Article’s

The process of designing question papers in academic institutions is often manual, time consuming, and prone to inconsistencies in terms of difficulty, coverage, and structure. With the growing demand for scalable and standardized assessment systems, there is a need for intelligent automation in exam generation. This paper presents PaperPilot, an AI-driven system that automates the generation of question papers directly from syllabus documents in PDF format. The proposed system leverages Natural Language Processing (NLP) techniques to extract, preprocess, and structure syllabus content, followed by the use of Large Language Models (LLMs) to generate contextually relevant questions. PaperPilot allows users to configure parameters such as difficulty level, question types, marks distribution, and exam patterns, enabling flexible and customized paper creation. In addition, the system provides AI-based insights, including topic importance analysis and predicted question trends, along with an integrated student assistant for real-time doubt resolution and concept explanation.