An automated question-answering system allows students to learn as an integral part of digitized learning. This system responds to
queries using text. We also include a knowledge graph, which significantly enhances the model’s intrigue and improves learners’
understanding. The features of knowledge entity extraction, information point evaluation and analysis, knowledge graph construction from unstructured text, and knowledge entity integration are all explored. The question-answering paradigm we suggest in this
study uses knowledge graphs and BERT (Bidirectional Encoder Representations from Transformers) to provide diverse learners
with quick feedback on the subject. In order to facilitate non-native learners’ understanding, we also include an English-to-Hindi
translation. As a result, access to and continued learning can be very beneficial for educators.