Artificial Intelligence-Based Chatbot for Student Mental Health Support

This research addresses the urgent concern of student mental health by innovatively implementing an efficacious chatbot intervention. The pri-mary focus is on delivering accessible and personalized support, adopting a mixed-methods approach that combines quantitative insights from pre-intervention and post-intervention mental health assessments with qualitative perspectives gathered through user interviews. The dataset, sourced from Kaggle and GitHub, contains authentic conversations between healthcare providers and patients, grounding the project in real-world scenarios. Leveraging Visual Studio Code, ReactJS, Vite, SCSS, and Dido for chatbot training, keyframes are strategically applied to integrate these technological components seamlessly. Data preprocessing involves a meticulous curation process, emphasizing crucial attributes for addressing student mental well-being effectively. The resultant met-rics encompass comprehensive user engagement data, pre- and post-intervention mental health scores, and valuable qualitative insights. This project stands out for its user-centric design, which makes use of cutting-edge technologies to produce a platform that is welcoming and stigma-free. The final objective is to significantly improve student men-tal health support, which will represent a breakthrough in this field.