Charting the evolution and strategic integration of artificial intelligence in human resource acquisition: A bibliometric review (2019-2025)

Objectives This study systematically reviews the literature on Artificial Intelligence (AI) in Recruitment & Selection (R&S) and maps the intellectual, conceptual, and social structures of the domain. In particular, three objectives were addressed: (1) to explore trends in publication volume and collaboration patterns in AI-R&S research. (2) Cluster the literature and classify research streams. (3) Identify gaps and suggest future research directions. Design In this mixed-method study, consisting of a bibliometric analysis and qualitative thematic content analysis, data were collected from the Scopus database (2019-2025), and 48 documents (20 articles, 4 reviews, 11 conference papers, 10 book chapters and 3 books) were selected following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Results The research field is growing at an average rate of 63.35% per year. Documents have on average 3.19 co-authors, and 25% involve international co-authorship. India is the most productive country (34 affiliation mentions, present in 17 of the 48 documents). The conceptual space is broadly delineated by two streams of work: ethical and technical concerns (algorithmic bias, black box problem), and practical implementation and organizational impact. The field remains fragmented into isolated research clusters, marking it as pre-paradigmatic. Conclusion AI for R&S is still immature and requires practitioners to move from experimentation to ethical and planned use in decision-making. Future research should investigate human-AI collaboration, culture effects, and governance mechanisms.