Investigating AI in Peer Support via Multi-Module System-Driven Embodied Conversational Agents

TL;DR

This study explores the use of large language model-driven embodied conversational agents, based on CBT, to provide accessible and empathetic peer support for young people's mental well-being, addressing limitations of previous systems.

Contribution

It introduces a multi-module system leveraging LLMs for emotionally sensitive peer support, and evaluates user perceptions in a mental health context.

Findings

Participants valued empathetic and context-aware responses.

Trust and response quality influenced user perception.

Design insights for future mental well-being support systems.

Abstract

Young people's mental well-being is a global concern, with peer support playing a key role in daily emotional regulation. Conversational agents are increasingly viewed as promising tools for delivering accessible, personalised peer support, particularly where professional counselling is limited. However, existing systems often suffer from rigid input formats, scripted responses, and limited emotional sensitivity. The emergence of large language models introduces new possibilities for generating flexible, context-aware, and empathetic responses. To explore how individuals with psychological training perceive such systems in peer support contexts, we developed an LLM-based multi-module system to drive embodied conversational agents informed by Cognitive Behavioral Therapy (CBT). In a user study (N=10), we qualitatively examined participants' perceptions, focusing on trust, response quality, workflow integration, and design opportunities for future mental well-being support systems.