Guided Interactive Learning through Chatbot using Bi-directional Encoder Representations from Transformers (BERT)

In this digital era, the smart application is ought to continuously generate a huge amount of data into existence. These data can be utilized to gain a large amount of information that deploys numerous uses. Education is one of the key fields that generate huge amounts of data in existence. However, it is difficult to obtain only the required information because of the speed and volume of data being generated from numerous online educational resources. One of the tools that can be useful in extracting useful information from textual data is text summarization and analysis tools. Many text summarization tools are being developed but largely focus on summarizing a single document effectively. This project aims to create a text summarization tool using natural language processing techniques that can extract relevant and important information from multiple documents to enable users to learn effectively. This information can be presented to users interactively and effectively through a chatbot interface. The tool also performs multiple analyses on the user responses provided to the chatbot to control the conversational flow and personalize the user experience to enhance learning.