Intelligent Chat Bot for KU Website: Automated Query Response and Resource Navigation

This project introduces an intelligent chatbot designed to improve user experience on our university website by providing instant, automated responses to common inquiries. Navigating a university website can be challenging for students, applicants, and visitors who seek quick information about admissions, campus services, events, and more. To address this challenge, we developed a chatbot that simulates human conversation using Natural Language Processing (NLP), allowing users to find information more efficiently. The chatbot is powered by a Bidirectional Long Short-Term Memory (BiLSTM) model, an architecture well-suited for understanding complex sentence structures. This model captures contextual information from both directions in a sentence, enabling it to identify user intent with high accuracy. We trained the chatbot on a dataset of intent-labeled queries, enabling it to recognize specific intentions such as asking about campus facilities, academic programs, or event schedules. The NLP pipeline includes steps like tokenization, lemmatization, and vectorization. Tokenization and lemmatization prepare the text by breaking it into manageable units and standardizing word forms, making it easier for the model to recognize similar word patterns. The vectorization process then translates this processed text into numerical data that the model can interpret. Flask is used to manage the backend, allowing seamless communication between the user interface and the BiLSTM model. When a user submits a query, Flask routes the input to the model, processes the prediction, and delivers the appropriate response back to the user interface. This chatbot demonstrates a successful application of NLP in creating interactive, efficient, and user-friendly solutions. By automating responses, it reduces reliance on manual support and ensures users can access relevant information at any time. This project highlights how intelligent chatbots can transform the way users interact with university websites, offering a faster and more engaging experience.