25, April 2026

CareerMate AI: An AI-Driven Real-Time Interview Assistance System Using Behavioral and Visual Cues

VOLUME-12; ISSUE-4; APR-2026

Author(s): B. Sneha

Authors Affiliations:

UG Student, AIDS, Nalla Malla Reddy Engineering College

DOIs:10.2015/IJIRMF/202604026     |     Paper ID: IJIRMF202604026


Abstract
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Job seeking in a restructured competitive employment field is very much dependent upon how well prepared the individuals are for interviews. Conventional interview methods may not be personalized, doesnot provide instantaneous feedback or be readily available. To solve such problems, this paper introduces CareerMate AI a multimodal AI-based interview preparation system that evaluates candidates using NLP, computer vision, and speech analysis. It provides real-time feedback based on responses, behavior, and communication skills that help users improve interview performance effectively.

The system developed uses techniques based on NLP, facial expression recognition and speech processing. It provides real-time feedback, AI-generated questions (both HR as well as technical) based on resume or job description and grades answers based on relevance, grammar and semantic accuracy. The system recognizes behavioral signals such as eye contact and body language, in addition to vocal characteristics including tone fluctuations, fluency and confidence. The automated scoring system offers organized and impartial assessments, while dedicated feedback reports assist users in recognizing their strengths and weaknesses. The platform is user-friendly and provides scalability for real-world interview preparation scenarios. CareerMate AI improves users’ confidence, fluency, and response relevance before they enter real interview

Artificial Intelligence, Mock Interview System, Interview Chatbot, Career Preparation, Natural Language Processing, Job Application Platform

B. Sneha (2026); CareerMate AI: An AI-Driven Real-Time Interview Assistance System Using Behavioral and Visual Cues, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-12, Issue-4, Available on –   https://www.ijirmf.com/

  1. Chavan and S. Jadhav, “A General Paper on AI Based Mock Interview System,” Int. J. Sci. Res. Sci. Eng. Technol., 2026.
  2. Koshti, P. Gosavi, R. Pagar, P. Khairnar, and S. Talekar, “AI-Powered Interview Preparation System: Integrating Resume Analysis, HR Simulation, and Technical Skill Assessment,” Journal of Engineering Research and Reports, 2025.
  3. K. Jain, R. Sai, G. R. S. Charan, and V. A. Narayana, “AI Interviewer Chatbot for Technical and HR Brilliance: A Tool for Upskilling Candidate,” International Journal of Engineering Research & Technology (IJERT), vol. 13, no. 2, 2024.
  4. Sahu, R. K. Khare, Y. R. Singh, and Y. Sahu, “AI Interviewer Using Generative AI,” in Proc. International Conference on Advances and Applications in Artificial Intelligence (ICAAAI), Atlantis Press, 2025.
  5. Jaybhaye, H. Koli, N. Thakkar, O. Latkar, and M. S. Patil, “Personalized AI-Based Interview Preparation and Evaluation System,” International Journal of Science, Engineering and Technology, vol. 14, no. 1, 2026.
  6. Samrose and E. Hoque, “MIA: Motivational interviewing agent for improving conversational skills in remote group discussions,” Proc. ACM Hum.-Comput. Interact., vol. 6, no. CSCW2, pp. 1–25, 2022.
  7. S. Rai, A. Padthe, and H. N. R., “AI Based Interview Evaluator: An Emotion and Confidence Classifier,” International Advanced Research Journal in Science, Engineering and Technology (IARJSET), vol. 11, no. 4, pp. 299–304, 2024.
  8. Kumar, P. Sharma, and S. Verma, “AI-based mock interview evaluator: An emotion and confidence classifier model,” Int. J. Eng. Res. Technol. (IJERT), 2022.
  9. W. Schuller, S. Steidl, A. Batliner, et al., “The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats,” in Proc. INTERSPEECH, 2018.
  10. Bukya, Ravi, G. Madhu Mohan, and M. Kumar Swamy. “Artificial Intelligence role in optimizing electric vehicle charging patterns reduce costs and improve overall efficiency: A review.” Journal of Engineering, Management and Information Technology3 (2024): 129-138.
  11. -Y. Suen, K.-E. Hung, and C.-L. Lin, “Intelligent video interview agent used to predict communication skill and perceived personality traits,” Human-Centric Computing and Information Sciences, vol. 10, no. 3, 2020.
  12. Xiao, J. Song, et al., “If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills,” in Proc. ACM CHI Conf. on Human Factors in Computing Systems, 2020.
  13. Agrawal, R. A. George, S. S. Ravi, S. Kamath, and A. Kumar, “Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback,” in Proc. ACL Workshop on Multimodal Language, 2020.
  14. Poria, E. Cambria, R. Bajpai, and A. Hussain, “A review of affective computing: From unimodal analysis to multimodal fusion,” Inf. Fusion, vol. 37, pp. 98–125, 2017.
  15. Schuller, S. Steidl, A. Batliner, et al., “Paralinguistics in speech and language—State-of-the-art and the challenge,” Comput. Speech Lang., vol. 27, no. 1, pp. 4–39, 2013.
  16. -Y. Suen, K.-E. Hung, and C.-L. Lin, “Intelligent video interview agent used to predict communication skill and perceived personality traits,” Journal of Ambient Intelligence and Humanized Computing, 2021.
  17. Rasipuram and D. B. Jayagopi, “Automatic assessment of communication skill in interview-based interactions,” Multimedia Tools and Applications, vol. 77, pp. 18709–18739, 2018.
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