Published Paper Details:

Preparing question papers manually takes a lot of time and effort. Teachers need to maintain proper marks distribution, question variety, and topic coverage. In many cases, the same content is formatted again and again to create different test papers. This process is repetitive and sometimes inconsistent. In this paper, we present the design and implementation of an intelligent web-based quiz and exam paper generator. The system accepts input in different formats such as PDF, Word, PowerPoint, image files, and plain text. The uploaded content is converted into clean text and processed using natural language processing techniques to identify important topics. Based on the extracted content, the system generates multiple choice questions, short answer questions, and long answer questions following a fixed 100 marks structure. The system is developed using React for the frontend and Flask for the backend. Named Entity Recognition is used to detect key concepts from the text. The generated paper also includes answer keys and proper formatting. Experimental testing shows that the system reduces manual effort and generates structured question papers within a few seconds. The system can be useful for teachers and students in academic environments.