International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 18, No. 4, 8 Aug. 2026
Cover page and Table of Contents: PDF (size: 568KB)
GenAI-Driven Interview Performance Assessment: Revolutionizing Recruitment with AI Insights
PDF (568KB), PP.93-104
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Author(s)
Sathvik Vadarevu
1
Manasa Viriyala
1
Garlapati K. V. S. Sai Komal
1
Venkat Vinukonda
1
Jeethu V. Devasia
2,*
Index Terms
AI-driven platform, Convolutional Neural Networks, Large Language Models, YOLO, GenAI
Abstract
This paper proposes IPAMS (Interview Performance Assessment using Gen AI), which is an AI-driven platform that automates interview evaluations using advanced technologies like Convolutional Neural Networks (CNN) to gain insights on facial emotions and expressions, Large Language Models (LLM) to generate and process interview questions, YOLO (You Only Look Once) for real-time object detection, and APIs for speech-to-text transcription and behavioral analysis. The system captures video responses and analyzes key elements such as sentiment, speech patterns, body posture, and facial expressions, generating a detailed report. This report highlights a candidate’s strengths and areas of improvement and is sent directly to their email with actionable insights. IPAMS modernizes recruitment by providing unbiased assessments, saving time and resources for recruiters. For candidates, it offers a valuable mock interview tool, delivering feedback on technical skills, confidence, stress levels, and nonverbal communication. By combining cutting-edge AI and analytics, IPAMS delivers an efficient, objective, and insightful solution for recruitment and self-assessment, benefiting all stakeholders in the interview process.
Cite This Paper
Sathvik Vadarevu, Manasa Viriyala, Garlapati K. V. S. Sai Komal, Venkat Vinukonda, Jeethu V. Devasia, "GenAI-Driven Interview Performance Assessment: Revolutionizing Recruitment with AI Insights", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.18, No.4, pp. 93-104, 2026. DOI:10.5815/ijieeb.2026.04.06
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