Cloning the Self for Mental Well-Being: A Framework for Designing Safe and Therapeutic Self-Clone Chatbots

2026-01-21

AI Summary

This study addresses the risks posed by self-cloning chatbots in mental health applications—namely identity confusion, negative reinforcement, and ambiguous autonomy—and calls for a therapy-oriented safety design framework. Through qualitative thematic analysis of interviews with 16 mental health professionals and 6 users, integrated with psychotherapeutic theory and human-computer interaction principles, this work proposes the first systematic design framework tailored to self-cloning chatbots in mental health contexts. The framework centers on therapeutic goals, cloned persona, and interactional dynamics, prioritizing emotional and ethical safety. It identifies three key design dimensions that collectively establish an interdisciplinary foundation for developing emotionally attuned and ethically compliant self-cloning AI tools, thereby advancing their responsible deployment in mental health care.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

As digital tools increasingly mediate mental health care, self-clone chatbots can offer a uniquely novel approach to intra-personal exploration and self-derived support. Trained to replicate users'conversational patterns, self-clones allow users to talk to themselves through their digital replicas. Despite the promises, these systems may carry risks around identity confusion, negative reinforcement, and blurred user agency. Through interviews with 16 mental health professionals and 6 general users, we aim to uncover tensions and design opportunities in this emerging space to guide responsible self-clone design. Our analysis produces a design framework organized around three priorities: (1) defining goals and grounding the approach in existing therapeutic models, (2) design dimensions including the self-clone persona and user-clone relationship dynamics, and (3) considerations for minimizing potential emotional and ethical harms. This framework contributes an interdisciplinary foundation for designing self-clone chatbots as AI-mediated self-interaction tools that are emotionally and ethically attuned in mental health contexts.

Problem

• self-clone chatbots
• mental well-being
• identity confusion
• user agency
• ethical harms

Innovation

• self-clone chatbots
• mental health AI
• therapeutic design framework
• user agency
• ethical AI