Optimizing Recruitment with Machine Learning: A Novel Intelligent Agent Framework for HRM

Abstract

Purpose: The study proposes a framework for integrating intelligent agents (IA) into human resource management (HRM) to improve recruitment, screening, training, and decision-making. It utilizes machine learning and pattern recognition to enhance candidate search accuracy and efficiency, addressing the limitations of traditional Boolean methods.
Study design/methodology/approach: The study develops an Intelligent Agent AI (IAI) system for recruitment, using reinforcement learning and Naïve Bayes to optimize decision-making. It compares the IAI system to traditional methods like RecruitEm and Merlin, evaluating performance in accuracy, time efficiency, and resource management.
Sample and data: The study customizes the IAI system, integrates historical candidate data, and conducts pilot testing in real-world HR settings. Job seeker data is used to train and test the system’s performance.
Results: The IAI system significantly outperforms traditional methods in accuracy, time efficiency, and resource management. It also addresses issues like algorithmic bias and Boolean search limitations, improving candidate-job alignment and recruitment efficiency.
Originality/value: The study provides a novel AI-powered solution to HR workflows, improving scalability, efficiency, and adaptability. It helps overcome traditional recruitment bias and enhances decision-making through data-driven insights.
Research limitations/implications: Challenges include scalability, data bias, and integration issues. Future research should focus on addressing these limitations, refining AI processes, and optimizing performance in diverse HR contexts.