Chatbot for Academic Institutions

This research presents the design and implementation of an intelligent chatbot tailored for academic institutions, specifically focusing on one intuition as of now. Leveraging natural language processing (NLP) techniques, the chatbot interprets user queries and accesses a structured JSON-type database housing intents, patterns, and responses. The database, featuring 47 broad tags with multiple patterns, ensures the chatbot’s accuracy in addressing diverse user inquiries. The Flask framework serves as the system’s backbone, providing a lightweight and versatile backend for efficient query processing. Dynamic buttons in responses enhance user interaction, categorizing queries for a structured experience. Statistical analysis methods, including confusion matrices, assess intent matching accuracy, providing insights for improvement. The research contributes to advancing user engagement and information accessibility in educational environments.