DESIGNING CONVERSATIONAL WELLNESS INTERFACES: EMOTION-AWARE DIGITAL COMPANIONS FOR MENTAL WELL-BEING

Abstract

The necessity to develop conversational wellness interfaces is now an urgent concern in the development of emotion-sensitive digital companions on mental health. This paper bridges this gap of generic talking machines and the nuances of emotional needs of the customers that need constant psychological assistance. The aim of the study is to present the best human-oriented model of conversational wellness systems that can detect, decode, and respond to emotional conditions in a responsive and ethically appropriate manner. The process has integrated natural language processing, understanding of paralinguistic cues, and affection computing models to determine emotion of the user when communication is through text and where the communication input is multimodal and optional. With the help of such inferences, dialogue management strategies transform the tone, the pace, the level of empathy, and cogitates about the prompts simultaneously. The system is developed in a way that it facilitates mental health practices, such as emotional validation, reflection of stress, mood monitoring, and directed coping behaviors and is not explicitly clinical in its offerings. Future gains on perceived empathy, conversational trust and continued interactions are experimental compared to non-emotion-aware interfaces as evaluated by simulated user interactions and direct pilot user tests. Users allege that they are even more emotionally precise and that they are better understood in the process of communication. The findings indicate that the emotion-sensitive conversation design can have a positive effect on the digital wellness experience under the conditions of transparency, user control, and privacy-saving strategies.