IJRTI
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)

Call For Paper

For Authors

Forms / Download

Published Issue Details

Editorial Board

Other IMP Links

Facts & Figure

Impact Factor : 8.14

Issue per Year : 12

Volume Published : 11

Issue Published : 120

Article Submitted : 23800

Article Published : 9146

Total Authors : 24342

Total Reviewer : 837

Total Countries : 162

Indexing Partner

Licence

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Published Paper Details
Paper Title: AI - Powered Job Interview Simulation System
Authors Name: SUDHARSAN S , SUBASREE G S , SUBATHRA A , VIKNESWARY P , GANESHEN P
Download E-Certificate: Download
Author Reg. ID:
IJRTI_210855
Published Paper Id: IJRTI2604224
Published In: Volume 11 Issue 4, April-2026
DOI:
Abstract: AI - Powered Job Interview Simulation System 1)ABSTRACT : The interview stage is essential for measuring both technical competence communication skills and problem-solving capabilities of applicants but many students and employment candidates are often unable to access to practical interview scenarios and organized feedback which is which is crucial for proper preparation in that regard this paper presents an ai powered job interview simulation system which puts forth an intelligent and an intelligent platform designed for interview practice and candidate evaluation by examining resumes uploaded as pdf format via optical character recognition ocr and natural language processing nlp to extract out key info like competencies credentials and experience based out of the present study we have developed a which which presents a personalized set of interview questions is created for the candidate from the available question pool which are relevant with reference to the candidates profile also we have integrated biometric verification which is done in real time via facial recognition which we have put in via the local binary pattern histogram lbph algorithm to confirm user identity and to also prevent against the issue of some else taking the interview which is not the actual person we implemented the system using html and css for the front end and python with the flask framework for the back end which is also for the systems integration we also used opencv pypdf2 numpy and scikit-learn for image processing document parsing and data analysis also we present auto evaluation and interactive feedback to the candidates which is to help them to do better we did some tests which showed that the put forth system we have is very efficient secure and scalable for use in todays interview preparation and candidate evaluation. Keywords : Artificial Intelligence (AI), Interview Simulation, Resume Analysis, Optical Character Recognition (OCR), Natural Language Processing (NLP), Facial Recognition, Local Binary Pattern Histogram (LBPH), Candidate Assessment, Adaptive Question Generation, Flask Framework. 2) INTRODUCTION In today’s competitive job market, interviews are a key part of evaluating a candidate’s technical knowledge, communication skills, confidence, and problem-solving ability. However, many students and job seekers struggle due to lack of practice, nervousness, and limited exposure to real interview environments. To address this issue, this paper proposes an AI-Powered Job Interview Simulation System that helps candidates prepare for HR and technical interviews in a structured and personalized way. The system analyzes resumes uploaded in PDF format using OCR and NLP to extract important details such as skills, qualifications, and experience, and generates role-specific interview questions. It analyzes facial expressions, speech patterns, and responses to provide AI-driven scoring and personalized feedback. Developed using HTML, CSS, Python, Flask, OpenCV, PyPDF2, NumPy, and Scikit-learn, the proposed system offers an intelligent, practical, and scalable solution for modern interview preparation and candidate assessment. 3)SURVEY [1] This research introduces a smart ai-enabled interview rehearsal and evaluation system engineered to refine evaluation session abilities preparation through structured practice and personalized assessment the system analyzes resumes using nlp techniques and conducts hr technical and aptitude-based interview rounds it further applies ai-driven semantic analysis and similarity scoring to evaluate reaction and generate performance inspection aimed at job seeker develop confidence and readiness to face interviews [2] This study delivers an intelligent ai-based interview practice tool that leverages cloud-based infrastructure for scalable performance and safe data management to enhance domain-specific interview readiness in addition to voice verbal answers and interview session rehearsals users can receive ai-driven assessments using nlp and ml approaches the system provides methodical feedback by comparing responses to predefined criteria the study also addresses technology problems optimization techniques and implementation difficulties for ai-based interview platforms [3] This paper describes the development and testing of an ai-driven virtual mock interview platform to help clients perform better during interviews the system which was created with the mern stack assesses responses for accuracy and relevance employing nlp voice recognition and semantic comparison methods validation results show significant improvements in interview skills and increased user confidence demonstrating the platforms effectiveness in preparing candidates for real-world interviews [4] An ai-powered mock interview system that improves interview practice with realistic virtual interactions and personalized feedback is presented in this study while image algorithmic procedures examine non-verbal indicators and facial cues the system employs automated content evaluation to assess verbal responses grounded in coherence relevance together merging sentiment and voice analysis is used to evaluate articulation intonation and spoken data pitch allowing for a thorough multi-modal assessment of candidate performance and enhancing overall interview preparedness [5] In order to improve early-stage recruiting decisions by extracting role-specific and nuanced candidate information, this research suggests using massive language models as virtual interviewers. By using a rubric-based examination to update ideas about applicants' latent abilities, the system assesses candidates beyond the conventional resume screening process. The potential of LLM-driven systems for scalable and economical candidate evaluation is highlighted by experimental results on simulated interviews, which show effective convergence towards applicant skill levels. [6] In order to enhance corporate interview readiness, this work presents a polyglot artificial intelligence (AI) interview training system that makes use of customized and ethnically sensitive simulations. In order to create realistic interview environments and enable real-time conversational assessments, the system matches interview scenarios with user resumes and job criteria using massive language models with retrieval-augmented generation. Included is voice synthesis. Technologies for virtual avatars and speech recognition The trials' outcomes demonstrate improved conversational preparedness. outstanding client satisfaction and precise multilingual adherence to work standards [7] In order to improve hiring procedures by lessening the cognitive load on interviewers, this work proposes an AI-assisted interview support system. During interviews, the system offers real-time capabilities including skill-evidence mapping, intelligent note-taking, and adaptive question design. In addition to exposing usability issues and worries about trust and interaction complexity in human-AI collaboration, evaluation results show decreased documentation effort without raising stress. [8] In order to enhance interview preparation this study proposes an ai-based interview platform that simulates real-world interview scenarios using an android application using generative intelligent systems and language processing techniques the system analyzes written and spoken responses to assess technical mastery accuracy and expressive abilities it offers a scalable and a versatile platform for effective interview coaching that uses integrated learning modules to develop both technical expertise and soft skills while offering immediate personalized feedback [9] In order to improve interview readiness through customized and interactive simulations, this study introduces a generative AI-based mock interview platform. The system conducts resume-based, role-specific interviews in real time by combining massive language models with natural language processing, computer vision, and speech recognition. It provides AI-driven feedback to enhance performance and close the gap between practice and actual interview situations by assessing user responses based on communication skills, grammar, and confidence. [10] This study offers an AI-based simulated interview system to enhance candidates' social and communication skills under realistic interview conditions. The system uses virtual recruiters and signal processing techniques to analyze behavior, emotions, and performance in real time. Users can track their progress and improve their interview readiness with tools like voice analysis, behavioral feedback, and facial expression analysis. The proposed approach shows how social skills and interview performance can be enhanced by AI-powered platforms. 4)EXISTING SYSTEM Current interview preparation approaches mainly rely on self-study question banks online tutorials aptitude platforms and manual mock interviews which often lack direct real-time communication consistent evaluation and tailored feedback over the past few years several ai-based interview systems have been introduced using natural language processing nlp speech analysis and facial expression recognition to simulate interviews and assess candidate performance some platforms can generate role-specific questions and provide automated evaluation based on answer accuracy grammar speech fluency or basic behavioral indicators however most existing systems focus only on limited aspects of interview assessment and do not offer a fully integrated solution that incorporates resume evaluation adaptive question generation behavioral monitoringand candidate-specific feedback in a single platform hence there is still a need for a more intelligent interactive and comprehensive interview simulation system for effective candidate preparation 5) CHALLENGES IN EXISTING SYSTEMS Although existing interview preparation systems are useful, they still have several limitations that reduce their ability to provide a realistic interview experience. One of the main problems is the lack of personalization, as many platforms use common or fixed interview questions without considering the candidate’s resume, skills, educational background, or job role. Another challenge is that answer evaluation is often based only on keyword matching or simple text comparison, which may not properly judge the quality, meaning, or depth of the candidate’s response. In addition, many systems do not fully evaluate communication and behavioral aspects such as speech fluency, confidence, pauses, over-speaking, facial expressions, and nervousness, which are very important in real interviews. Most existing platforms also do not provide a complete end-to-end solution that combines resume analysis, personalized question generation, behavior monitoring, AI-based scoring, and improvement feedback in one system. Technical issues such as poor lighting, background noise, and reduced face or speech recognition accuracy can also affect the performance of these systems. Therefore, there is a strong need for a more intelligent, accurate, and user-friendly AI-based interview preparation system. 6) METHODOLOGY This is an interview preparation and assessment system that has been created to present a real life interactive and personal experience for candidates throughout the workflow begins with user registration and login after wherein the job seeker chooses identifies the intended job role and interview type mode once that is done the candidate uploads a resume in pdf format we have used pdf parsing ocr and nlp in to analyze the resume which in turn we use to put together a in-depth profile of the candidates expertise and qualifications projects and relevant experience through the processed data the platform puts out custom and occupation-specific role-specific interview questions suited to the candidates profile during the interview session the verbal the interviewees responses are analyzed with the help of ai and nlp tools which we use to determine the relevance clarity and quality of the answers also within the system we look at webcam based analysis of the interviewees facial expressions and study elements of speech like fluency use of pauses speaking speed and over speaking after we put all that together the system puts out a total performance score and we also give out very personal feedback which notes what did great and what needs work also at the end we save off the results of each session which allows applicants to assess how they did along with how they have improved on over time thus we have put forth a smart practical and easy for the user approach to interview prep and also to how we evaluate candidates 7)PROPOSED SYSTEM The proposed system is an AI-powered job interview simulation platform designed to help students and job seekers improve their interview skills through realistic, interactive sessions. Users register and log in, select a job role and interview type, and upload a PDF resume. The system analyzes the resume using PDF parsing, OCR, and NLP to extract skills, education, projects, and certifications, and generates profile-based, role-specific interview questions. During the interview, responses in text or voice are evaluated for relevance, clarity, and quality using AI techniques. Facial expressions and speech patterns are also analyzed to assess confidence, communication, and behavioral cues. A scoring mechanism combines these parameters, and personalized feedback highlights strengths and areas for improvement. The system stores results for progress tracking, while an admin module manages job roles, question banks, and difficulty levels. Overall, the platform provides a comprehensive, intelligent, and scalable solution for modern interview preparation, combining resume analysis, adaptive questioning, AI evaluation, and performance feedback. 8)FLOW DIAGRAM OF PROPOSED SYSTEM 9)EXPECTED OUTCOMES The goal of the proposed smart ai-integrated this interview platform intends to give students together with job applicants an automated and interactive system for refining job interview techniques it is anticipated to replicate real-world interview simulation scenarios lessen anxiety and improve poise the system offers distinctive role-based practice interviews harnesses ai to evaluate responses and examines emotional expressions and communication patterns to evaluate behavioral factors and interpersonal skills personalized feedback identifies strengths weaknesses and areas for improvement promoting progressive learning by sustaining interview analytics of past performance metrics overall it is anticipated that the system will improve technical preparedness verbal and interaction skills self-confidence and interview outcomes while allowing candidates to follow their progress over time over time 10)CONCLUSION The suggested ai-powered job interview simulation system combines speech analysis resume analysis adaptive question generation ai-based answer evaluation and facial expression detection to offer a clever and efficient platform for interview preparation through practical interview practice and tailored feedback the system assists candidates in enhancing their technical expertise communication abilities and self-assurance all things considered it provides a workable and expandable way to improve interview preparedness and facilitate improved performance in actual job interviews 11)RESULTS AND OUTPUT 12) REFERENCES [1] Pushpa Chavan and Sandeep Jadhav, “A General Paper on AI Based Mock Interview System,” 2026. [2] Sharayu Deote, Vaishnavi Pawar, Yeshaswini Pandilwar, Shruti Chandra and Gunashree Bawankule, “AI-PrepMate: AI-Assisted Mock Interview and Feedback System,” 2026. [3] Dr. Vijayant Verma, Rana Padwar, Apurva Chandrakar, Khushi Jaiswal and Palak Mishra, “AI-Powered Mock Interview System for Automated Skill Assessment,” 2025. [4] Sridevi R and Nithyabharathi S, “Virtual Interview Simulator: Leveraging AIML and Vision Technology,” 2025. [5] Harry Stuart, Masahiro Kaneko and Timothy Baldwin, “Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation,” 2026. [6] Truong Thanh Hung Nguyen, Tran Diem Quynh Nguyen, Hoang Loc Cao, Thi Cam Thanh Tran, Thi Cam Mai Truong and Hung Cao, “SimInterview: Transforming Business Education Through Large Language Model-Based Simulated Multilingual Interview Training System,” 2026. [7] Zhengtao Xu, Nattapat Boonprakong, Casimiro Pio Carrino, Zimo Xia, Yu-An Chen, Yi-Chieh Lee, Zicheng Zhu and Rabih Zbib, “InterPilot: Exploring the Design Space of AI-Assisted Job Interview Support for HR Professionals,” 2026. [8] Prof. Swapnali S. Bhokare, Dr. Anand A. Khatri, Mitali Vaidya, Dnyanal Pawar and Akshada Kolhe, “AI Interview Platform: Intelligent Android-Based System for Automated Interview Practice and Evaluation,” 2025. [9] K. Divya Bhavani, G. H. S. V. K. Prasad, V. G. S. Sai Krishna, M. S. Koti Reddy, K. Shanmukh Vardhan and Md. Sadik, “Prep AI - Customized Mock Interview Platform Using Gen AI,” 2026. [10] Prof. Sakharam Kolpe, Sarvesh Patil, Jay Deshmukh, Suraj Jeughale and Yash Misal, “AI Based Mock-Interview Behavioural Recognition Analyst,” 2024.
Keywords: Artificial Intelligence (AI), Interview Simulation, Resume Analysis, Optical Character Recognition (OCR), Natural Language Processing (NLP), Facial Recognition, Local Binary Pattern Histogram (LBPH), Candidate Assessment, Adaptive Question Generation, Flask Framework.
Cite Article: "AI - Powered Job Interview Simulation System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b646-b652, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604224.pdf
Downloads: 000205481
ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2604224
Registration ID:210855
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b646-b652
Country: Salem, Tamil Nadu, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604224
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604224
Share Article:

Share Facebook Twitter Google+ Pinterest LinkedIn Email Tumblr WhatsApp Google Gmail

Click Here to Download This Article

Article Preview
Click Here to Download This Article
Major Indexing from www.ijrti.org
Google Scholar ResearcherID Thomson Reuters Mendeley : reference manager Academia.edu
arXiv.org : cornell university library Research Gate CiteSeerX DOAJ : Directory of Open Access Journals
DRJI Index Copernicus International Scribd DocStoc

ISSN Details

ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

DOI (A digital object identifier)


Providing A digital object identifier by DOI.ONE
How to Get DOI?

Conference

Open Access License Policy

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Creative Commons License This material is Open Knowledge This material is Open Data This material is Open Content

Important Details

Join RMS/Earn 300
IJRTI

WhatsApp
Click Here

Indexing Partner
Copyright © 2026 - All Rights Reserved - IJRTI