Informing Robot Wellbeing Coach Design through Longitudinal Analysis of Human-AI Dialogue

TL;DR

This study analyzes long-term interactions between university students and an AI-based wellbeing coach, revealing user behavior patterns that can inform more autonomous, supportive, and ethically aware robot wellbeing systems.

Contribution

It provides the first detailed longitudinal analysis of human-AI wellbeing dialogues, highlighting interaction dynamics crucial for designing effective robot wellbeing coaches.

Findings

Users guide and steer conversations, showing autonomy.

Patterns of guidance-seeking and emotional expression emerge.

Interaction dynamics inform ethical and supportive design principles.

Abstract

Social robots and conversational agents are being explored as supports for wellbeing, goal-setting, and everyday self-regulation. While prior work highlights their potential to motivate and guide users, much of the evidence relies on self-reported outcomes or short, researcher-mediated encounters. As a result, we know little about the interaction dynamics that unfold when people use such systems in real-world contexts, and how these dynamics should shape future robot wellbeing coaches. This paper addresses this gap through content analysis of 4352 messages exchanged longitudinally between 38 university students and an LLM-based wellbeing coach. Our results provide a fine-grained view into how users naturally shape, steer, and sometimes struggle within supportive human-AI dialogue, revealing patterns of user-led direction, guidance-seeking, and emotional expression. We discuss how these dynamics can inform the design of robot wellbeing coaches that support user autonomy, provide appropriate scaffolding, and uphold ethical boundaries in sustained wellbeing interactions.