Authors : Mrunalini Makhwana; Nilakshi Kale

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

Google Scholar : https://tinyurl.com/yc443f2w

Scribd : https://tinyurl.com/bdhecepm

DOI : https://doi.org/10.38124/ijisrt/26apr1071

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

Abstract : The rapid increase in job applications has made traditional resume screening inefficient and time-consuming. Artificial Intelligence (AI) offers innovative solutions to automate recruitment processes and enhance hiring efficiency. This research presents an AI-based resume screening system that automates candidate evaluation using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The proposed system extracts relevant information such as skills, education, and experience from resumes and compares them with job descriptions to generate similarity scores. This enables recruiters to rank candidates effectively and make data-driven hiring decisions. The results indicate that automated screening significantly reduces recruitment time and improves selection accuracy [1, 6]. Despite its advantages, challenges such as algorithmic bias and lack of explainability remain areas for further improvement [8].

Keywords : Artificial Intelligence, Resume Screening, Natural Language Processing, Machine Learning, Candidate Ranking, Recruitment Automation, Skill Matching.

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