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Original Article
AI Based Mock Interview System - Artificial Intelligence Driven Virtual Interview Preparation and Evaluation System
Dr. C. Sathish1
Bavin TR2
Kanth V3
Fardeen L4
Adhil aswaqh A5
1 Associate Professor, Department of Information Technology Er. Perumal Manimekalai College of Engineering Hosur, Tamil Nadu, India. 2 3 4 5 Department of Information Technology Er. Perumal Manimekalai College of Engineering Hosur, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 406-410
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260602057References
1. M. A. Smith and J. L. Roberts, “The role of communication skills in graduate employability: A systematic review,” Journal
of Education and Work, vol. 34, no. 5, pp. 612–627, 2021.
2. P. Kumar and R. Sharma, “Limitations of traditional interview prepa- ration methods: A survey,” International Journal of
Human Resource Management, vol. 29, no. 8, pp. 1445–1467, 2020.
3. T. Brown et al., “Language models are few-shot learners,” in Proc. Ad- vances in Neural Information Processing Systems
(NeurIPS), Vancouver, Canada, 2020, pp. 1877–1901.
4. J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language
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5. Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv preprint arXiv: 1907.11692, 2019.
6. M. Chollet et al., “Automatic assessment of nonverbal behavior and fluency in interview settings,” in Proc. ACM
International Conference on Multimodal Interaction (ICMI), Tokyo, Japan, 2015, pp. 355–362.
7. T. Nakashima, S. Mao, and Y. Matsuyama, “Multimodal personality recognition using audio-visual features in job interview
scenarios,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona,
Spain, 2020, pp. 3987–3991.
8. B. du Boulay, “Artificial intelligence as an effective classroom assistant,” IEEE Intelligent Systems, vol. 31, no. 6, pp. 76–
81, Nov./Dec. 2016.
9. Ramesh and D. Goldwasser, “Toward an adaptive interview coaching system based on item response theory,” in Proc.
International Confer- ence on Educational Data Mining (EDM), Montreal, Canada, 2019, pp. 289–298
of Education and Work, vol. 34, no. 5, pp. 612–627, 2021.
2. P. Kumar and R. Sharma, “Limitations of traditional interview prepa- ration methods: A survey,” International Journal of
Human Resource Management, vol. 29, no. 8, pp. 1445–1467, 2020.
3. T. Brown et al., “Language models are few-shot learners,” in Proc. Ad- vances in Neural Information Processing Systems
(NeurIPS), Vancouver, Canada, 2020, pp. 1877–1901.
4. J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language
understanding,” in Proc. NAACL-HLT, Minneapolis, MN, USA, 2019, pp. 4171–4186.
5. Y. Liu et al., “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv preprint arXiv: 1907.11692, 2019.
6. M. Chollet et al., “Automatic assessment of nonverbal behavior and fluency in interview settings,” in Proc. ACM
International Conference on Multimodal Interaction (ICMI), Tokyo, Japan, 2015, pp. 355–362.
7. T. Nakashima, S. Mao, and Y. Matsuyama, “Multimodal personality recognition using audio-visual features in job interview
scenarios,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona,
Spain, 2020, pp. 3987–3991.
8. B. du Boulay, “Artificial intelligence as an effective classroom assistant,” IEEE Intelligent Systems, vol. 31, no. 6, pp. 76–
81, Nov./Dec. 2016.
9. Ramesh and D. Goldwasser, “Toward an adaptive interview coaching system based on item response theory,” in Proc.
International Confer- ence on Educational Data Mining (EDM), Montreal, Canada, 2019, pp. 289–298
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