AI-Powered Question Paper Generator

Designing a well-balanced exam requires instructors to review extensive course materials, determine key concepts, and design questions that reflect appropriate difficulty and cognitive depth. This project develops an AI-powered Question Paper Generator that automates much of this process while keeping instructors in full control. The system accepts PDFs, Word documents, PPT slides, and text files, extracts their content, and builds a FAISS-based retrieval index using sentence-transformer embeddings. A large language model then generates multiple question types—MCQs, short answers, and true/false—guided by user-selected difficulty levels and Bloom’s Taxonomy distributions to ensure meaningful coverage. Each question is evaluated with a grounding score that measures how closely it aligns with the source material, improving transparency and reducing hallucination. A React frontend enables instructors to monitor progress, review questions, toggle answers, and export to PDF or Word, while an ASP.NET Core backend manages processing and metrics. The system reduces exam preparation time and enhances consistency across assessments.