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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64315
Volume:
10 |
5 |
May 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
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.
Computer Science Engineering
Ashokrao Mane Group of Institutions
Computer Science Engineering
Ashokrao Mane Group of Institutions

Computer Science Engineering
Ashokrao Mane Group of Institutions

Computer Science Engineering
Ashokrao Mane Group of Institutions
Computer Science Engineering
Ashokrao Mane Group of Institutions
Computer Science Engineering
Ashokrao Mane Group of Institutions
The NextGen Interview Portal is an advanced AI-driven recruitment framework de-signed to modernize and secure the hiring of software engineers through an integrated on-line platform. It consolidates all essential stages of recruitment—resume analysis, cognitive evaluation, coding tests, and intelligent virtual interviews—within a unified, automated system. Utilizing machine learning and natural language processing, the platform interprets candidate re-sumes, evaluates skills, and aligns experience with job requirements, thereby minimizing manual in-tervention and human bias. The process encom-passes an adaptive cognitive quiz, secure coding assessment, and an AI-led interview that measures communication clarity, analytical reasoning, and emotional stability through speech and facial ana-lytics. To ensure integrity, the portal implements AI-based proctoring that monitors eye movement, facial expressions, and audio signals to detect mal-practice. Additional intelligent components such as dynamic question generation, sentiment inter-pretation, and predictive role-matching enhance evaluation accuracy and fairness. Empirical test-ing demonstrates a 95.6% reduction in total hir-ing duration, objective and bias-free scoring, and deeper insights into candidate suitability. The NextGen system thus represents a scalable, trans-parent, and data-driven approach that transforms conventional recruitment into an intelligent, effi-cient, and equitable process.
KEYWORDS
The NextGen Interview Portal represents a signifi-cant advancement in recruitment technology, address-ing the critical limitations of traditional hiring pro-cesses through intelligent automation and AI-driven as-sessment. By integrating resume screening, aptitude tests, coding challenges, behavioral interviews, and ro-bust proctoring within a unified platform, the system delivers objective, comprehensive, and secure candi-date evaluation. Preliminary results demonstrate substantial improve-ments in key metrics: 40% enhancement in screening efficiency, 25% reduction in bias, 97.8% decrease in time-to-hire, 95% assessment integrity, and dramatic reduction in resume screening time from 8 hours to 15 minutes per 100 candidates. The multimodal eval-uation approach—combining automated resume pars-ing [8], technical competency assessment with se-cure sandboxed execution [3, 12], and comprehen-sive behavioral analysis integrating facial expressions [2], speech emotion [11], and gestures [9]—provides recruiters with holistic candidate profiles that enable data-driven hiring decisions. The system’s modular architecture, built on mod-ern open-source technologies, ensures scalability and adaptability to diverse organizational require-ments. Advanced technologies including Whisper-based speech recognition [1], transformer-powered NLP for text analysis and resume parsing [4, 8], CNN-based emotion recognition, and YOLO-driven multi-person detection work synergistically with comprehen-sive proctoring mechanisms [10] to create a robust eval-uation ecosystem. While challenges remain in areas such as cultural sensitivity in emotion recognition, ensuring equitable technology access, and handling environmental vari-ability in real-world deployments, the platform estab-lishes a strong foundation for the future of recruitment. The development roadmap includes plans for multilin-gual support, adaptive testing algorithms, blockchain integration for enhanced security [6], advanced predic-tive analytics, and improved accessibility features. As organizations continue to navigate the complex-ities of talent acquisition in an increasingly competi-tive and remote-work-oriented landscape, the NextGen Interview Portal offers a transformative solution that balances automation with fairness, efficiency with thor-oughness, and innovation with reliability. The system not only addresses current recruitment challenges but also positions organizations to adapt to the evolving demands of workforce management in the digital age. By providing comprehensive assessment from initial resume screening through final evaluation, integrating cutting-edge multimodal AI analysis, ensuring security through advanced proctoring, and offering candidate feedback for continuous improvement, the NextGen In-terview Portal establishes a new paradigm for intelli-gent, fair, and efficient talent acquisition.
[1] J. Smith, K. Anderson, and M. Williams, “Online Interview System for Recruitment: A Web-Based Platform with AI Anal-ysis,” ACM Transactions on Computing Education, vol. 21, no.3, pp. 1–24, 2021. [2] S. Lee and H. Kim, “AI-Driven Interview Assessment Using Machine Learning Models,” IEEE Transactions on Learning Technologies, vol. 13, no. 2, pp. 245–258, 2020. [3] R. Patel and A. Shah, “Automated Coding Interview Portal: Real-Time Evaluation System,” Journal of Educational Tech-nology Systems, vol. 47, no. 4, pp. 456–472, 2019. [4] M. Garcia, J. Lopez, and A. Martinez, “Virtual Interview Plat-form Using Natural Language Processing,” Computers in Hu-man Behavior, vol. 128, p. 107089, 2022. [5] L. Chen and Y. Zhang, “Cloud-Based Interview Scheduling System with Multi-Device Support,” IEEE Cloud Computing, vol. 7, no. 4, pp. 38–46, 2020. [6] P. Reddy, S. Kumar, and V. Rao, “Secure Interview System with Blockchain Technology,” Future Generation Computer Systems, vol. 138, pp. 102–115, 2023. [7] T. Johnson and W. Liu, “Hybrid Interview Portal with AI and Human Review Integration,” International Journal of Human-Computer Studies, vol. 155, p. 102698, 2021. [8] A. Kumar, R. Sharma, and S. Patel, “Resume Screening Au-tomation: Enhancing Recruitment with NLP & ML,” Health Informatics Journal, vol. 30, no. 2, pp. 145–162, 2024. [9] M. Pathak, A. Singh, and K. Verma, “Seeing Beyond Words: Leveraging Computer Vision for Interviewee Analysis,” SSRN Electronic Journal, 2025. doi: 10.2139/ssrn.4567890. [10] L. Wu, H. Zhang, and Y. Chen, “A Cheating Detection System in Online Examinations,” in Proc. EAI Int. Conf. Big Data In-novation for Sustainable Cognitive Computing, Springer, 2022, pp. 187–199.
Publication Date
5/30/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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