AI in human resource management

Abstract

Rapid adoption of Artificial Intelligence (AI) approaches, especially Large Language Models (LLM), in Human Resource Management (HRM) is transforming core functions that include recruitment and selection, training and development , and performance evaluation, among others. Although prior work offers conceptual insights, empirical evaluations and a methodological debate on AI in HRM, there is no unified synthesis of LLM-specific contributions. In this paper, we present a systematic review of the literature on LLM-enabled HRM, integrating both conceptual frameworks and technical findings. Specifically, we classify the retrieved literature along two intersecting axes: (a) its technical footprint (namely discriminative versus generative LLMs), matched to concrete HRM tasks such as résumé triage and sentiment-based performance reviews and (b) its theoretical lens through five streams that frame LLM adoption through conceptual modeling, empirical theory building, ethics and governance analysis, prompt design research and instructional/pedagogical studies. We also provide a classification of prevalent limitations within the retrieved literature along with several open issues that demand deeper, context-rich inquiry. Based on the findings, we provide a set of governance checklists for practitioners, researchers and regulators. To guide future research, we offer a four-lane road map plus a six-point research agenda. Converting LLM promise into trustworthy, equitable HRM practice will require co-evolving robustness metrics, layered human oversight and harmonized audit standards.