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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65278
Volume:
10 |
6 |
Jun 2026
Pages:
1-4
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 Engineering
Zeal College Of Engineering and Research
Computer Engineering
Zeal College Of Engineering and Research
Computer Engineering
Zeal College Of Engineering and Research
Computer Engineering
Zeal College Of Engineering and Research
Computer Engineering
Zeal College Of Engineering and Research
This paper presents the Perceptive Test Portal, an intelligent web-based assessment platform designed to automate academic examinations and reduce administrative workload. The system integrates Natural Language Processing (NLP) techniques and Transformer-based models for Automated Question Generation (AQG). The platform is implemented using a scalable Spring Boot architecture that provides secure role-based access control, RESTful API communication, efficient database management, and seamless integration with machine learning services. The AQG module employs Bidirectional Encoder Representations from Transformers (BERT) for Answer Span Identification (ASI) and Text-to-Text Transfer Transformer (T5) for context-aware question generation. Comparative analysis indicates that T5 provides superior inference speed and lower computational overhead compared to similar transformer architectures, making it suitable for real-time assessment generation. The proposed system improves scalability, maintainability, security, and assessment quality while reducing manual effort in examination management.
KEYWORDS
The Perceptive Test Portal integrates Spring Boot, Natural Language Processing, and Transformer-based Automated Question Generation techniques to create a secure and scalable online assessment platform. By utilizing BERT for Answer Span Identification and T5 for question generation, the system generates high-quality and context-aware assessment content. The adoption of Spring Boot enhances maintainability, security, scalability, and performance while supporting seamless integration with AI services. The platform reduces manual effort, enables adaptive testing, and improves the overall assessment experience for educational institutions.
[1] E. Avuçlu and S. Özdemir, “An interactive and advanced online exam platform for both teachers and students,” 2025. [2] J. Berengueres, “How to regulate large language models for responsible AI,” 2024. [3] Z. Lin et al., “Evaluating text-to-visual generation with image-to-text generation,” 2024. [4] S. Aftan and H. Shah, “A survey on BERT and its applications,” 2023. [5] H. Prasetyaningrum and R. A. Nugroho, “Online exam proctoring system based on artificial intelligence,” 2023. [6] M. Alsubait et al., “Automatic quiz question generation,” 2018.
Publication Date
6/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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