Development of Personality Adaptive Conversational AI for Mental Health Therapy Using LLMs

Abstract

Many individuals with mental health issues cannot get access to professional help due to reasons such as lack of awareness, limited availability, and high costs. Conversational agents present a viable alternative to deliver mental health support that is accessible, affordable, and scalable. However, the effectiveness of these agents can vary among users, as different users have different personality types such as extroversion, agreeability, etc. which influence how users interact with chatbots. Therefore, it is important to develop therapy chatbots that adapt to individual personalities. In this study, we highlight the significant role of Personality Adaptive Conversational Agents (PACAs) in mental healthcare. We designed an architecture around traditional ML models and open-source LLMs to build a PACA for mental health (based on the existing iCare project at the DAIS research group at UWB). We utilized the architecture to build a functional prototype and conducted a user study, which concluded that personality adaptiveness is a critical feature for mental health chatbots. The prototype is currently live and freely available for use at http://test.icare.uw.edu:3010/.