Quantum - Assisted AI Interview Intelligence and Placement Readiness Platform

Author

Dr N. Lakshmi Devi, MD. Ashraf Ali, V. Niharika, K. Manoj Lakshmi Narayana, P. Ajay


Abstract

 Campus placements play a prominent role in student’s career. Many students find it challenging to prepare effectively for interviews due to the absence of adaptive mock interview platforms and personalized performance analysis. However, existing platforms primarily focus on aptitude and coding assessments, offering limited support for comprehensive interview preparation. This project proposes a Quantum-Assisted AI Interview Intelligence and Placement Readiness Platform that conducts adaptive technical and HR interviews based on a student's resume, skills, projects, and target job role. The system employs an AI interviewer to generate dynamic follow-up questions and includes coding assessments with an integrated code editor supporting multiple programming languages. Candidate responses are analyzed using Natural Language Processing, speech processing, and visual analysis to evaluate technical knowledge, coding performance, communication skills, project understanding, and behavioral indicators. Based on the assessment, the platform generates personalized feedback and interview-readiness reports. Quantum Approximate Optimization Algorithm (QAOA) is used in this system to optimize personalized interview preparation paths by considering individual performance, skill gaps, target role, and available preparation time. This enables adaptive learning and enhances student’s readiness for campus placement interviews.


Keywords

AI Interview, Adaptive Interviewing, Coding Assessment, Candidate Selection, Quantum Optimization



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References


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