Authors : Tejashwini E.; Dr. Girish Kumar D.; SreeLakshmi J.

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

Google Scholar : https://tinyurl.com/3a3z9d2u

Scribd : https://tinyurl.com/345z45fv

DOI : https://doi.org/10.38124/ijisrt/26apr1791

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

Abstract : As digital recruitment continues to expand, or- ganizations increasingly require assessment systems that en- sure fairness while efficiently managing large applicant pools. Traditional hiring approaches often rely heavily on subjective human judgment, which can lead to inconsistency and unin- tended bias. To address these challenges, this study presents CogniView, an advanced framework that evaluates candidates using a combination of structured aptitude assessments, natural language processing techniques, and behavioral analytics. The system is designed with an end-to-end architecture that handles candidate authentication, initial skill evaluation, AI-based anal- ysis of responses, and comprehensive reporting driven by data insights. By generating transparent and explainable evaluation metrics, CogniView improves both the accuracy and efficiency of recruitment processes, while still allowing final decisions to be guided by human judgment. Experimental results demonstrate that the proposed approach enhances objectivity and significantly accelerates modern hiring workflows.

Keywords : AI Interview Evaluation, Cognitive Assessment, Natural Language Processing, Aptitude Test, Multimodal Ana- Lytics, Recruitment Automation.

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