Automated Question Generation System Using NLP

This research paper proposes the use of Natural Language Processing (NLP) techniques for generating multiple-choice questions (MCQs) from an input paragraph. MCQs are widely used in educational assessments, but creating effective MCQs can be a challenging task for educators. The proposed approach involves extracting key phrases and entities from the input paragraph to generate questions and answer options using neural networks, attention mechanisms, and transfer learning. The paper provides an overview of various NLP-based approaches to question and MCQ generation and highlights the challenges associated with MCQ generation, such as ensuring grammatical correctness and creating effective distractors. The effectiveness of the proposed approach is evaluated through empirical studies, which demonstrate high accuracy and comparability to human-generated MCQs. The paper also discusses the potential applications of NLP-based question generation in educational assessments, such as formative and interim assessments. It emphasizes the advantages of this approach, such as its efficiency and comprehensive coverage of the lesson. Overall, this research paper contributes to the field of education by providing insights into NLP-based MCQ generation and its potential for improving educational assessments. It also underscores the need for further research in this field.