Revolutionizing Interview Preparation: An AI-Driven Mock Interview System for Skill Assessment

Abstract

In today\'s highly competitive job market, effective interview preparation is essential. Conventional mock interviews often lack the ability to provide detailed, personalized feedback, which can hinder a candidate’s readiness for specific job roles. Our AI-powered Mock Interview Platform bridges this gap by leveraging advanced Machine Learning (ML) and Natural Language Processing (NLP) technologies to simulate realistic, domain-specific interview scenarios. The system captures video, audio, and relevant data, employing sophisticated preprocessing techniques such as noise reduction. Using Convolutional Neural Networks (CNN) for video analysis and NLP for text evaluation, the platform assesses facial expressions, speech patterns, and technical responses. By extracting key features related to emotional states and technical proficiency, it delivers comprehensive, real-time feedback on a candidate’s problem-solving approach, communication skills, and overall body language. This AI-driven system empowers candidates by enhancing their confidence and improving their ability to perform under pressure. By offering structured insights and targeted feedback, the platform serves as a cutting-edge tool for job interview preparation, equipping users with the technical and interpersonal skills required to excel in today’s dynamic job landscape.

Introduction

The text presents an AI-driven mock interview platform designed to enhance job interview preparation by simulating real-world interview scenarios and providing personalized, data-driven feedback. With the growing role of AI in education and professional development, the system leverages advanced technologies to evaluate both technical knowledge and behavioral performance, helping candidates improve confidence, communication, and interview readiness.

The platform generates role-specific and up-to-date interview questions using Natural Language Processing (NLP) and web scraping. During mock interviews, it captures real-time audio and video data to assess candidates across multiple dimensions. Facial expressions and emotions such as stress, confidence, or anxiety are analyzed using CNN-based video processing, while speech patterns, tone, and clarity are examined through audio analysis tools. NLP models, including BERT, evaluate the relevance, structure, and correctness of candidate responses.

In addition to verbal responses, the system analyzes non-verbal cues such as eye gaze, head movement, hand gestures, posture, and body language using OpenCV, MediaPipe, and deep learning models. All performance data is stored to enable progress tracking, allowing candidates to compare current interviews with previous sessions and identify improvement areas over time.

The platform provides instant and comprehensive feedback, highlighting strengths, weaknesses, emotional state, confidence level, and communication effectiveness. This creates a low-pressure environment where users can practice repeatedly, refine their skills, and build confidence before real interviews. The system also aims to reduce human bias by offering objective, consistent evaluations.

Overall, the AI-powered mock interview system bridges the gap between theoretical knowledge and practical interview experience. By combining real-time multimodal analysis, personalized question generation, and continuous performance monitoring, it offers a holistic and effective solution for modern interview preparation in an increasingly competitive and online-driven job market.

Conclusion

The AI-powered Mock Interview Platform marks a significant advancement in interview preparation, offering job seekers an intelligent and interactive way to enhance their skills. By replicating real interview scenarios and delivering personalized, data-driven insights, the platform helps users boost their confidence, refine their communication, and strengthen their overall performance. Leveraging multimodal analysis—including facial expressions, voice characteristics, and natural language processing (NLP)—the system provides a well-rounded assessment, making it more effective than convention mock interviews.

References

[1] Bharati Thawali D, Pranjal Kalal, Samarth Dugam and Harshada Lokhande, “EmoConfident Interviewer: An AI Mock Interview Evaluator,” International Journal for Research in Applied Science and Engineering Technology, 2024. [2] Rubi Mandal, Pranav Lohar, Dhiraj Patil, Apurva Patil, Suvarna Wagh, “AI-Based Mock Interview Evaluator: An Emotion and Confidence Classifier Model,” International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS), 2023. [3] Prof. Sakharam Kolpe, Sarvesh Patil, Jay Deshmukh, Suraj Jeughal, Yash Misal, “AI-Based Mock-Interview Behavioural Recognition Analyst,” International Journal for Research in Applied Science and Engineering Technology, 2024. [4] Bhagyashree Patil, Vaishnavi Sarmalkar, Farhat Shaikh, Kuntal Thakur, Rohit Wahwal, “AI Mock Interviewer: Preparing You Towards Smarter Interview Practice,” International Journal for Research in Engineering and Management, 2024. [5] Mr. Purushottam S. Chavan, Mr. Jayesh T. Derle, Sejal Sonawane, Pratik Pawar, Renuka Sathe, Kalyani Thakur, “AI-Based Mock Interview Evaluator,” International Journal for Research in Engineering and Management, 2024. [6] Shashank Rai, Alisha Miranda, Samiya Jagirdar, Nidhi Chitalia, “Skillup Bot: AI-Driven Mock Interview Platform,” International Journal for Research in Engineering and Technology, 2024. [7] Amrutha, Mandalapu Bhavana, et al. \"Enhancing interview evaluation: AI-based emotion and confidence analysis in mock interviews.\" Journal of Nonlinear Analysis and Optimization 15.1 (2024): 1507-1511. [8] Christy Crutsinger and Unity Herrer, “Mock Interviews: Leveraging AI Resources to Enhance Professional Skills,” ITAA Proceedings, 2021. [9] B. C. Lee, B. Y. Kim, “Development of AI-Based Interview System for Remote Hiring,” International Journal of Advanced Research in Engineering and Technology, 2021. [10] P. Shwetha, B. Gowthami, CH. Sreeman, G. Amruth, “AI-Based Mock Interview Evaluator: Emotion and Confidence Classifier Model,” ZKG International, Volume IX, 2024. [11] Paresh Narendra, Rakhtate Shriraj Vikas, “AI-Based Interview Critique System: Interview Preparation Companion Using Deep Learning,” International Journal of Creative Research Thoughts, 2024. [12] Harsha Pariyani, Anshika Sinha, Preeti Bhat, Roshni Rote, Prof. N. A. Mulla, “AI-Based Scrutiny Bot for E-Interview Using Natural Language Processing and Emotion Recognition,” Journal of Emerging Technology and Innovative Research, 2020. [13] Dulmini Yashodha Dissanayake, Venuri Amalya, Raveen Dissanayaka, Lahiru Lakshan, Pradeepa Samarasinghe, Madhuka Nadeeshani, Prasad Samarasinghe, “AI-Based Behavioural Analyser for Interviews/Viva,” IEEE, 2021. [14] Prof. Taware R. K., Bhagyashree Shinde, Nikita Rasal, Snehal Ghorpade, “Smart Interview System Using AI Technology,” International Research Journal of Modernisation in Engineering, Technology and Science, 2024. [15] Balasaheb Jadhav, Avadhut Sawant, Arnav Shah, “A Comprehensive Study and Implementation of the Mock Interview Simulator with AI and Pose-Based Interaction,” 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU), 2024. [16] M. Sunitha, B. Vijitha, E. Gunavardhan, “Artificial Intelligence-Based Smart Education System,” 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), 2023. [17] Pankaj Rambhau Patil, Shinde Rushikesh Rajendra, Gosavi Vishakha Mahendra, “Elevating Performance Through AI-Driven Mock Interviews,” International Journal for Research in Applied Science and Engineering Technology, 2024. [18] Weishi Wang, “An Analysis of the Optimization Strategies of Ecological College English Teaching Based on Artificial Intelligence,” 2020 International Conference on Computers, Information Processing and Advanced Education (CIPAE), 2020. [19] Aaradhana Vinayak Rukadikar, Komal Khandelwal, “Adoption of Artificial Intelligence in Personalizing E-Learning: A Helping Hand for K-12 Teachers,” 2023 7th International Conference on New Media Studies (CONMEDIA), 2023. [20] Stoev, Teodor, et al. \"Towards Automated Classification of Adult Attachment Interviews in German Language Using the BERT Language Model.\" IEEE Access (2025). [21] Hao, Shijie, et al. \"Interview-based Depression Detection Using LLM-based Text Restatement and Emotion Lexicon.\" IEEE Transactions on Affective Computing (2025). [22] Flores, Ricardo, et al. \"WavFace: A Multimodal Transformer-based Model for Depression Screening.\" IEEE Journal of Biomedical and Health Informatics (2025). [23] Chiligireddy, Sai. \"The Importance of Technical Competencies in the Engineering Manager Role and How Companies Evaluate Them in Interviews.\" IEEE Engineering Management Review (2025). [24] Li, Yuxin, et al. \"Automated Depression Detection from Text and Audio: A Systematic Review.\" IEEE Journal of Biomedical and Health Informatics (2025)

Copyright

Copyright © 2026 Shrinika Inamdar, Atharva Pawar, Gauri Jarag, Samruddhi Jadhav, Rutuja Padghan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.