This article presents a systematic review and descriptive analysis of the use of artificial intelligence (AI) in recruitment and personnel selection. The study adopts a cross-sectional qualitative design with a deductive approach, based on a Scopus search covering publications from 2020 to February 2025. The descriptive analysis reveals the emergence of a new research field, showing notable growth in 2024 and a landscape dominated by output from India and the United States. The main subject areas are business, management, accounting, and computer science, reflecting the interdisciplinary nature of the topic. Scientific production remains nascent and dispersed, with no prominent institutions or authors. AI applications, particularly machine learning and natural language processing, are increasingly integrated into human resource management, automating repetitive tasks, optimizing decision-making, and enhancing the candidate experience. Within personnel selection, AI operates in several domains, including candidate pre-selection, interview analysis, soft skills assessment, résumé classification, prediction of success and job suitability, automation of recruitment and communication tasks, and personalized profile recommendations. The spectrum of AI includes supervised and unsupervised machine learning, classification algorithms, neural networks, predictive and recommendation models, Bayesian methods, generative chatbots, facial recognition, and emotion analysis. AI approaches encompass analytical and predictive methods for performance evaluation and forecasting job suitability; operational methods to automate processes; and strategic methods to align talent with organizational goals, improve diversity, and personalize the candidate experience. The integration of AI in recruitment and personnel selection enhances efficiency, objectivity, and agility, offering a competitive advantage while also raising ethical and bias-related challenges.
González-Morales et al. (Thu,) studied this question.