Rasel, Annajiat AlimSwapno, Ahmed SymumHamid, Mohammad RafidShaheer, SafwanTaz, Yaseen Nur2024-07-032024-07-03©20232023ID 20101308ID 20101491ID 22241148ID 22241147http://hdl.handle.net/10361/23647Cataloged from PDF version of thesis.Includes bibliographical references (pages 38-39).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.Natural Language Processing (NLP) is one of the most revolutionary technologies today. It uses artificial intelligence to understand human text and spoken words. It is used for text summarization, grammar checking, sentiment analysis, and advanced chatbots and has many more potential use cases. Furthermore, it has also made its mark on the education sector. Much research and advancements have already been conducted on objective question generation; however, automated subjective question generation and answer evaluation are still in progress. An automated system to generate subjective questions and evaluate the answers can help teachers in assessing student work and enhance the learning experience of the students by allowing them to self-assess their understanding after reading an article or a chapter of a book. This research aims to improve current NLP models or make a novel one for automated subjective question generation and answer evaluation from text input.48 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Question generationAnswer evaluationMachine learningLanguage processingAutomatic answer gradingNatural language processing (Computer science).Artificial intelligence.Thesis