AI-POWERED EXAM QUESTION PAPER PREDICTOR

Abstract

The rapid growth of digital education has created a need for intelligent systems that can assist in academic evaluation and exam preparation. Traditional methods of question paper setting are time-consuming, repetitive, and often lack adaptability to changing syllabi and student needs. To address these challenges, this project presents an AI-powered Question Paper Predictor, a system designed to automatically generate structured and relevant question papers using past examination data and syllabus inputs.

The proposed system integrates Natural Language Processing (NLP) and Large Language Models (LLMs) to analyze syllabus content and previously asked questions. It identifies important topics, extracts key concepts, and generates new questions that align with academic standards. The system follows principles such as Bloom’s Taxonomy to ensure a balanced distribution of difficulty levels, including knowledge-based, analytical, and application-oriented questions.

The workflow of the system involves extracting text from uploaded PDF documents, cleaning and segmenting the data, and processing it using AI models to generate meaningful and contextually accurate questions. A user-friendly interface allows users to upload syllabus or past papers and receive a complete question paper as output, which can also be downloaded for further use.

By automating the question paper generation process, the system reduces manual effort, improves efficiency, and provides a smart tool for both educators and students. It can be used for exam preparation, practice tests, and academic planning. The project demonstrates how artificial intelligence can enhance educational tools by making them more adaptive, scalable, and intelligent.