A Comprehensive AI-Powered System for Screening Resumes and Ranking Job Applicants for Optimal Hiring Decisions

Abstract

The rapid growth of online recruitment platforms has significantly increased the number of resumes received for individual job vacancies, making conventional manual screening increasingly time-consuming and difficult to scale. Traditional Applicant Tracking Systems primarily rely on keyword matching and predefined rules, which may overlook candidates whose qualifications are expressed using different terminology or contextual descriptions. Recent advances in Natural Language Processing (NLP), transformer-based language models, semantic similarity, Explainable Artificial Intelligence (XAI), and Large Language Models (LLMs) provide opportunities for developing more intelligent recruitment systems [1]–[5]. This review presents a comprehensive AI-powered framework for resume screening and applicant ranking that integrates document processing, NLP-based candidate representation, semantic candidate–job matching, multifactor scoring, explainable ranking, fairness monitoring, adaptive learning, and human-in-the-loop decision support. The proposed framework transforms unstructured resumes and job descriptions into structured and contextual representations, identifies relevant skills, qualifications, experience, projects, and certifications, and computes candidate–job alignment using semantic similarity. A multi-factor ranking mechanism then combines semantic relevance with job-specific requirements to generate an ordered list of applicants. An explainability layer provides evidence for ranking decisions, while fairness monitoring evaluates potential bias in automated recommendations. The review further discusses evaluation metrics, deployment challenges, data limitations, model drift, and future research directions. The resulting architecture provides a unified foundation for scalable, transparent, fair, and recruiter-assisted intelligent hiring.