: artificial intelligence, natural science, bibliometric analysis, research collaboration

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DOI: http://dx.doi.org/10.24014/jnsi.v9i1.39385