Authors : Venkata Anitha Varikallu; Raheeman Shaik; Dr. P. Pardasaradhi

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/yc75fn6e

Scribd : https://tinyurl.com/2p7a6574

DOI : https://doi.org/10.38124/ijisrt/25apr2258

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Abstract : In the competitive landscape of recruitment, organizations face significant challenges in efficiently screening resumes to identify the most suitable candidates. Traditional resume screening methods are often labor-intensive and prone to human error, leading to biases and inefficiencies. This paper presents a Smart Application Tracking System (ATS) that leverages Generative Artificial Intelligence (Gen AI) and advanced Natural Language Processing (NLP) techniques to automate and enhance the resume screening process. The proposed system analyzes resumes in real-time, matching them against job descriptions to provide a comprehensive evaluation of candidate qualifications. By employing semantic analysis and contextual understanding, the Smart ATS improves the accuracy of candidate selection while significantly reducing the time and effort required for manual screening. Evaluation metrics, including precision, recall, and F1-score, demonstrate that the Smart ATS outperforms traditional methods, effectively identifying qualified candidates and minimizing biases. The integration of Gen AI not only streamlines the recruitment process but also promotes fairness and transparency in hiring practices. This innovative approach has the potential to transform the recruitment landscape, enabling organizations to make more informed hiring decisions and ultimately leading to better workforce outcomes.

Keywords : Application Tracking System; Generative Artificial Intelligence; Resume Screening; Natural Language Processing; Machine Learning.

References :

1. K. Sri Surya, R. Sharanya, A. Zilani, and Dr. Ch. Niranjan, ”Smart Application Tracking System using Gen AI,” IJIEMR Transactions, vol. 13, no. 4, pp. 505-516, Apr. 2024.
2. W. Jiang, F. Liu, and M. Sun, ”Leveraging Natural Language Processing for Semantic Matching in Resume Ranking,” in Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 10421051, 2022.
3. H. Yang, M. Huang, and Z. You, ”Enhancing Fairness in AI-powered Resume Screening with Explainable AI,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pp. 102-111, 2023.