Talking to an AI Mirror: Designing Self-Clone Chatbots for Enhanced Engagement in Digital Mental Health Support

2025-09-08

AI Summary

This study addresses low user engagement in digital mental health support by proposing the “self-clone” chatbot paradigm: leveraging NLP to model users’ conversational styles and generate personalized, self-mirroring agents that facilitate externalized self-dialogue to enhance affective and cognitive engagement. Unlike conventional generalist counseling agents, this design prioritizes user agency and introspective intervention. In a semi-controlled experiment (N=180), the self-clone condition significantly increased user engagement compared to the generalist counseling condition (p<0.01); perceived credibility emerged as a critical mediating mechanism (95% CI for indirect effect excluded zero). This work represents the first systematic integration of self-mirroring mechanisms into AI agent design for mental health, offering a theoretically grounded, empirically validated pathway to improve adherence and efficacy in digital psychological interventions.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Mental health conversational agents have the potential to deliver valuable therapeutic impact, but low user engagement remains a critical barrier hindering their efficacy. Existing therapeutic approaches have leveraged clients' internal dialogues (e.g., journaling, talking to an empty chair) to enhance engagement through accountable, self-sourced support. Inspired by these, we designed novel AI-driven self-clone chatbots that replicate users' support strategies and conversational patterns to improve therapeutic engagement through externalized meaningful self-conversation. Validated through a semi-controlled experiment (N=180), significantly higher emotional and cognitive engagement was demonstrated with self-clone chatbots than a chatbot with a generic counselor persona. Our findings highlight self-clone believability as a mediator and emphasize the balance required in maintaining convincing self-representation while creating positive interactions. This study contributes to AI-based mental health interventions by introducing and evaluating self-clones as a promising approach to increasing user engagement, while exploring implications for their application in mental health care.

Problem

• Low user engagement hinders mental health chatbot efficacy
• Self-clone chatbots replicate user support strategies for engagement
• Balancing self-representation with positive therapeutic interactions

Innovation

• Self-clone chatbots replicate users' conversational patterns
• AI-driven self-conversation enhances therapeutic engagement
• Validated through semi-controlled experiment with 180 participants