A Context-Aware Generative AI Framework for Automated Interview Evaluation and Intelligent Feedback Generation

Mr.Kanikireddy Pavan Teja Reddy , Ms.Kottapu Tulasi Priya , Ms.Allampalli Vasavi , Mr.Atragada Ramesh , Mr. S.V.V.D Venu Gopal

Artificial Intelligence, Speech-to-Text, Natural Language Processing, Large Language Models, Automated Interview Assessment, Candidate Evaluation, Real-Time Feedback, Semantic Analysis, Recruitment Analytics, Web-Based Interview System.

The rapid adoption of Artificial Intelligence (AI) in recruitment has created new opportunities for improving the efficiency, consistency, and scalability of interview evaluation. Traditional interview processes often rely on subjective human judgment, delayed feedback, and inconsistent assessment criteria, which can negatively affect hiring quality and candidate experience [1], [2]. Recent studies have shown that AI-assisted interview systems can improve evaluation accuracy by analyzing candidate responses, communication patterns, and behavioral cues in virtual interview settings [1], [4], [9]. In parallel, advancements in Large Language Models (LLMs) have demonstrated strong capability in contextual understanding, semantic reasoning, and automated feedback generation, making them highly suitable for intelligent interview assessment applications [1], [8], [14]. This paper presents an AI-powered automated interview feedback generation system that integrates Speech-to-Text (STT), Natural Language Processing (NLP), and Large Language Models (LLMs) to provide real-time and structured candidate evaluation. The proposed system begins with role-specific interview initialization through a lightweight web-based interface, where candidate details and job preferences are collected. Based on the selected role, the system dynamically generates technical and behavioral interview questions tailored to the candidate profile [3], [8]. During the interview session, candidate responses are captured through audio input and converted into text using speech recognition. The transcribed responses are analyzed using NLP techniques to evaluate communication clarity, response relevance, logical flow, and answer quality [9], [11], [12]. An LLM-based semantic evaluation module further assesses contextual understanding, identifies strengths and weaknesses, and generates personalized feedback. The system then produces a detailed audit report comprising competency-wise scores, overall performance summaries, skill gap analysis, and actionable recommendations. The proposed framework offers a lightweight, scalable, and practical end-to-end solution for intelligent interview assessment, reducing evaluator bias, improving feedback consistency, and enabling efficient decision-making in modern digital recruitment workflows [5], [6], [15].

Volume 14 Issue 4, April-2026

Pages : h15-h26