AI Powered Virtual Job Interview Simulator Using Natural Language Processing

Interviews are a crucial gateway to employment in today's highly competitive job market. However, many job seekers face challenges due to inadequate preparation resulting from limited access to realistic and tailored practice sessions. Traditional mock interviews with career counselors or peers often lack constructive feedback and fail to replicate actual interview dynamics. The AI-Driven Virtual Job Interview Simulator addresses this gap by utilizing advanced technologies, particularly Natural Language Processing (NLP), to create an interactive, real-time interview practice platform. This simulator generates role-specific questions aligned with the user's desired career path and evaluates responses in real-time through sentiment analysis and coherence assessments. It offers constructive, personalized feedback that highlights both strengths and areas for improvement. The adaptive learning design of the simulator continuously adjusts question difficulty based on user performance, ensuring a seamless progression. Over time, it learns from individual user behaviors to enhance its feedback, thereby making the experience increasingly personalized and effective. With a user-friendly interface, the system requires no technical proficiency for operation. By combining practicality with cutting-edge AI technology, this innovative tool empowers users by instilling the confidence and skills necessary to excel in interviews, effectively bridging the gap between preparation and employment. The simulator represents a transformative solution for career preparedness, revolutionizing the interview preparation process.

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Publication Details

Published
2025-04-24
DOI
https://doi.org/10.1109/icoei65986.2025.11013504
Citations
1
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
2.76
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article

AI Powered Virtual Job Interview Simulator Using Natural Language Processing

R. Nivash, M. Jaiganesh, Pramod Kumar Parida, M. A. et al.
1 citations
AI and HR Technologies
2.76
article

AI Powered Virtual Job Interview Simulator Using Natural Language Processing

R. Nivash, M. Jaiganesh, Pramod Kumar Parida, M. A., M. K., G.S. Rao
article en
1 citations

Abstract

Interviews are a crucial gateway to employment in today's highly competitive job market. However, many job seekers face challenges due to inadequate preparation resulting from limited access to realistic and tailored practice sessions. Traditional mock interviews with career counselors or peers often lack constructive feedback and fail to replicate actual interview dynamics. The AI-Driven Virtual Job Interview Simulator addresses this gap by utilizing advanced technologies, particularly Natural Language Processing (NLP), to create an interactive, real-time interview practice platform. This simulator generates role-specific questions aligned with the user's desired career path and evaluates responses in real-time through sentiment analysis and coherence assessments. It offers constructive, personalized feedback that highlights both strengths and areas for improvement. The adaptive learning design of the simulator continuously adjusts question difficulty based on user performance, ensuring a seamless progression. Over time, it learns from individual user behaviors to enhance its feedback, thereby making the experience increasingly personalized and effective. With a user-friendly interface, the system requires no technical proficiency for operation. By combining practicality with cutting-edge AI technology, this innovative tool empowers users by instilling the confidence and skills necessary to excel in interviews, effectively bridging the gap between preparation and employment. The simulator represents a transformative solution for career preparedness, revolutionizing the interview preparation process.

Karpagam Academy of Higher Education (IN)
Openalex Percentile: Top 10%
AI and HR Technologies
2.76
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AI Powered Virtual Job Interview Simulator Using Natural Language Processing — R. Nivash, M. Jaiganesh, et al. · (2025) | TGRS Research Map | TGRS