Authors : Shifa Shah; Neha Kumari; Saloni Verma

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3xcse5ah

Scribd : https://tinyurl.com/2t3zdd57

DOI : https://doi.org/10.38124/ijisrt/26apr648

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Recruitmentis a criticalfunction inhuman resourcemanagement, yet traditional methods are frequently hampered by inefficiency, subjectivity, and scalability limitations. This paper investigates the role of Artificial Intelligence (AI) in automating and enhancing resume screening processes. By leveraging Natural Language Processing (NLP), machine learning classification algorithms, and semantic similarity models, AI-driven screening systems enable organizations to evaluate large candidate pools objectively, rapidly, and at reduced cost. We examine the technical architecture of such systems, analyze their real-world applications across corporate and academic hiring, enumerate their advantages, critically assess their limitations including algorithmic bias and transparency concerns, and outline future research directions encompassing explainable AI, bias mitigation, and multimodal candidate evaluation. Our findings indicate that while AI screening offers transformative potential, responsible deployment requires robust governance frameworks and continuous auditing.

Keywords : Artificial Intelligence, Resume Screening, Natural Language Processing, Machine Learning, Recruitment Automation, Algorithmic Bias, Explainable AI.

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