Blooms Taxonomy Based Question Paper Generation

Exams are a key part of education, helping to test what students know and can do. However, making question papers the traditional way can be a tiring an\\d time-consuming task for teachers. It’s not always easy to balance the difficulty of the questions and make sure they cover all the important topics in the curriculum. This can sometimes lead to mistakes or uneven question papers. With technology improving quickly, there is now a need for better ways to create exam papers that save time and still maintain high quality. One of the big challenges in this process is ensuring that questions cover different thinking levels, as explained in Bloom’s Taxonomy. This framework organizes learning into categories like remembering, understanding, applying, and analyzing. This project introduces an automated system to make creating question papers faster and easier. Using machine learning, the system can generate questions that match Bloom’s Taxonomy and create well-balanced papers. These papers are customized to meet the needs of the subject and the educational goals of the institution. To make the process even smoother, the system can produce the question papers as PDFs. This makes it simple for teachers to review, share, and store them. Automation reduces the chances of errors and gives teachers more time to focus on teaching and helping students. In short, this project uses smart technology to simplify exam paper creation. It ensures that question papers are fair, high-quality, and aligned with educational goals, making life easier for teachers and improving the learning experience for students.