Smart Q Gen: An AI-Powered Question Paper Generation System Using Hybrid Information Retrieval

Preparing examination question papers is a repetitive and time-consuming task for faculty members. The conventional process requires faculty to review educational materials, identify important concepts, formulate questions, determine suitable difficulty levels, and arrange questions according to examination requirements. Smart Q Gen is an AI-assisted web-based question paper generation system designed to reduce this manual effort by generating questions from faculty-provided educational documents. The system accepts PDF, DOCX, and TXT files and performs text extraction, cleaning, structure detection, and text chunking. A hybrid information retrieval approach combines BM25 keyword retrieval and FAISS semantic retrieval using Sentence Transformer embeddings. Reciprocal Rank Fusion (RRF) is applied to combine the rankings produced by the two retrieval methods. The retrieved context is supplied to the FLAN-T5-Small model together with parameters such as question type, number of questions, difficulty level, and Bloom’s Taxonomy level. Generated questions undergo validation and duplicate checking before being organized into a structured question paper. The system also provides faculty authentication, question preview, PDF export using ReportLab, and MongoDB Atlas-based storage. The proposed system integrates document processing, hybrid retrieval, AI-based question generation, validation, and paper generation into a single faculty-assisted workflow.