Talking to an AI mirror : designing self-clone chatbots for enhanced engagement in digital mental health support
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Shirvani, Mehrnoosh Sadat
issued: 2025-04-23T15:06:02Z
issued: 2025
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: 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 clientsâ 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 usersâ 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.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/90722
note: Talking to an AI Mirror: Designing Self-Clone Chatbotsfor Enhanced Engagement in Digital Mental HealthSupportbyMehrnoosh Sadat ShirvaniBS in Electrical Engineering, Iran University of Science and Technology, 2020MS in Industrial Design, Iran University of Science and Technology, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORALSTUDIES(Computer Science)The University of British Columbia(Vancouver)April 2025© Mehrnoosh Sadat Shirvani, 2025The following individuals certify that they have read, and recommend to the Fac-ulty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Talking to an AI Mirror: Designing Self-Clone Chatbots for EnhancedEngagement in Digital Mental Health Supportsubmitted by Mehrnoosh Sadat Shirvani in partial fulfillment of the requirementsfor the degree of Master of Science in Computer Science.Examining Committee:Dongwook Yoon, Associate Professor, Computer Science, UBCSupervisorGiuseppe Carenini, Professor, Computer Science, UBCSupervisory Committee MemberiiAbstractAs mental health support becomes increasingly digitized, chatbots have emergedas promising tools for immediate, cost-effective remedies. These conversationalagents have the potential to deliver valuable therapeutic impact, but low user en-gagement remains a critical barrier hindering their efficacy. Existing therapeuticapproaches have leveraged clientsâ internal dialogues (e.g., journaling, talking toan empty chair) to enhance engagement through accountable, self-sourced sup-port. Inspired by these, we aimed to design, implement, and evaluate novel AI-driven self-clone chatbots replicating usersâ support strategies and conversationalpatterns to improve therapeutic engagement through externalized meaningful self-conversation. To ensure the therapeutic safety and effectiveness of self-clone chat-bots, we analyzed insights from 16 expert interviews with mental health profes-si…
has type: Thesis/Dissertation
date issued: 2025-05
is shown at: 10.14288/1.0448512
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Yoon, Dongwook
type: Text
identifier URI: http://hdl.handle.net/2429/90722