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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

AI-POWERED VIRTUAL INTERVIEWER WITH REAL-TIME FEEDBACK

Pournima Mali Anand Magar Jagruti More Kunal Patil

Prof. M.D. Ingle

Department of Computer Engineering Jayawantrao Sawant College of Engineering Pune, India

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Abstract

The interview process is a critical gateway to professional employment, yet it remains heavily reliant on subjective human evaluation, scheduling constraints, and inconsistent assessment criteria. Candidates from diverse backgrounds often lack access to structured practice environments that can simulate real interview scenarios and deliver actionable, personalized feedback. This paper proposes an AI-Powered Virtual Interviewer system that addresses these limitations by delivering an end-to-end, automated interview experience with real-time, multi-dimensional feedback. The proposed system dynamically generates domain-specific questions using a fine-tuned large language model, transcribes candidate responses through an advanced automatic speech recognition module, evaluates answer quality through semantic similarity scoring, and simultaneously analyzes communication cues including speech fluency, sentiment, and facial expressions. A personalized post-interview feedback report is automatically generated and delivered to the candidate. This paper surveys five closely related research works in automated interview analysis, spoken dialogue systems, virtual coaching agents, and LLM-based question generation, identifies persistent gaps in existing approaches, and describes how the proposed system is designed to close them.

Keywords - Automated Interview System; Large Language Models; Automatic Speech Recognition; Real-Time Feedback; Sentiment Analysis; Facial Expression Analysis; Question Generation; Natural Language Processing; BERT; Whisper STT

How to Cite this Paper

Mali, P., Magar, A., More, J. & Patil, K. (2026). AI-Powered Virtual Interviewer with Real-Time Feedback. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.264

Mali, Pournima, et al.. "AI-Powered Virtual Interviewer with Real-Time Feedback." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.264.

Mali, Pournima,Anand Magar,Jagruti More, and Kunal Patil. "AI-Powered Virtual Interviewer with Real-Time Feedback." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.264.

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References


  1. Hemamou, G. Felhi, V. Vandenbussche, J.-C. Martin, and C. Clavel, “HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews,” in Proc. AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 661–668, 2019.

  2. Naim, M. I. Tanveer, D. Gildea, and M. E. Hoque, “Automated Analysis and Prediction of Job Interview Performance,” IEEE Transactions on Affective Computing, vol. 9, no. 2, pp. 191–204, Apr.–Jun. 2018, doi: 10.1109/TAFFC.2016.2614299.

  3. E. Hoque, M. Courgeon, J.-C. Martin, B. Mutlu, and R. W. Picard, “MACH: My Automated Conversation coacH,” in Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2013,697–706.



  1. Chen and K. Jokinen, “Spoken Dialogue Systems for Job Interview Training,” in Proc. Workshop on Spoken Dialogue Systems Technology (IWSDS), 2011, pp. 1–10.

  2. Guo, Z. Zhang, and S. Zhao, “Automated Interview Question Generation Using Retrieval-Augmented Large Language Models,” arXiv preprint arXiv:2312.04345, 2023.

  3. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust Speech Recognition via Large-Scale Weak Supervision,” in Proc. Int. Conf. Machine Learning (ICML), vol. 202, 2023, pp. 28492–28518.

  4. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pretraining of Deep Bidirectional Transformers for Language Understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.

  5. Liu et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv preprint arXiv:1907.11692, 2019.

  6. Serengil and A. Ozpinar, “DeepFace: A Lightweight Face Recognition and Facial Attribute Analysis Framework for Python,” in Proc. Innovations in Intelligent Systems and Applications Conference (ASYU), 2021, pp. 1–4.

  7. Brown et al., “Language Models are Few-Shot Learners,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020,1877–1901.

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  • Peer Review Type: Double-Blind Peer Review
  • Published on: Jun 21 2026
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