A Comparative Review for Automatic Question Generation Using NLP

This project introduces an innovative approach to advance the field of automatic question generation using natural language processing (NLP), with a specific focus on Bloom’s Taxonomy. With the increasing availability of resources and online learning platforms there is a need for efficient methods to create diverse and contextually relevant questions. The main goal of this project isto develop a system that can automatically generate questions using Natural Language Processing (NLP) techniques aligned with first three cognitive levels of Bloom’s Taxonomy: remembering, understanding, and applying. This project will make a contribution to the field of NLP by providing a framework for automatic question generation. The project follows stages; preprocessing the input text identifying concepts and information creating question rules and generating different versions of questions based on these rules. This project utilizes NLP techniques such as Named Entity Recognition (NER) Part of Speech tagging (POS), syntatic analysis and Discourse analysis. The overarching goal is to provide educators, content creators, and learners with an efficient and intelligent tool for generating questions that enhance comprehension and critical thinking. By automating this process, the project seeks to save time and effort while improving the overall learning and assessment experience.