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arXiv · 2509.06393

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

Mehrnoosh Sadat Shirvani·Jackie Liu·Thomas Chao·Suky Martinez·Laura Brandt·Ig-Jae Kim·Dongwook Yoon
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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.

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Mehrnoosh Sadat Shirvani, Jackie Liu, Thomas Chao, Suky Martinez, Laura Brandt, Ig-Jae Kim, Dongwook Yoon. 2025-09-08. Talking to an AI Mirror: Designing Self-Clone Chatbots for Enhanced Engagement in Digital Mental Health Support. https://arxiv.org/abs/2509.06393

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