Automated Question Paper Generator Using LLM

Karri Praveen Kumar, Gudimetla Avinash Reddy, Devana Vignesh, Adapa Pavan, Taneti Vamsi, Kasa Rishi and Kommu Sunil Kumar
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Karri Praveen Kumar: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Gudimetla Avinash Reddy: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Devana Vignesh: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Adapa Pavan: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Taneti Vamsi: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Kasa Rishi: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India
Kommu Sunil Kumar: Department of Computer Science and Engineering, Sri Vasavi Engineering College, Tadepalligudem, India

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 4, 266-275

Abstract: The Automated Question Paper Generator (AQPG) represents a transformative advancement in educational assessment, addressing the inefficiencies of manual exam paper creation. Traditional methods rely heavily on instructor effort, often leading to inconsistencies, time delays, and limited scalability. In response, AQPG leverages cutting-edge Natural Language Processing (NLP) and rule-based algorithms to dynamically generate balanced, syllabus-aligned question papers. Educators can customize assessments by specifying parameters such as question type (MCQs, descriptive), cognitive levels (Bloom’s Taxonomy K1–K6), and topic weightage, ensuring adherence to pedagogical standards. Deployed via a Flask-based web application with Python backend and HTML/CSS frontend, the system automates question selection, difficulty balancing, and PDF formatting. Rigorous testing demonstrates that AQPG reduces question paper generation time by over 80% compared to manual methods while maintaining academic rigor and diversity. Its AI-driven randomization mitigates bias, offering a fair, scalable solution for institutions. Beyond efficiency, AQPG has far-reaching implications for standardized testing, remote education, and adaptive learning systems. This study not only validates the feasibility of automated assessment design but also paves the way for future integrations with Learning Management Systems (LMS) and AI-based difficulty adaptation. By bridging the gap between curriculum objectives and evaluative outcomes, AQPG sets a new benchmark for educational technology in the era of digital transformation.

Date: 2025
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