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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63624

AI Powered Mock Interview System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63624

Volume:

10 |

5 |

May 2026

Pages:

1-18

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63624

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KP

Kashish Pimpalshende

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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SM

Sanju Mandal

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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GC

Gayatri Chippawar

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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TP

Treksha Pachdhare

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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DS

Disha Sakarkar

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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Mentors

PSS

Prof. Shailendra Shende

Computer Science and Engineering

Government College Of Engineering, Chandrapur

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Abstract

A thorough analysis of AI-driven interview systems is presented in this study, focusing on the development of a web-based mock interview platform designed to enhance candidate preparedness. With the increasing demand for efficient and unbiased recruitment processes, this research explores the integration of lightweight language models such as Gemma and TinyLlama for intelligent question generation, answer evaluation, and personalized feedback. The study addresses a critical problem—interview anxiety among students—which often affects their performance in real-world scenarios. To mitigate this, the proposed system enables users to practice in a simulated interview environment, improving confidence and communication skills. Methodologically, the system is built using a structured SDLC approach, incorporating model fine-tuning to adapt to domain-specific interview contexts. The models are trained to generate role-relevant questions and assess responses based on accuracy and relevance. The findings suggest that such AI-powered systems can significantly improve preparation outcomes, offering scalable, consistent, and insightful feedback, thereby bridging the gap between theoretical knowledge and practical interview performance.

KEYWORDS

AI Powered

Cite This Article

Kashish Pimpalshende, Sanju Mandal, Gayatri Chippawar, Treksha Pachdhare, Disha Sakarkar, Prof. Shailendra Shende, "AI Powered Mock Interview System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-18, May 2026. https://doi.org/10.55041/IJSREM63624

Full Article/Conclusion

This research introduces a system that uses artificial intelligence to help people get ready for job interviews. The system combines two technologies: one that reads and understands text on resumes and another that analyzes how people behave on video. It helps figure out if someone's ready for a job. The system uses a tool that helps companies track job applicants so people can make their profiles better for job searches. It also has a feature that stops people from cheating during tests. The results show that the system can spot signs of how people behave, like where they look and how they feel. This gives people feedback that they do not get from regular practice interviews. The goal of AceTheJob is to provide high-quality interview coaching to everyone. This can help reduce stress and improve people's chances of getting a job. The system provides an evaluation of a candidate's readiness for a job interview. It uses Artificial Intelligence to bridge the gap between learning and getting hired. AceTheJob helps candidates prepare for job interviews. Provides feedback to improve their performance.  

References

[1]              A. Vaswani et al., “Attention Is All You Need,” 2023. [2]            S. Minaee et al., “Large Language Models: A Survey,” Mar. 2025, [Online]. Available: http://arxiv.org/abs/2402.06196 [3]            V. B. Parthasarathy, A. Zafar, A. Khan, and A. Shahid, “The ultimate guide to fine-tuning LLms from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities,” Oct.2024, [Online]. Available: http://arxiv.org/abs/2408.13296 [4]            J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” May 2019, [Online]. Available: http://arxiv.org/abs/1810.04805 [5]            J. Kaplan et al., “Scaling Laws for Neural Language Models,” Jan. 2020, [Online]. Available: http://arxiv.org/abs/2001.08361 [6]            L. Ouyang et al., “Training language models to follow instructions with human feedback,” Mar. 2022, [Online]. Available: http://arxiv.org/abs/2203.02155 [7]            A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language Models are Unsupervised Multitask Learners.” [Online]. Available: https://github.com/codelucas/newspaper [8]            H. Touvron et al., “LLaMA: Open and Efficient Foundation Language Models,” Feb. 2023, [Online]. Available: http://arxiv.org/abs/2302.13971 [9]            T. B. Brown et al., “Language Models are Few-Shot Learners,” Jul. 2020, [Online]. Available: http://arxiv.org/abs/2005.14165 [10]         C. Raffel et al., “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer,” Sep. 2023, [Online]. Available: http://arxiv.org/abs/1910.10683

Editorial & Review Transparency

Publication Date

5/21/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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