Abstract: The process of designing the examination papers is quite lengthy, biased, and time-consuming. The current research introduces a novel solution to create automatic examination papers based on the syllabus documents in the PDF format. The system uses PyMuPDF for extracting information and processing unstructured text using state-of-the-art Natural Language Processing tools. The generator makes use of a transformer neural network model named Flan-T5 which can produce multiple- choice questions (MCQs) along with contextually appropriate distractors and descriptive long-answer questions. The system also incorporates a login module to ensure secure access and provides the option of exporting the question papers in TXT and PDF formats. According to experimental results, the system shows a remarkable improvement in the speed of generating questions and saves almost 85 percent of the time as compared to the conventional technique. The experiments also confirm the quality of the system as far as coherence and grammaticality of the generated questions are concerned.

Keywords: Natural Language Processing, Automatic Question Generation, Transformer Models, PDF Text Extraction, Educational Technology

Downloads: | DOI: 10.17148/IARJSET.2026.13503