HUMAN RESOURCES AND ARTIFICIAL INTELLIGENCE: A TEXTUAL ANALYSIS OF EMERGING RESEARCH DIRECTIONS
DOI:
https://doi.org/10.70301/SBS.MONO.2026.1.1Keywords:
artificial intelligence; machine learning; human resources; textual analysis.Abstract
This study maps the dominant themes in recent research at the intersection of human resources, AI, and machine learning, based on 28 journal articles from applied science journals published over the past year. Using IRaMuTeQ software, it applied Reinert hierarchical classification and Correspondence Factor Analysis, producing visualizations and matrices that identified four key thematic clusters: AI Adoption in Education, Healthcare & Organizations (28.8%), ML Model Performance & Evaluation (28.8%), Automated Image Classification & Computer Vision (18.9%), and Geospatial AI & System Monitoring (23.4%). The factorial plan explained 53.5% of the variance, indicating strong independence among the clusters. Findings show AI/ML research expanding into healthcare, education, environmental monitoring, and infrastructure, urging human resource professionals and organizational leaders to consider cross-disciplinary AI applications. Limitations include the focus on 28 articles from a single year and applied science journals, which may omit broader perspectives. The analysis reflects lexical patterns, not deeper semantics. This overview offers a data-driven map of current AI/ML and HR research intersections, contributing methodologically to bibliometric and textual studies in management and sciences.
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