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AI Powered Resume Screening System
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
DOI: https://doi.org/10.64388/IREV9I6-1712632
Abstract
Resume screening is the most time-intensive stage of recruitment, especially in large-scale hiring environments. Traditional manual evaluation often results in delays, inconsistency, and unintentional bias. With advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), automated systems now offer reliable alternatives to manual screening. This paper presents an overview of an AI-powered resume screening system capable of parsing resumes, extracting key candidate information, comparing profiles with job descriptions, and generating ATS-based relevance scores. The system improves fairness, accuracy, and efficiency while reducing recruiter workload. The paper discusses system architecture, algorithms, advantages, limitations, and future scope of AI-driven recruitment.
How to cite this paper
@article{1712632,
author = {Abdul Rehaman, Abhishek M P, Gangadhar K, Mahendra C C},
title = {AI Powered Resume Screening System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {277-280},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712632.pdf},
abstract = {Resume screening is the most time-intensive stage of recruitment, especially in large-scale hiring environments. Traditional manual evaluation often results in delays, inconsistency, and unintentional bias. With advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), automated systems now offer reliable alternatives to manual screening. This paper presents an overview of an AI-powered resume screening system capable of parsing resumes, extracting key candidate information, comparing profiles with job descriptions, and generating ATS-based relevance scores. The system improves fairness, accuracy, and efficiency while reducing recruiter workload. The paper discusses system architecture, algorithms, advantages, limitations, and future scope of AI-driven recruitment.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712632}
}