Development of an Artificial Intelligence Chatbot System for Depression

Abstract

The growing global burden of mental health disorders, particularly depression, has revealed significant gaps in the
accessibility, affordability, and scalability of traditional therapeutic services. In response, this study presents the
design and implementation of an AI-powered chatbot system aimed at providing mental health support to
individuals experiencing symptoms of depression. The chatbot leverages advancements in artificial intelligence,
natural language processing (NLP), and evidence-based therapeutic practices primarily Cognitive Behavioral
Therapy (CBT), to offer immediate, empathetic, and structured support in a conversational format. The system
was developed using a modular architecture, incorporating a ReactJS-based frontend, a Flask backend interactions,
Rasa for NLP and dialogue management, and MongoDB Atlas for secure data storage. The chatbot was trained
using a preprocessed Kaggle dataset containing anonymized mental health-related user, enabling it to accurately
detect user emotions, classify intents, and generate context-sensitive responses. Its conversational flow was
designed around key CBT principles, guiding users through mood tracking, thought identification, cognitive
restructuring, and the application of coping strategies.