Designing Personality-Adaptive Conversational Agents for Mental Health Care

TLDR

Millions of people face mental health problems each year, yet provider shortages and access barriers limit care, prompting interest in conversational agents that can offer scalable support. The study aims to design personality‑adaptive conversational agents (PACAs) for mental health care. Using a design science research approach, the authors iteratively derived six design principles for PACAs in mental health settings. Evaluation by psychologists and psychiatrists indicates that PACAs are a promising source of mental health support, and the design principles can guide practitioners to improve user interaction.

Abstract

Millions of people experience mental health issues each year, increasing the necessity for health-related services. One emerging technology with the potential to help address the resulting shortage in health care providers and other barriers to treatment access are conversational agents (CAs). CAs are software-based systems designed to interact with humans through natural language. However, CAs do not live up to their full potential yet because they are unable to capture dynamic human behavior to an adequate extent to provide responses tailored to users' personalities. To address this problem, we conducted a design science research (DSR) project to design personality-adaptive conversational agents (PACAs). Following an iterative and multi-step approach, we derive and formulate six design principles for PACAs for the domain of mental health care. The results of our evaluation with psychologists and psychiatrists suggest that PACAs can be a promising source of mental health support. With our design principles, we contribute to the body of design knowledge for CAs and provide guidance for practitioners who intend to design PACAs. Instantiating the principles may improve interaction with users who seek support for mental health issues.