UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Talking to an AI mirror : designing self-clone chatbots for enhanced engagement in digital mental health support Shirvani, Mehrnoosh Sadat

Abstract

As mental health support becomes increasingly digitized, chatbots have emerged as promising tools for immediate, cost-effective remedies. These 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 aimed to design, implement, and evaluate novel AI-driven self-clone chatbots replicating users’ support strategies and conversational patterns to improve therapeutic engagement through externalized meaningful self-conversation. To ensure the therapeutic safety and effectiveness of self-clone chatbots, we analyzed insights from 16 expert interviews with mental health professionals to identify key design considerations that informed our final chatbot principles. Validated through a controlled experiment (N=180), significantly higher emotional and cognitive engagement was demonstrated with self-clone chatbots than a chatbot with a generic counselor persona. This finding was consistent in a follow-up study with a subgroup of participants (N=66) conducted after a 10-week period to mitigate any novelty effects. Our findings highlight self-clone believability as a mediator and emphasize the balance required in maintaining convincing self-representation while creating positive interactions. This work contributes to AI-based mental health interventions by introducing and evaluating self-clones as a promising approach to increasing the efficacy of conversational agents while offering insights into their design and exploring implications for their application in mental health care.

Item Citations and Data

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International