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Automatic Question Paper Generator
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence & machine learning
DOI: https://doi.org/10.64388/IREV9I11-1717525
Abstract
Curiosity is the fuel for discoveries and learning, and we humans are always curious about learning novel things. Asking different types of questions is a tendency of a good learner and so the students ask a variety of questions from their teachers. Similarly to examine the students, the teachers also ask questions from the students and also evaluate their performances. Because of their inconsistent minds in certain situations, sometimes humans are not very skilled in asking good questions and because of that we have come with the idea of a system with the help of which we would be able to generate the questions from a text automatically. The system is known as the Automated Question Paper Generator System, which is a fast and secure system and which can also generate questions in a random way The system allows registered users to log in and a new user is allowed to register themselves on the application. The system helps in the process of examination by generating question papers. The project provides the most basic functionality required for staff to conduct an examination. The system allows the user to create courses, divide classes, and generate different question papers. All the courses, clasists, and questions are stored in a database and the user is permitted to create and edit the data. The courses and classes created by registered users can be viewed by the administrator and the administrator has permission to delete users, edit users, edit question papers, edit home page content, edit about page content, and contact page content. It facilitates users to generate question papers with the instant print.
References
[1] Agashe, A., & Chandak, S. (2018). Automatic question paper generation using machine learning. International Journal of Advanced Research in Computer Science, 9(5), 12-18.
[2] Bane, V., & Kamble, A. (2017). Question paper generation using NLP. International Journal of Computer Applications, 164(6), 40-45.
[3] Pradhan, S., & Singh, A. (2019). Automatic question paper generation system using natural language processing. International Journal of Computer Science and Information Technology, 10(2), 37-43.
[4] Shukla, M., & Mishra, P. (2018). Automated question paper generation using NLP techniques. International Journal of Advanced Computer Research, 8(36), 200-204.
[5] Verma, A., & Jain, S. (2016). Automatic question paper generator using artificial intelligence. International Journal of Engineering and Technology, 8(6), 2532-2537.
[6] Automatic Question Paper Generation System – IJCA Online, Vol.166/No.10. Implements a multi-constraint model for creating question papers for examinations.
[7] AI-Powered Question Paper Generation With NLP – IJCRT. Uses AI/NLP models to extract key concepts and learning objectives to generate diverse questions across difficulty levels, customizable per user/institution.
[8] Automated Question Paper Generator Using LLM – IJRIAS / RSIS International. Discussion of a system leveraging a large language model (LLM) + rule-based algorithms to dynamically generate syllabus-aligned papers, with empirical data on reduction in time and maintenance of rigor.
[9] Ramez Elmasri and Shamkant B. Navathe, “Database Management Systems for Educational Applications,” International Journal of Computer Applications, vol. 45, no. 12, pp. 15–21, 2019.
[10] A. Kumar and S. Patel, “Automatic Question Paper Generation System Using Randomization Algorithm,” International Journal of Advanced Research in Computer Science, vol. 11, no. 4, pp. 45–50, 2020.
[11] P. Sharma and R. Singh, “Web-Based Examination Management System,” International Journal of Scientific Research in Computer Science, vol. 8, no. 3, pp. 120–126, 2021.
[12] M. Gupta and K. Verma, “Secure Online Question Paper Generation Using Database Management,” International Journal of Innovative Technology and Exploring Engineering, vol. 9, no. 6, pp. 2100–2105, 2020
How to cite this paper
@article{1717525,
author = {H. Harish Hemanathan, Dr. J. Srinivasan},
title = {Automatic Question Paper Generator},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1119-1129},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717525.pdf},
abstract = {Curiosity is the fuel for discoveries and learning, and we humans are always curious about learning novel things. Asking different types of questions is a tendency of a good learner and so the students ask a variety of questions from their teachers. Similarly to examine the students, the teachers also ask questions from the students and also evaluate their performances. Because of their inconsistent minds in certain situations, sometimes humans are not very skilled in asking good questions and because of that we have come with the idea of a system with the help of which we would be able to generate the questions from a text automatically. The system is known as the Automated Question Paper Generator System, which is a fast and secure system and which can also generate questions in a random way The system allows registered users to log in and a new user is allowed to register themselves on the application. The system helps in the process of examination by generating question papers. The project provides the most basic functionality required for staff to conduct an examination. The system allows the user to create courses, divide classes, and generate different question papers. All the courses, clasists, and questions are stored in a database and the user is permitted to create and edit the data. The courses and classes created by registered users can be viewed by the administrator and the administrator has permission to delete users, edit users, edit question papers, edit home page content, edit about page content, and contact page content. It facilitates users to generate question papers with the instant print.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717525}
}