Smart Chatbot for Human Resources

This study presents a comprehensive literature review on the integration of Artificial Intelligence (AI)-powered chatbots in Human Resource Management (HRM). The paper examines the evolution of HR chatbots from rule-based systems to advanced transformer-based architectures leveraging Natural Language Processing (NLP) and Large Language Models (LLMs). The research highlights key applications including recruitment automation, employee self-service, onboarding support, and performance management. It further evaluates technical architectures such as NLU pipelines, LLM + Retrieval-Augmented Generation (RAG) systems, and enterprise integrations with HRIS and Applicant Tracking Systems. The study also critically analyzes challenges associated with HR chatbots, including algorithmic bias, lack of interpretability, data privacy risks, and over-reliance on automation in sensitive decision-making processes. Regulatory perspectives such as the EU AI Act and responsible AI frameworks are also discussed. Findings suggest that while HR chatbots significantly improve operational efficiency and user experience, their optimal implementation requires a hybrid human-AI approach, strong governance, and continuous monitoring. This work contributes to the field of intelligent enterprise systems and provides insights for future research in scalable, fair, and explainable HR chatbot solutions.