Publication

Abstract : Educational technology and natural language processing (NLP) achievements are constantly contributing to new approaches to student’ assessment. In this study, an Automated Question Paper Generation system is created to provide high-quality practice support in education. The system leverages NLP methods for context comprehension and question generating, which involve paradigmatic analysis, dependency, and constituency parsing, both syntactic and semantic, for example, BERT and LDA. Attention mechanisms in GPT and BERT models are among the elements that have been added to the models to have a better way of generating questions. Evaluation metrics like BLEU, ROUGE, Precision, Recall, and Semantic Similarity scores are used to evaluate the system’s competency to produce high-quality questions and answers. This research underscores great strides that have been made in educational technology and NLP applications, resulting in an easy-to-use system for teachers and learners.