Automatic Question Generation from Textual Data Using NLP

Abstract: Automatic Question Generation (AQG) systems aim to convert textual material into meaningful assessment questions, offering a valuable tool for both educators and learners. Unlike conventional text summarization, AQG involves identifying essential information, generating accurate answers, and creating plausible distractors to form high-quality multiple-choice questions. This paper presents an NLP-based AQG framework that processes instructional content through a sequence of linguistic operations, including text pre-processing, syntactic and semantic analysis, and probabilistic language modeling. The proposed system utilizes established NLP libraries such as SpaCy and NLTK to detect key concepts and automatically construct factual questions related to entities, events, and contextual details. By automating question creation, the approach reduces the manual workload of educators and provides learners with an efficient tool for self-assessment. The study also highlights architectural considerations, discusses implementation challenges, and suggests future improvements to enhance the scalability and accuracy of AQG systems.

Keywords: Automatic Question Generation (AQG), Natural Language Processing (NLP), Educational Technology, Semantic Analysis, Question Formulation and Generation