A Conversational AI System for Daily Support and Risk Detection in Workplace Stress

Abstract

This paper presents the design and implementation of a conversational AI-based chatbot application for the early detection and support of workplace-related psychological stress. The system leverages natural language processing (NLP) through Google’s Gemini large language models, integrated into a custom web-based interface developed using Python, JavaScript, and HTML. The user-centered frontend emulates a familiar coffee shop environment to reduce perceived stigma and foster user engagement in a relaxed digital space.

To assess mental health risk, the application incorporates surveys derived from contemporary mental health datasets. These responses are processed through a decision-support pipeline that utilizes machine learning models to classify users into one of three risk categories: low, medium, or high. Risk levels are masked using themed terminology (e.g., coffee-based metaphors) to maintain an approachable user experience. Based on the determined risk level, users are directed to a corresponding AI chatbot experience tailored to their emotional needs. The user is recommended to use this application daily in which they input their situation and daily mood and are given personalized exercises, words of affirmation, and comforting comments, tailored to their current mood in order to boost morale and decrease stress and anxiety.  

Preliminary testing indicates high user retention rates and satisfactory classification accuracy across test cases. The proposed system demonstrates the feasibility and value of empathetic, AI-driven conversational interfaces in non-clinical mental health support settings. Future development will focus on multilingual support, integration with corporate wellness platforms, and increased personalization through embedded deployment and user-specific customization features.