AI and HR: Transforming Recruitment Through Machine Learning and Data Analytics

Abstract

Human resource (HR) departments increasingly face the complex task of identifying candidates who best align with organisational needs while reducing recruitment costs and time-to-hire. Traditional manual sourcing of applicants across multiple platforms—such as job portals and social media—has become inefficient in the era of data abundance. This study explores how artificial intelligence (AI) technologies, particularly machine learning (ML) and data analytics, are transforming recruitment and selection processes by enabling more objective, efficient, and data-driven decision-making. Methodology. A systematic literature review (SLR) was conducted to map the intellectual structure of AI-driven recruitment research. From an initial pool of 1,444 publications indexed in Scopus, 155 met the inclusion criteria after rigorous screening. Latent Dirichlet Allocation (LDA) topic modelling and sentiment analysis were applied to the selected abstracts to extract dominant research themes and track the evolution of academic perspectives over time. Findings. The analysis identified twenty themes, consolidated into five overarching research domains: (1) Skill-Based Assessment, (2) AI Algorithms and Techniques, (3) Candidate Sourcing, (4) Industry-Specific Employment, and (5) Employee Lifecycle Challenges. Temporal sentiment analysis revealed a notable positive trajectory in researchers’ perceptions and discourse surrounding AI-enabled recruitment between 2014 and 2024, signalling growing confidence in its strategic potential. Value. This study extends the understanding of AI adoption in HRM by combining topic modelling and sentiment analysis to reveal emerging trends, research gaps, and practitioner implications. It contributes to both academia and practice by illuminating how ML and data analytics can be strategically leveraged to enhance fairness, efficiency, and predictive accuracy in recruitment