Ai Mockprep - An Ai-Driven Interview Simulation & Resume Optimization System

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Authors: Prof. Neelam Shrivastava, Nikhil Pratap Singh, Pallavi Rawat, Rajpal Nishad, Shubham Bhardwaj
Cited by: 0

Abstract

The hiring industry is undergoing a significant transformation due to the rise of AI-based systems used for resume screening, interview analysis, and candidate shortlisting. However, most existing AI tools serve employers rather than job-seekers. AI MockPrep aims to bridge this gap by offering an AI-powered interview preparation platform that integrates speech analysis, behavioural scoring, and ATS-compliant resume optimization. This research presents a fully functional prototype evaluated through simulated experiments on 120 participants, including final-year students and working professionals preparing for technical roles. The system uses NLP-based semantic scoring, fluency analysis using ML models, and speech-pattern recognition to generate real-time feedback. Experimental results show that candidates who practiced with AI MockPrep for 10 days improved communication clarity by 34%, reduced filler words by 41%, and demonstrated a 26% increase in technical domain accuracy. These simulated research findings highlight the system's effectiveness in providing personalized interview readiness at scale.

Fields of Study & Subjects

AI and HR Technologies Artificial Intelligence in Education Employer Branding and e-HRM

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
11 February 2026
Language
EN