Authors - Cristian Castillo-Olea, Clemente Zuniga Gil, Angelica Huerta Abstract - Question paper preparation in educational institutions is conventionally manual and time-consuming, often generating question papers of uneven difficulty and less diversity. This project solves the problem of automatic question paper generation from voluminous academic content available in multiple formats. The motivation for this work is reducing human effort and enhancing efficiency, ensuring fair and balanced assessment generation, while supporting modern digital learning environments. Input content, in the form of text documents, portable document files, presentation slides, images, audio recordings, and video lectures, forms the bedrock of the proposed system; first, it gets preprocessed into a unified textual format through document parsing, optical character recognition, and speech-to-text techniques. Natural language processing approaches like sentence segmentation, tokenization, stop word removal, and extraction of key concepts are subsequently applied on the meaningful and relevant identification of the contents. It follows a hybrid approach relying on the Transformer architecture: a classification model that assesses the importance of a sentence, relevance of concepts, and difficulty level; and a generation model providing question types such as multiple choice, short answer, long answer, case studies, reasoning, fill-in-blanks, and programming. The proposed model goes through training and fine-tuning using publicly available datasets of question-answer pairs and pre- processed information in textbooks. In the experimental results, the proof of efficiency by the proposed approach is shown in generating accurate and diverse question papers with high relevance. Such an approach would definitely ensure much better outcomes for the question papers and the assessment.
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