Science, Faculty of
Computer Science, Department of
DSpace
UBCV
Shirvani, Mehrnoosh Sadat
Master of Science - MSc
University of British Columbia
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.
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 clients’ 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 users’ 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-sionals to identify key design considerations that informed our final chatbot prin-ciples. Validated through a controlled experiment (N=180), significantly higheremotional and cognitive engagement was demonstrated with self-clone chatbotsthan a chatbot with a generic counselor persona. This finding was consistent in afollow-up study with a subgroup of participants (N=66) conducted after a 10-weekperiod to mitigate any novelty effects. Our findings highlight self-clone believabil-ity as a mediator and emphasize the balance required in maintaining convincingself-representation while creating positive interactions. This work contributes toAI-based mental health interventions by introducing and evaluating self-clones asa promising approach to increasing the efficacy of conversational agents while of-fering insights into their design and exploring implications for their application inmental health care.iiiLay SummaryGenerative Artificial Intelligence (AI) technology is getting closer to realisticallymimicking how humans talk and act, allowing us to create ”self-clones,” whichare digital replicas of individuals that mirror their personality and behavior. Theseself-clones have the potential to offer new and unique ways of supporting mentalhealth, similar to talking to a mirror or journaling, but they also come with someunpredicted risks. To explore this, we first spoke to 16 experts, such as psychol-ogists, counselors, and mental health researchers. With their insight and previousresearch, we used AI to develop a self-clone chatbot and tested it with 180 partici-pants. The results showed that people were more engaged with the self-clone thana typical mental health chatbot, addressing a common issue with digital mentalhealth tools. This suggests that self-clones could be a valuable option for mentalhealth care in the future as long as they are designed with care.ivPrefaceThis thesis is the original intellectual work of the author, Mehrnoosh Sadat (Merry)Shirvani. It represents a merger of two working papers. All studies described in thisthesis were conducted with the approval of the UBC Behavioral Research EthicsBoard (certificate number #H23-00825).This work was conducted in collaboration with research associates under thesupervision of Prof. Dongwook Yoon, who provided primary support in guidingthe project, refining the study design, and enhancing the writing. Dr. Ig Jae Kim,from the Korea Institute of Science and Technology (KIST), conceptualized theself-clone framework. Prof. Laura Brandt from The City College of New York,Prof. Suky Martinez from The Johns Hopkins University School of Medicine,and Dr. Thomas Chao from the University of British Columbia (UBC) providedcritical consultation and invaluable guidance in shaping the direction of this work.Jackie Liu (UBC graduate research assistant) offered ongoing support and feedbackthroughout the project and contributed to the analysis of expert interviews andGPT prompt design. Cher Peng (UBC undergraduate research assistant) assistedin participant recruitment and conducted the second half of the expert interviewswith the preliminary deductive analysis of the relevant data. All presented studiesand analyses were designed, implemented, and led by Mehrnoosh Sadat Shirvani.Generative artificial intelligence was not used beyond its inclusion in the researchcomponent for the development of AI chatbots and was not involved in the creationof any final materials, including but not limited to thesis and supporting documents.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1 The Role of Self in Mental Health . . . . . . . . . . . . . . . . . 52.2 AI Clones and Digital Twins . . . . . . . . . . . . . . . . . . . . 62.3 AI-Driven Mental Health Interventions . . . . . . . . . . . . . . . 72.4 User Engagement in Digital Mental Health Tools . . . . . . . . . 83 Formative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103.1.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . 10vi3.1.2 Data Collection and Analysis . . . . . . . . . . . . . . . . 113.2 Design Framework: Dimensions . . . . . . . . . . . . . . . . . . 133.2.1 Therapeutic Goals and Approach . . . . . . . . . . . . . . 133.2.2 Self-Clone’s Persona . . . . . . . . . . . . . . . . . . . . 173.2.3 User-Clone Relationship and Language Use . . . . . . . . 193.2.4 Balancing Self-Portrayal’s Adaptability and Consistency . 203.2.5 Provider Involvement . . . . . . . . . . . . . . . . . . . . 213.3 Design Framework: Considerations . . . . . . . . . . . . . . . . 223.3.1 Targeted User Groups Considerations . . . . . . . . . . . 233.3.2 Constraints in Creating the Perfect Replica . . . . . . . . 233.3.3 Risks and Safety . . . . . . . . . . . . . . . . . . . . . . 243.3.4 Ethics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Designing Self-Clone Chatbots . . . . . . . . . . . . . . . . . . . . . 284.1 Design Principles . . . . . . . . . . . . . . . . . . . . . . . . . . 284.2 Design Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Prompt Design . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Evaluating Self-Clone Chatbots . . . . . . . . . . . . . . . . . . . . . 365.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.1.1 Experiment Procedure . . . . . . . . . . . . . . . . . . . 365.1.2 Evaluation Instruments and Measures . . . . . . . . . . . 375.1.3 Participants and Recruitment . . . . . . . . . . . . . . . . 395.1.4 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . 405.1.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . 425.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.2.1 The perceived believability of the self-clone is not uni-modal but instead consists of two distinct subpopulations. 435.2.2 Engagement with self-clones is positively correlated withparticipants’ perceived believability of the clone. . . . . . 44vii5.2.3 The self-clone chatbots (SCX and SCS) resulted in signif-icantly higher engagement compared to the baseline in thehigh believability group, with SCS showing a trend towardfurther increased engagement relative to SCX . . . . . . . 465.2.4 Consistent follow-up results suggest minimal influence ofnovelty effect on initial findings. . . . . . . . . . . . . . . 475.2.5 Perceived motivation and acceptance were strongly corre-lated with engagement and exhibited similar improved pat-terns in both self-clone conditions compared to baseline. . 485.2.6 AI awareness shapes user perception and engagement withchatbots, varying by individual AI literacy and attitude to-ward AI . . . . . . . . . . . . . . . . . . . . . . . . . . . 496 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516.1 Design Implications . . . . . . . . . . . . . . . . . . . . . . . . . 516.2 Ethics and Safety Considerations . . . . . . . . . . . . . . . . . . 546.3 Self-Clones beyond Mental Health . . . . . . . . . . . . . . . . . 556.4 Limitation and Future Work . . . . . . . . . . . . . . . . . . . . 567 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58A Supporting Materials for Formative Study . . . . . . . . . . . . . . 73A.1 EXPLANATION OF ABBREVIATIONS USED IN TABLE 3.1 . 73A.2 Resulting Concepts for Self-Clones - Three Examples . . . . . . . 74A.3 Thematic Analysis and Affinity Diagram . . . . . . . . . . . . . . 75B Supporting Materials for the Evaluation Study . . . . . . . . . . . . 78B.1 DESIGN SCENARIOS . . . . . . . . . . . . . . . . . . . . . . . 78B.2 ENGAGEMENT QUESTIONNAIRES . . . . . . . . . . . . . . 78B.2.1 TWente Engagement with EHealth and Technologies Scale(TWEETS) without the behavioral items . . . . . . . . . 78B.2.2 User Engagement Scale (UES) . . . . . . . . . . . . . . . 80viiiB.3 MOTIVATION AND ACCEPTANCE QUESTIONNAIRES . . . 81B.3.1 Motivation - Adapted Client Motivation for Therapy Scale(CMOTS) . . . . . . . . . . . . . . . . . . . . . . . . . . 81B.3.2 Acceptance - Adapted The Unified Theory of Acceptanceand Use of Technology (UTAUT) . . . . . . . . . . . . . 81B.4 PROMPTS FOR AI SYSTEMS IN THE IMPLEMENTATION . . 82B.4.1 ’A Friend In Need’ Chatbot . . . . . . . . . . . . . . . . 82B.4.2 Social Support Prompting - Analysis . . . . . . . . . . . . 83B.4.3 Main Chatbot - Baseline . . . . . . . . . . . . . . . . . . 84B.4.4 Main Chatbot - Self-Clone without SSP . . . . . . . . . . 84B.4.5 Main Chatbot - Self-Clone with SSP . . . . . . . . . . . . 85B.5 THE STUDY WEBSITE . . . . . . . . . . . . . . . . . . . . . . 87B.6 RESULTS FROM THE EVALUATION STUDY . . . . . . . . . 87B.6.1 Behavioral Engagement Report . . . . . . . . . . . . . . 87B.6.2 Correlation Matrices . . . . . . . . . . . . . . . . . . . . 89B.6.3 Engagement Scores Across Conditions . . . . . . . . . . 90ixList of TablesTable 3.1 Summary of participant demographics and specialization (SeeAppendix A.1 for an explanation of abbreviations) . . . . . . . 12Table 3.2 Summary of Design Framework Components . . . . . . . . . . 14Table 4.1 Examples of conversation with our self-clones, two represen-tative examples sampled from the chatlogs of our participants.P155 from the SCX condition on the left and P26 from the SCScondition on the right side. Identifiable information has beenreplaced with placeholders. . . . . . . . . . . . . . . . . . . . 35Table B.1 Number of messages and words per message across conditionsand ’A Friend in Need’ chatbot: Mean and standard deviation . 87Table B.2 Time spent per message and total time across conditions and ’AFriend in Need’ chatbot: Mean and standard deviation . . . . . 87Table B.3 Correlation Matrix (N=180) - Significance shown as ** for p<0.001 89Table B.4 Correlation Matrix for All Samples in Baseline and High Be-lievability Self-Clones (N=124) - Significance shown as ** forp<0.001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90xList of FiguresFigure 5.1 Comparison of user interaction and training process across thethree conditions . . . . . . . . . . . . . . . . . . . . . . . . . 38Figure 5.2 The distribution of the perceived believability scores is shownas a bar graph (left axis) alongside the mixture model repre-sented by density curves (right axis). . . . . . . . . . . . . . . 43Figure 5.3 Positive correlation between Perceived Believability Score andEngagement Score across Self-Clone without SSP (red) andSelf-Clone with SSP (blue) conditions, with shaded 95%con-fidence intervals. To address overplotting, jitter was added tothe data points. . . . . . . . . . . . . . . . . . . . . . . . . . 45Figure 5.4 Boxplots illustrating the engagement scores, including com-bined, cognitive, and emotional dimensions, across baselineand high-believability self-clone conditions. The significancecodes are ** for p < 0.01, * for p < 0.05. . . . . . . . . . . . 47Figure A.1 Self-Clone Chatbot Concept 1 - Mind Shift . . . . . . . . . . 74Figure A.2 Self-Clone Chatbot Concept 2 - Future You . . . . . . . . . . 75Figure A.3 Self-Clone Chatbot Concept 3 - Inner Coach . . . . . . . . . 75Figure A.4 Initial Thematic Analysis of Expert Interviews . . . . . . . . 76Figure A.5 Design Dimensions and Considerations Affinity Diagram . . . 77Figure B.1 Design Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 79Figure B.2 Screenshot of the study website . . . . . . . . . . . . . . . . 88xiFigure B.3 Boxplots illustrating the engagement scores, including com-bined, cognitive, and emotional dimensions, across all conditions 91xiiAcknowledgmentsFirst and foremost, I would like to thank my supervisor, Dongwook, for his guid-ance, mentorship, and support, which have been truly invaluable in this journeyand to my growth both as a researcher and as a person; I am ever grateful for hisbelief in me and the opportunity to learn to become a better designer under hismentorship. A big thank you to Jackie for being there every step of the way andmaking this journey all the more rewarding. I would like to extend my heartfeltthanks to all my collaborators, Thomas, Suky, and Laura, for their many feedbackand patient guidance throughout the project. Ig-Jae, for providing the initial con-cept for self-clones. And lastly, Cher, for her invaluable work, which helped uselevate the impact of our work further. I would also like to sincerely thank Prof.Giuseppe Carenini for his review and comments on refining this thesis.I am grateful for the support of the KIST, the Natural Sciences and EngineeringResearch Council of Canada (Discovery Grant; CREATE), and the UBC LanguageSciences Institute, which made this research possible.My academic path would not have been the same without all the amazing peo-ple I met along the way. I am thankful for my time at UBC and DFP, and I wouldlike to thank the members of the SOCIUS Lab (formerly D-Lab) and MUX Lab atUBC for their feedback. A special thanks to my friend, Zhe, for making unforget-table memories together. I cannot express enough gratitude to my dearest friends,Sepideh, Mahboobe, and Saba, for being my unwavering source of strength.Lastly, to my parents and brother, thank you for the endless love, encourage-ment, and support you have given me in following my dreams. And to my partner,Iman, you are my light and my rock. Thank you for believing in me and for stand-ing by my side with patience as I found my way.xiiiDedicationTo Iman.xivChapter 1IntroductionMental health issues are a pressing global concern, affecting millions of lives andsignificantly impacting overall well-being and quality of life [83, 117]. Despitethe high prevalence, access to adequate mental healthcare remains challenging dueto resource constraints and fragmented service delivery [2, 49]. In response, digi-tal solutions, particularly chatbot-based interventions, have emerged as promisingtools for providing accessible mental health support [3]. In the domain of human-computer interaction (HCI), elements of established therapeutic methods such ascognitive behavioral therapy (CBT) and mindfulness have been integrated into Ar-tificial Intelligence (AI)-driven conversational agents to offer mental and emotionalsupport, such as MindfulDiary [53] and Sunny [67]. The role and potential ofthese digital tools for mental health care are becoming increasingly recognized bynational initiatives like the World Health Organization (WHO) [95, 118].Effective user engagement—the extent to which users actively interact withand adhere to digital mental health interventions—is essential for maximizing theirtherapeutic potential. Engagement plays a crucial role in enabling interventions todeliver their full impact by fostering sustained participation, which is necessary formeaningful psychological change [37, 102, 115]. While engagement alone doesnot guarantee improved mental health outcomes, it serves as a critical pathwaythrough which therapeutic strategies can be effectively implemented. Factors suchas user motivation, symptom severity, and intervention design all influence engage-ment levels. As mental health challenges continue to grow—exacerbated by events1like the COVID-19 pandemic [58]—there is an urgent need to refine digital tools tobetter support engagement, ensuring that users remain actively involved and benefitfully from the interventions.One promising avenue for enhancing engagement is drawing inspiration fromself-centered therapeutic practices. Carl Rogers’ person-centered therapy positsthat individuals have an inherent drive and inner capacity for self-healing [9]. Anindividual’s internal dialogue can be either a solid positive source to foster this self-healing or a negative influence at the root of the mental health issues [42, 57, 101].Various therapeutic interventions leverage positive self-dialogue to promote emo-tional resilience and behavior change. Techniques like Gestalt therapy’s emptychair exercise, where clients engage in dialogue with imagined figures or internalparts of themselves, and reflective journaling encourage self-reflection and per-sonal growth [75, 76]. Utilizing self-talk to enhance self-driven accountability andmotivation in the healing process can improve engagement; however, integratingthese into digital tools calls for careful design, as subtle differences in framing caneither enhance emotional healing or exacerbate distress. For instance, while exter-nalizing negative self-talk can be a tool for facilitating self-awareness and encour-aging a constructive mindset, it may also evoke negative emotions if not carefullymanaged.The rise of generative AI has introduced new possibilities for personalizedmental health support, particularly through AI companions. However, many cur-rent applications fail to fully leverage AI’s potential, often digitizing traditionalpractices rather than reimagining them. An emerging trend in Human-ComputerInteraction is the concept of clones, which utilize personal data to create digitalreplicas of individuals [59, 61, 92]. This concept holds significant implications forintrospection and self-sourced emotional support. For instance, in 2022, a Twitterartist, Michelle Huang, demonstrated this potential by prompting GPT-3 with thecontent of her childhood diaries to create a conversational ”time portal” with heryounger self, reporting experiencing unexpectedly emotional healing [63].Motivated by these recent developments, we aimed to design self-clone chat-bots, which enable the externalization of inner dialogues and explore their potentialto enhance user engagement with AI-based mental health chatbots, thereby foster-ing an environment necessary for the optimal delivery of mental health support.2Our study is guided by the following research questions (RQs):• RQ1: How can we design a self-clone chatbot to engage users effectively?• RQ2: To what extent can the self-clone persona help engagement levels?• RQ3: How do users perceive self-clones, and what factors shape the experi-ence?Through an exploratory interview study with 16 mental health experts, includ-ing psychologists, psychiatrists, counselors, and researchers, alongside drawingfrom existing human-AI design guidelines and evidence on common engagementbarriers, we established three design principles for the self-clone chatbots to guideour process: 1) counseling goal and approach, 2) persona, and 3) selective traitrepresentation. We brainstormed with our interdisciplinary team, including threemembers with a clinical psychology background, and refined ten design scenariosfor potential theoretical framing of the self-clones, ultimately selecting the mostpromising one for implementation and evaluation. Based on our design principles,our chatbot is informed by the empty chair technique[9], utilizing a future-self per-sona, and focuses on recreating conversational and support patterns. For data col-lection, we developed a novel approach using a hypothetical chatbot that simulateshow users would support a friend in need, allowing us to capture users’ natural so-cial support strategies and behavioral patterns. The captured support behavior wasincorporated into our self-clones through our new prompting technique, social sup-port prompting (SSP). Informed by psychological theories on types of supportingstrategies, SSP shows promise to improve the model’s ability to accurately identifyand replicate specific social support patterns observed in the data.To evaluate the impact of our self-clone chatbot designs on user engagement,we conducted a semi-controlled experiment with 180 participants, randomly as-signed across three conditions: baseline (BL), self-clone without SSP (SCX), andself-clone with SSP (SCS). SCX was designed to replicate a user’s behavioral andconversational style, such as tone and word choice, while SCS, utilizing SSP, alsoincorporated their preferred supportive strategies. Statistical analysis of the datashowed significant enhancement in cognitive and emotional engagement compared3to the baseline. Notably, SCS demonstrated significantly higher emotional engage-ment toward the baseline than SCX. Moreover, self-clone believability emergedas an important mediating factor. Additionally, thematic analysis of participants’comments revealed that this believability was likely influenced by participants’perceptions toward themselves and the inherent limitations of the ’talking acrosstimelines’ concept (i.e., talking to the future self). Further statistical and thematicanalyses indicated generally positive perceptions, including higher motivation andacceptance among participants.Alongside the insights from our expert interview study, our work contributes:1) the design of self-clone chatbots as a novel AI-driven approach, along with SSPas a promising technique to enhance the capabilities of self-clones in recreatinguser support strategies, 2) evidence of improved user engagement, and 3) insightsinto users’ perceptions of the self-clone chatbots alongside the implications forfuture applications.4Chapter 2Related Works2.1 The Role of Self in Mental HealthIn psychology, the concept of self is not merely an abstract notion but a funda-mental element in therapeutic interventions. Each individual constructs a personalnarrative based on their beliefs, experiences, and perceptions, which collectivelyshape their identity [82]. This self-narrative plays a critical role in mental well-being, serving as the lens through which individuals interpret their experiences andinteract with the world [42, 69]. A key aspect of this narrative is self-talk or in-ner dialogue — a form of self-directed verbal expression unique to each individual[15, 48, 82]. While positive self-talk can foster motivation, resilience, and emo-tional regulation, dysfunctional self-talk is often present in psychological disorderssuch as depression, anxiety, and personality disorders [101].Addressing self-talk in a therapeutic setting can help reconstruct personal nar-ratives more positively and foster resilience. Many therapeutic approaches aimto improve self-perception by enhancing the quality of self-talk. For example,a key component of Carl Rogers’ person-centered therapy is to shift inner dia-logues toward a more accepting form, aligning the real self (who one actually is)with the ideal self (who one wants to be) [84, 107]. Other therapeutic techniques,such as Internal Family Systems (IFS), Cognitive Behavioral Therapy (CBT), andCompassion-Focused Therapy (CFT), also leverage self-talk to increase self-compassion,address distorted self-perceptions, and resolve internal conflicts [16, 29, 41]. Re-5search shows that creating psychological distance from one’s inner thoughts—self-distancing—can enhance self-awareness and improve emotional regulation [36,54]. While externalizing self-talk through techniques like journaling and expres-sive writing can promote distancing from negative internal “loops”, these methodsare often self-guided and non-interactive, highlighting an opportunity for more dy-namic approaches to facilitate this process [72, 87].2.2 AI Clones and Digital TwinsWith the recent advancement in AI, there is an emerging trend of human digitalreplicas, often called digital twins or clones. From deepfakes to voice-clone, therecreation of famous personalities through mimicking visual or audio likenesseshas become popular in online communities [86, 112]. Using generative AI, plat-forms such as character.ai have emerged to offer access to beyond pre-programmedchatbots of famous individuals, enabling users to create their own clones of differ-ent people [19]. Going beyond entertainment, there has also been a rise in taskdelegation clones, with platforms such as personal.ai or fraia.ai offering to createclones of users to perform the role of real-world counterparts without real-worldcosts [33, 79].Although the term digital twin has recently been adapted to refer to digitalclones in online settings, the concept originated in industrial engineering as a dig-ital counterpart designed to mimic real-world entities in a virtual environment,primarily for optimizing complex control systems [51]. Today, however, the useof digital twins has expanded across many domains. In the health sector, digitaltwins of patients, created from health and biology data and symptoms, are usedfor preventive measures, such as illness identification, personalized diagnoses, andtreatments [98]. In marketing, Truby and Brown [104] developed what they calleda human digital thought clone, using customer data to replicate consumer thoughtprocesses and decision-making for profit maximization. Within HCI, McIlroy-Young et al. [61] introduced the concept of mimetic models, which use personaldata to train AI systems that mimic an individual. Similarly, Lee et al. [59] defineAI clones as interactive digital representations of individuals, trained on personaldata, that replicate certain traits or behaviors of the person. Our study expands upon6the existing research in the field of AI clones by examining potential use cases forthese clones when utilized by the original individual. We refer to these clones asself-clones.Although AI clones have not been explicitly studied for emotional support,there are relevant cases that suggest their potential. For instance, Replika offers anAI companion that can be personalized to users’ preferences, with some individu-als using this feature to create clones of real-world people they know, substitutingfor lack of real-world relationships, and many have reported finding comfort andemotional support through these interactions [24, 60]. Slater et al. [92] exploredthe therapeutic implications of conversing with a static self-avatar in virtual real-ity environments, finding that simulated self-conversations led to greater perceivedhelpfulness. Similarly, our study focuses on the implications of using a digitalrepresentation of oneself for mental health benefits. However, we specifically ex-plore using AI to enable interactivity, simulating natural, human-like conversationsthrough a chatbot.2.3 AI-Driven Mental Health InterventionsDigital mental health interventions (DMHIs) refer to the use of technology-basedtools and digital platforms to deliver mental health support, which can take variousforms, such as mobile applications, web-based programs, or video games [73]. Thegrowing demand for accessible mental health care, coupled with recent advance-ments in AI capabilities, has the potential to transform the development of DMHIs.For example, AI-driven chatbots have emerged as a promising tool for screeningand symptom management [12]. Conversational agents like Woebot [31], Wysa[45], and Tess [34] have gained popularity for managing mild to moderate symp-toms, demonstrating the therapeutic potential of these tools [18].DMHIs are closely integrated with digital systems, making HCI principlesvaluable for designing safe and effective mental health tools [8]. Recent researchon designing AI-driven chatbots emphasizes the importance of a consistent chatbotpersonality, transparency, designing for dialog failures, and fostering human-likeconversations [46]. Additionally, guidelines for human-AI interactions (HAI) high-light the need to account for potential biases and align with relevant social norms7based on the user’s context [7]. Our study incorporates these design practices andguidelines to create a thoughtful, user-centered approach to developing AI-drivenmental health tools.2.4 User Engagement in Digital Mental Health ToolsUser engagement (also referred to as client engagement) is a critical and frequentlystudied factor in digital mental health interventions, as higher levels of engagementare strongly associated with greater therapeutic effectiveness [37, 120]. Althoughengagement is not directly linked to improved mental health, without sufficientclient engagement, intervention outcomes can be limited, leading to minimal orinconsistent outcomes. Engagement is a complex, multi-dimensional process, andwhile there is not yet a universally accepted definition, it is commonly categorizedinto three main components: behavioral, cognitive, and emotional engagement.Improvements in any of these areas are thought to enhance overall engagement[10]. Behavioral engagement is typically characterized by intervention adherenceand clients’ active efforts while using the tool [77]. In HCI research, it is oftenmeasured through objective metrics such as number of clicks, time spent per task,or frequency of use [68]. Cognitive engagement refers to the degree to whichclients agree with the therapeutic approach, meaning how suitable they perceive theintervention to be in helping them reach their goals [120]. Emotional engagement,or affective engagement, reflects the user’s subjective experience with the tool,including interest, emotional connection, and overall satisfaction [77]. A review ofrecent studies reveals that most research in the HCI and DMHI domains employsa combination of adapted self-report or observer-rated questionnaires alongsideobjective measures, which aligns with the recommendation to use mixed methodsto comprehensively assess the complexity of engagement in relation to specificstudy contexts [78].User engagement can be influenced by a variety of factors. Common contrib-utors to low engagement include a lack of motivation to persist with self-guidedinterventions, poor usability and user experience, and a misalignment between theprovided support and the user’s needs [102, 115]. Other barriers include user-related factors (e.g., demographic variables, beliefs, prior experience, and skills),8program-related factors (e.g., perceived usefulness and fit), and technology- andenvironment-related factors (e.g., privacy concerns and technical issues) [11]. Stud-ies have shown that HCI practices, including user-centric design, persuasive designpractices, gamification, and external motivators (e.g., reminders), can improve en-gagement [17, 114]. However, these approaches often fail to address the root is-sue—the one-size-fits-all therapeutic approach commonly used in digital interven-tions [44, 66]. This challenge, along with the aforementioned barriers, highlightsthe need for a more adaptive and personalized approach to mental health support,which would leverage individual differences to be better tailored to meet the spe-cific traits and needs of users.9Chapter 3Formative StudyTo investigate the potential of self-clone chatbots in the field of mental health,we conducted a need-finding study that involved expert interviews as our primaryresearch method. Acknowledging the exploratory and interdisciplinary nature ofthis phase, we engaged with mental health professionals to obtain essential insightsand establish foundational design considerations within this domain.3.1 Method3.1.1 ParticipantsOur study enlisted the participation of 16 mental health professionals. Our inclu-sion criteria required the possession of a higher degree in psychology or relatedfields and a minimum of two years of experience in mental health research or clin-ical practice. We employed the snowball sampling technique, starting from thepersonal social networks of the research team, and extending to digital channelsthat include social media and university newsletters. From the list of volunteers,we used purposeful sampling to optimize not only diversity but also the overalldepth and breadth of experiences our experts have in the domain. With an averageage of 37.37 years (SD = 10.69) and an equal distribution of women and men, theparticipants had a mean of 6.75 years (SD = 6.95) of research experience in mentalhealth and an average of 9.12 years (SD = 7.49) of clinical experience. For details,10please refer to Table 3.1. All participants were compensated $100 for their time.We obtained demographic information via an online survey prior to the inter-view sessions. Subsequently, further data were collected, including participants’familiarity with and perspectives on existing mental health chatbots. Apart fromone participant (P2) who had previous research experience in AI-related fields, therest of the respondents reported limited or no prior experience with AI tools. De-spite this, all participants indicated a general understanding of the concept; theparticipants were aware of the existence of mental health chatbots; however, nonehad any personal use-case experience with them. Nonetheless, the participants re-ported a neutral or optimistic viewpoint regarding the potential utility of such toolsin the provision of mental health support.3.1.2 Data Collection and AnalysisFor data collection, we utilized semi-structured interviews on Zoom, lasting aroundone hour, to give the flexibility to both the participants and the interviewer to ex-pand on relevant points and views as needed. For data analysis, we used inductiveand deductive thematic analysis [13] informed by prevailing knowledge on chatbotdesign. The initial open coding was followed by inductive iterative axial codinginvolving an online whiteboard, leading us to compare groupings with interviewtranscripts and deductively against categories deriving from therapeutic techniquesand design elements. Consequently, relationships between codes yielded over 160codes like critical moments and trust building. These were eventually consolidatedinto 27 conceptual categories defining the core design dimensions and considera-tions detailed in this thesis. The main analysis was performed by the author andrefined in collaboration with the research team. Finally, the themes were assessedfor accuracy against the data and existing literature for novelty. The research team,recognizing the lack of expertise in chatbot design among participants, activelyprojected the experts’ opinions to existing bodies of knowledge in Design andHuman-Computer Interaction literature, especially those related to chatbot design,to distill their ideas into design considerations.After analysis and writing, the manuscript underwent member checking, allow-ing our experts to review their quotations and adjust or withdraw them as desired.11ResearchClinicalP130ManEast AsianClinical AssessorCPA, BCPA, BPSPsyD in Clinical PsychologyACT86P233ManWhitePsychiatrist, Clinical Reseach FellowFRCPCMD, PhDCancer Psychiatry, Mood Disorders55P333WomanSouth AisanClinical Supervisor, Psychologist, ProfessorRCCPsyDYoung Adults54P450WomanWhiteCollege Counseling Center DirectorAPAPhD in Clinical PsychologyTrauma2018P538WomanMultipleAssistant ProfessorAPAPhD in General PsychologyHealth Psychology, Women's Health1010P633ManSouth AisanAssistant ProfessorAARC, ACAPhD in Counselor EducationYouth, Adolescents, and Families 35P739ManMiddle EasternPsychiatrist, PsychotherapistIPAPostdocPsychodynamic Psychotherapy210P836WomanEast AsianPrivate PractitionerIPAPhD in General PsychologyCBT, ACT45P929WomanSouth AsianClinical CounsellorBCACCMS in Counselling Psychology General Counselling52P1027WomanMixedCounsellor & Art TherapistBCACC, CATAMS in Counselling PsychologyEFT, Narrative Therapy, Art Therapy 22P1164ManCaucasianClinical CounsellorRCCM.Ed in Counselling PsychologyMindfulness, Somatic Therapy2424P1231ManN/A Private PractitionerCRPOM.Ed in Counselling PsychologyOCD, ADHD08P1355WomanCaucasianPrivate PractitionerBCACCM.A. in Counselling PsychologyTrauma, Crisis Counseling 615P1426ManEuropeanAssociate ClinicianBCACCM.Sc. in Clinical PsychologyADHD, Interpersonal, Immigration-related Anxiety, Existential13P1531ManEast AsianClinical CounsellorBCACCMS in Counselling Psychology ADHD04P1643WomanBosnianPrivate Practitioner, Collage Department HeadBCCSW, BCACCMS in Social Work, PhD CandidateChild and Adolescent, Trauma, Eating and Personality Disorders1325GenderAgeIDExperience (Years)Areas of expertiseHighest EducationAssociation(s)Current OccupationEthnicityTable3.1:Summaryofparticipantdemographicsandspecialization(SeeAppendixA.1foranexplanationofabbrevi-ations)12Derived from the data, we present our proposed framework (Table 3.2) throughtwo overarching lenses: design dimensions and design considerations. We de-fine design dimensions as scalable or variable aspects that guide the overall de-sign, highlighting key possibilities and trade-offs for designers to decide upon.Conversely, design considerations refer to context-specific factors—such as con-straints, requirements, or impacts—that shape the design process but lie beyonddirect designer control. Together, these elements provide a structured frameworkfor understanding the complexities of self-clone design in mental health contexts.3.2 Design Framework: DimensionsDesign dimensions define the key variables that shape self-clone chatbot design,offering designers a model to navigate trade-offs and possibilities. These dimen-sions—spanning therapeutic goals, agent persona, user-clone dynamics, adaptabil-ity, and provider involvement—outline the structural choices that influence func-tionality and user experience. By identifying these core aspects, we provide afoundation for designing flexible, effective, and contextually relevant self-clonechatbots.3.2.1 Therapeutic Goals and ApproachDesigning self-clone chatbots for mental health requires a solid psychological foun-dation to ensure they can provide meaningful support. Although self-clones canserve a variety of therapeutic goals and models, they may be particularly bettersuited for certain approaches. This section explores key areas where self-clonescan offer valuable assistance and examines how established psychological frame-works can inform their implementation, ensuring both credibility and effectiveness.Therapeutic Goals Designing a self-clone chatbot for mental health should be-gin by defining its primary objectives and intended outcomes. These goals mayrange from addressing specific concerns—such as symptoms of anxiety or de-pression—to providing more generalized support for emotional regulation, self-reflection, or day-to-day challenges. While a self-clone cannot— and should not—replace a qualified mental health professional, experts agree that it holds consider-able potential to complement existing support systems and foster personal growth.13DescriptionExampleDimensionsTherapeutic Goals and ApproachThe theoretical foundations and traditional therapeutic practices that shape the Self-Clone’s supportive approach.Techniques like narrative therapy, role-playing, or the empty chair method can help the Self-Clone provide new perspectives.Self-Clone's PersonaThe distinctive behavioral characteristics of a SC that extend beyond mere user portrayal.A SC with a past persona can act as one's younger self, or by tweaking specific traits, user can create and explore their ideal-self.User-Clone Relationship and Language UseElements that shape interactions and influence how the user perceives their relationship with the SC.Using “we” and “us” instead of “you” vs. “I” can foster a greater sense of support and companionship.Balancing Self-Portrayal's Adaptability and ConsistencyManaging the trade-off between maintaining a stable SC identity and adapting to the user’s evolving self-perception.As a user’s self-awareness grows, their self-perception may shift. If SC doesn’t reflect this change, it may lead to distrust.Provider InvolvementThe extent of mental health professional supervision in the process.Self-directed care allows for a more personal and private experience, but professional oversight may be necessary in high-risk situations.ConsiderationsTargeted User GroupsThe user demographics and characteristics that influence the experience and adoption of SC.Users with prior negative AI experience may have biases toward SC, while younger generations may be more open to adopting it.Constraints in Creating the Perfect ReplicaLimitations in technology and the complexity of selfhood, making a perfect copy neither possible nor advisable.Human personality, emotions, and core beliefs are deeply intricate and cannot be easily extracted, replicated, or fully captured.Risks and SafetyPotential risks and safety concerns related to externalizing self-talk, along which requires thoughful safeguards.Failing to prevent replication negative thought patterns may create a reinforcing loop, leading to a downward spiral of negativity.EthicsHigh level ethical concerns surronding identity-based design and implications for morally gray areas. Users may perceive SC’s advice as their own decisions, blurring the line between personal agency and accountability.Table3.2:SummaryofDesignFrameworkComponents14A key advantage of self-clones lies in their ability to externalize self-talk. Manyexperts emphasized that self-clones can help users distance themselves from imme-diate reactions, demonstrate healthier internal dialogue (P10), and stimulate newperspectives through externalized observation (P11, P15). P2 highlighted the em-powering nature of drawing reassurance from one’s own self-portrayal, suggestingthat individuals who typically seek external validation could “achieve that reassur-ance from themselves” instead.Furthermore, talking to a self through a clone can create a safe, consequence-free environment for self-expression (P11). P8 added that self-clones may be par-ticularly helpful for those who “mistrust another supportive voice,” enabling themto reduce negative thought patterns and develop more constructive self-talk grad-ually. As P5 noted, effective self-talk “can serve as a reminder to be compas-sionate to oneself,” making the self-clone a readily accessible tool for everydayself-improvement.Some experts also underscored the potential for self-clones to address inter-personal challenges by encouraging users to see themselves from another’s per-spective. For instance, P16 envisioned employing a self-clone to facilitate self-awareness during conflict resolution, observing that users “can maybe put them-selves in the shoes of their parents or their [partner]... dealing [with them].” Simi-larly, P4 endorsed the self-clone’s capacity to “think about ways of seeing scenar-ios from a new perspective,” opening possibilities for enhanced understanding andempathy in personal relationships.Therapeutic Interventions and Methods Despite the potential of AI-drivenmental health tools, experts highlighted that skepticism remains among profession-als regarding their clinical validity. To build trust and set self-clones up for suc-cess, their design should be firmly rooted in evidence-based therapeutic principles.While various approaches can inform their development, self-clones are particu-larly well-suited for narrative and self-concept-based therapies, encouraging usersto externalize and explore different aspects of themselves. This section examinessome of the most promising therapeutic approaches for integrating self-clones ef-fectively. Importantly, self-clones should not merely replicate traditional therapytools but instead capitalize on their unique strengths—such as self-externalizationand adaptive role-playing—to create a more immersive and impactful therapeutic15experience.Many experts identified Internal Family Systems (IFS) [88] and parts work asespecially promising. These models inherently involve “talking. . . as yourself toyourself or to parts of yourself” (P10), which aligns naturally with the notion ofa self-clone. In practice, the chatbot could assume roles such as the inner critic,inner coach, or younger self, helping users integrate disparate aspects of their psy-che. P11 asked, “How does your inner coach respond to. . . your inner critic?”—aquestion that can be explored through targeted self-clone interactions. P12 addedthat IFS can be applied in many therapies: “People do. . . parts work in differentlanguages,” making it adaptable to various settings and populations. Likewise, nar-rative therapy may benefit from a self-clone’s ability to encourage reframing andrewriting personal stories, facilitating deeper insight and problem externalization.Several participants noted the self-clone’s potential in Emotion-Focused Ther-apy (EFT), Compassion-Focused Therapy (CFT), and Acceptance and Commit-ment Therapy (ACT). In CFT, a self-clone can help users “prove. . . [they] canhave. . . the capacity for self-compassion” (P1). EFT techniques, which emphasizerecognizing and expressing emotion, could similarly benefit from a chatbot thatresponds to users’ emotional states in real-time. Regarding ACT, P15 suggestedthat having a self-clone “script. . . or queue” could reinforce acceptance skills andcognitive defusion, allowing users to separate their thoughts from their identity.Cognitive Behavioral Therapy (CBT) remains one of the most widely rec-ognized modalities, and several experts saw the potential for incorporating basicCBT strategies into self-clone interactions. For instance, a chatbot could guideusers in identifying “cognitive distortions” (P3) or encourage them to “challengethemselves” (P2) through structured self-talk. However, as P14 cautioned, CBT’sscripted, session-based structure might not always translate seamlessly to the unique-ness of the AI agent. Dialectical Behavior Therapy (DBT) approaches, which targetemotional regulation and interpersonal skills, could be integrated to an extent, butparticipants stressed that more complex elements of DBT require attentive over-sight and might exceed the self-clone’s scope for certain high-risk conditions.Lastly, because a self-clone can adopt varied personas, it aligns well with tech-niques involving role-play or reversal, such as the empty chair method from Gestalttherapy (P10, P12, P16). Here, a user might engage in dialogue with their self-16clone as though it were another person or a different part of themselves to resolveunfinished emotions or gain new perspectives on relationships. P16 described it as“a wake-up call” for users, especially those struggling to see how their behaviorsaffect others. Caution remains crucial; participants emphasized the potential for“things [to] get too deep too quickly” (P4) if not carefully structured or supervised,a consideration we explore more in-depth in the upcoming sections. Properly im-plemented, however, these techniques could be a powerful means of encouragingintrospection and self-compassion.3.2.2 Self-Clone’s PersonaSelf-clone chatbots, serving as digital identities, can adopt varying personas. Thespecific traits of each persona can shape users’ perceptions of their self-clones,thus broadening the interaction possibilities while offering unique therapeutic po-tentials.Self-clone asMirror. In their most fundamental form, self-clone chatbots func-tion as digital mirrors, offering an externalized reflection of the user. As P2 sug-gested, the use of mirror as a metaphor could serve as a reminder for users that theself-clone’s therapeutic competence is ultimately a reflection of their own. There-fore, in cases where professional intervention is necessary, seeking support from amental health practitioner remains essential.This concept may foster an inherent sense of familiarity with the chatbot, par-ticularly if, as P13 speculated, it exhibits immediately recognizable shared traits.As a result, users may find the self-clone more approachable; as P5 noted, ”psycho-logically, we like things that are similar to ourselves.” P11 further explained thatindividuals naturally adjust their communication style depending on their audience—speaking differently with a parent, friend, or partner— while a self-clone couldoffer a unique space where users can freely discuss a wide range of topics, includ-ing sensitive topics like sex and money, without the social constraints typicallypresent in human interactions.Although the mirror persona suggests that the self-clone would align with theuser’s personality, it does not necessarily have to reflect the user’s self-perception.As P16 explained, a self-clone generated from another person’s perception of the17user could serve as a tool for addressing relationship issues, helping individuals”put themselves in the shoes of their parents or their [partner].”Variants of Self. Self-clones can be intentionally designed to partially repli-cate, modify, or even contrast with aspects of the user’s self-concept, enabling a di-verse range of interactions. By selectively externalizing specific traits, self-clonesprovide a unique approach to self-exploration, perspective-taking, and psychologi-cal insight. However, it is crucial to note that excessive modification can impact theauthenticity and believability of the clone as a self-representation. These variationscan be categorized into speculative and real representations of the self.Speculative representations allow users to engage with their alternative self,either through time-shifted versions or modified personality traits. For instance, aself-clone modeled after the user’s younger self could serve as a means to revisitpast thought processes and experiences, effectively ”giving a voice to the innerchild” (P4). Similarly, a self-clone representing an older version of the user couldprovide a glimpse into potential futures, enabling them to “begin with the end inmind” (P8) and reflect on “what they want to potentially keep the same or change”(P5) about their lives. Additionally, modifying specific traits—such as anger orself-confidence—could offer an opportunity to explore what-if scenarios, helpingusers reflect on their perception of their ”ideal self” (P10). By adjusting attributeslike optimism and openness, self-clones can encourage users to adopt differentperspectives on their thoughts and emotions, fostering deeper self-understandingand facilitating potential personal growth.Beyond speculative transformations, self-clones can also externalize real anddistinct aspects of a user’s self-concept. This approach aligns with therapeuticframeworks such as Internal Family Systems and parts therapy, which emphasizeengaging with different internal sub-personalities. Experts highlighted how a self-clone could be designed to embody the user’s ”inner critic and coach” (P8) or”childhood self” (P10), allowing individuals to externalize self-doubt and cultivatea more compassionate self-view. Similarly, self-clones could represent supportiveor nurturing aspects of the self, reinforcing self-acceptance and resilience. As P9noted, externalizing emotional parts—such as an “angry part” or a “sad part”—mayhelp users engage with and process their emotions more effectively.183.2.3 User-Clone Relationship and Language UseThe relationship between a therapist and a client plays a critical role in shapingtherapeutic outcomes, as healing and personal growth often occur within this in-terpersonal dynamic. Although a self-clone chatbot lacks a genuine human con-nection (P4), experts suggest that users may still develop a perceived relationshipwith it, akin to the emotional bonds formed with Tamagotchis or imaginary friends(P10), with curiosity serving as the driving force in the initial stages of interaction(P13, P15). The following areas present opportunities to consider in the design ofself-clones to maximize its potential impact, as the early experiences may shapethe perceived relationship:Perceived Authority The perceived authority of a self-clone, ranging from anequal counterpart to a more knowledgeable presence, can significantly shape theuser’s experience. When the self-clone is framed as an equal, it could provide amore comforting environment, particularly beneficial for users navigating issuesrelated to authority figures (P1). However, its interactions are also likely to beinfluenced by the user’s existing self-perception and mental state (P8). In this case,users may project their pre-existing self-views onto the self-clone, whether positiveor negative, which could reinforce biases before meaningful engagement occurs.Conversely, framing the self-clone as a more authoritative figure may increase trustin its advice and guidance.Strategic Support and Challenge Experts suggest that the self-clone approachto providing care should strike a balance between support and challenge, incorpo-rating a degree of unpredictability to enhance engagement (P6). Excessive or mist-imed challenges may undermine the user’s sense of self-representation and trustin the self-clone. The user-clone relationship can be designed to replicate positivecharacteristics of provider-client interactions, such as respect, trust, and openness.It would also need to challenge users to promote personal growth. However, as P3warns, challenges should be carefully aligned with the user’s emotional capacity,as overly forceful interventions could discourage users.Pronoun Use. Since text-based communication is the primary mode of inter-action with self-clone chatbots, the perceived user-clone relationship is formed en-tirely within this medium. Therefore, it is essential to design language use thought-19fully. One critical consideration is the extent to which users perceive their self-clone as a distinct entity. Experts propose incorporating a collective narrative—shifting from an individual ’me versus you’ perspective to an inclusive ’we’—tofoster a sense of companionship and shared experience (P2). This linguistic ap-proach can make the chatbot feel more cooperative and less adversarial, reinforcingthe notion that “we are in this together” (P6). However, some experts caution thatsuch language use may, in certain cases, cause confusion or even raise identity-related concerns, such as ”personality splitting” (P7).Emotional and Cognitive Alignment. Language is a highly subjective expe-rience, as P12 explains: ”It’s not [just] about the words we use. It’s about themeaning of the words and their connection to places and people.” A believableself-clone can build trust by recognizing and incorporating the contextual nuancesof this subjectivity. Accurately reflecting and adapting to the user’s cognitive ca-pacity and emotional state could encourage engagement and help build trust, how-ever, replicating negative emotional states or limited cognitive capabilities could incases be limiting or risky. Meeting users ”where they are emotionally” (P5) canenhance validation and improve the overall reception of interactions. A mismatchbetween the user’s emotional state and the self-clone’s tone may lead to frustrationor disengagement. While responsiveness to emotional cues is essential, maintain-ing appropriate boundaries is equally important to prevent unintended emotionalescalation. Additionally, aligning the chatbot’s linguistic style with the user’s in-tellectual and cognitive level presents a valuable opportunity to deliver the care inan accessible way that users can easily understand, regardless of factors such asage or educational background (P4).3.2.4 Balancing Self-Portrayal’s Adaptability and ConsistencyDesigning a self-clone that strikes the right balance between adapting to the user’sevolving self-perception and maintaining a coherent, predictable persona can bechallenging. As P13 observes, “How I am on the first day of therapy versus the20th day... I’m going to have much more insight,” which underscores the impor-tance of allowing the chatbot’s self-representation to change over time—much likea user’s awareness and attitudes shift throughout therapy. Similarly, P15 explains20how an individual’s perception of their “inner child” may transition from viewing itas a tantrum-throwing nuisance to recognizing its protective qualities. Translatingthis dynamic progression into a self-clone requires continuous learning from userinteractions, especially given that the user’s internal thought process is not alwaysas easily accessible as training data. One way to manage these changes is to clarifythe chatbot’s “role-playing” nature early on—emphasizing, for instance, that it isakin to a child who learns to resemble the user more accurately over time. Yet, ex-cessive or unexpected persona shifts can undermine trust and engagement. As P10points out, when the chatbot’s behavior and tone remain within clear, predictableboundaries, users are more likely to trust the interaction and feel in control. Ulti-mately, balancing adaptability and consistency ensures that the self-clone remainsboth responsive to the user’s evolving self-concept and sufficiently stable to main-tain a sense of reliability.3.2.5 Provider InvolvementThe degree of mental health provider involvement in using a self-clone can varysignificantly based on factors such as the complexity of the intended therapeuticgoals, the user-specific clinical needs, and any associated risks, as emphasized bythe majority of experts. For high-risk or complex interventions—such as exploringchildhood trauma—an active provider role may be essential. In these scenarios,real-time guidance and monitoring ensure the conversation remains both safe andtherapeutically effective while also allowing the provider to offer additional supportif needed. For less intensive goals, periodic supervision and alignment checks (e.g.,reviewing session transcripts or progress) may be sufficient to maintain safety andeffectiveness. Finally, in lower-risk situations where privacy and autonomy areparamount, fully independent use of the self-clone can help foster a sense of controland encourage more open self-reflection. These distinctions help highlight twoprimary approaches for designing self-clone chatbots:Self-directed Care. Experts believe that designing self-clones as standalonetools can have benefits as they are accessible and cost-effective tools to promote thedevelopment of healthy coping mechanisms, improve self-confidence, and promotegreater self-efficacy. As P5 noted, such a tool may be particularly advantageous for21individuals experiencing social isolation or lacking a dependable support system,and as P7 notes, it can provide a sense of connection and deliver ”immediate sup-port.” P11 mentions how talking to oneself without fear of judgment can providea ’safe space’ to ”speaking openly with another where there’s no consequence”,thus potentially opening avenues for more in-depth self-expression. However, aspointed out by P6, it is crucial to consider the effectiveness of using such a tool in-dependently, without the guidance or supervision of a mental health professional,which is yet to be determined and may vary on a case-by-case basis.Provider-Supported Care. With the user’s informed consent, the involvementof a mental health professional in the use of self-clones can be considered in thedesign. As P15 noted, ”The idea of what we think our [self-clone] says might ac-tually differ from the type of dialogue that can lead to healing.” This highlights thepotential for provider involvement, which could range from indirect post-sessionor real-time observation of chat logs to ensure user safety and alignment with ther-apeutic goals to more direct supervision during use. Alternatively, self-clones canbe designed to enable providers to engage more actively, offering real-time guid-ance or prompting during interactions with the chatbot. As P7 explained, such anapproach could shorten the duration of therapy, particularly when self-clones areused as a tool for introspection during ongoing treatment or as a source of sup-port between or following therapeutic sessions. However, this strategy requiresthorough preparation on the part of providers in addition to clients. Establishing aclear mutual understanding of the tool’s capabilities and limitations is essential tomitigate potential biases. As highlighted by P6, the success of such a tool couldrely not only on its efficacy but, importantly, on the user’s readiness to engage innew technologies, akin to art therapy, where, despite proven effectiveness, someclients may nevertheless harbor reservations.3.3 Design Framework: ConsiderationsWhile design dimensions shape the functionality and possibilities of self-clonechatbots, design considerations focus on creating the optimal conditions for theirsuccessful and ethical adoption. In this section, we explore factors that can helpensure that self-clones are integrated into environments where they can be most22effective, resonate with users, and align with professional standards, remaining asboth impactful and responsible tools.3.3.1 Targeted User Groups ConsiderationsAttitudes toward AI would likely significantly influence how both clients and men-tal health providers respond to self-clone technology. As P14 notes, “Willing-ness to engage. . . [depends on one’s] belief and understanding of AI,” which canrange from deep skepticism to over-enthusiasm. Clients who expect a chatbot tobe “cold” or mechanical may interpret even supportive responses as impersonal,while those familiar with AI-driven tools can more readily accept self-clones asa meaningful resource. Similarly, providers who distrust AI or question its clini-cal validity may resist recommending or integrating self-clones into their practice(P1). Cultural context compounds these considerations, in which self-clones maybe more suitable for a specific demographic (P16).Another key factor is the user’s age, developmental stage, and psychologicalreadiness. Younger individuals may find self-clones helpful for identity explo-ration, with P15 suggesting it “would be really helpful. . . to get a clear understand-ing” of personal desires and drives. However, without adequate maturity or su-pervision, adolescents might become confused or overwhelmed by a chatbot that“replicates” them, leading to an uncanny or bizarre experience (P14). Older adultsmay have richer internal narratives yet feel less comfortable with technology, lead-ing to resistance or skepticism. In all cases, proper screening is crucial to matchthe tool’s capabilities with the user’s window of tolerance and their mindset towardtherapy (P16), ensuring that self-clones remain supportive rather than destabilizing.3.3.2 Constraints in Creating the Perfect ReplicaAiming for a flawless replica may be neither feasible nor advisable. Even withextensive personal data from a user, designing a self-clone that truly captures everynuance of that individual is fraught with technical or data-related challenges:Technical limitations On a technical level, popular large language models of-ten perform best in English and rely on Western-centric data, which can introducecultural and socioeconomic biases (P9, P16). For instance, if a user’s childhood23language was not English, having a past-self clone speak fluent English could ap-pear inauthentic and undermine trust (P16). Likewise, users from different culturalbackgrounds may interpret experiences like racism or physical discipline in waysthat deviate from Western norms, yet generalized AI outputs may fail to reflectthese distinctions accurately (P9, P16). Any AI biases that diverge from a user’slived experience can compromise the authenticity of the self-clone, such as the ten-dency for AI to be overly positive, which may feel misaligned or invalidating forusers experiencing depression.Training data limitations To design the perfect replica, accurate and com-prehensive information to train a self-clone may be needed, which is inherentlychallenging due to the nature of human memory and self-awareness. As P14 ob-serves, “our understanding of past selves is quite skewed,” shaped by particularlyintense emotional experiences and influenced by denial or projection, as seen incases where individuals might mislabel themselves as narcissistic based on a part-ner’s feedback (P16). Core beliefs may also reside in the subconscious, becomingaccessible “revelation by revelation” (P13), only over time or through guided thera-peutic processes, making them difficult to capture in a single dataset. Additionally,users may withhold personal details out of discomfort or lack of insight towardthemselves, resulting in incomplete or inaccurate representations (P16). Even forthose willing to share, the complexity of emotions and the dynamic nature of self-perception mean that what a user discloses at one point may not reflect their evolv-ing understanding of themselves (P15). As a result, any self-clone built from suchconstrained input, at best, will not capture more than an approximation of the user.3.3.3 Risks and SafetySince self-clones are inherently more personalized than generic chatbots, they poseunique safety risks that may extend beyond standard AI guidelines. A promi-nent concern is reinforcing negative self-perceptions when users harbor harmfulor “distorted views of themselves” (P5). By mirroring these traits, the self-clonemay inadvertently perpetuate or “further ingrain delusions” (P2), creating whatP3 describes as a “negative cycle” that is difficult to break. In such cases, settingproactive limits—such as “planning a way to break out of the loop” (P3)—can help24prevent spiraling and encourage users to seek external interventions if necessary.A related risk stemming from AI biases, a user’s idealized self-perception, or nar-cissistic tendencies lies in excessive positivity, which can undermine personal re-sponsibility and diminish critical insight (P14). Without balancing encouragementwith constructive feedback, the self-clone may fail to challenge unhelpful thoughts,leaving users without a “fresh perspective” (P14) or the ability to conduct mean-ingful “reality testing” (P15) of their beliefs. For some, self-clones may become anunhealthy attachment—akin to “social media addiction” (P3) or a “parental figure”(P10)—as users may prefer interacting with a familiar, like-minded entity, reducingtheir motivation to “talk to real people” (P5). In turn, they may face increased iso-lation (P12, P15) and lose access to genuine social or professional support, whichis often vital for emotional growth. Additionally, confronting certain aspects ofthe self—especially past traumas—through a self-clone can trigger intense shame(P16), underscoring the need for an external safety net. To address these issues,many experts emphasize the importance of transparency and user preparation. AsP16 notes, “developing the strategy to cope if anything goes wrong” can includedisclaimers, consent forms, and clear explanations of the self-clone’s capabilitiesand limitations (P14). By paralleling the “initial phase in therapy” (P10), users canbe guided on how to handle distress, recognize the self-clone’s boundaries, andseek human help if their difficulties escalate. Such proactive measures—rangingfrom clarifying expectations to offering backup resources—ensure that self-clonesremain supportive without displacing healthier coping strategies or professionalguidance.3.3.4 EthicsWhile self-clone chatbots share certain ethical concerns with other mental healthtechnologies, they also raise unique challenges tied to their identity-based design.Users may feel safer over-sharing sensitive information with a digital replica ofthemselves than with a human professional, yet this perceived comfort can maskreal-world implications. This highlights the importance of confidentiality, consid-ering that potentially ”the client[s] would be coming to the chatbot in a space ofvulnerability,” more likely to discuss sensitive issues like infidelity (P11).25Another key ethical dilemma is intentional discomfort as a mechanism for per-sonal growth. In therapy, practitioners use clinical judgment and intuition to deter-mine the appropriate timing and intensity of challenging conversations (P10). AsP11 explains, “There are a lot of people who really need to be ’disturbed’ by coun-seling” to break patterns of avoidance and enable change. However, AI lacks theability to make nuanced, context-sensitive ethical decisions, creating gray zoneswhere pushing a user too hard or too soon could be harmful (P13). Questionsalso arise regarding how and when a chatbot might be designed or obligated tointervene—such as reporting imminent risks like ’suicidal thoughts’— without vi-olating the autonomy of users in the lack of clinical judgment and intuition (P10).Experts also emphasize user agency (P16) and recommend that self-clones,similar to a care provider, only make suggestions, not decisions, helping users ar-rive at their own conclusions rather than imposing predefined outcomes (P1, P4).However, ethical concerns arise when the boundaries between the user and self-clone blur, especially if users begin attributing decisions to the chatbot rather thanthemselves. Clear role delineation is necessary to maintain a healthy user-clonedynamic and avoid potential dependency or confusion. Lastly, as P15 observes, acore ethical dilemma is identifying “who the chatbot serves.” A balance should bestruck between ensuring individual well-being and societal considerations, such aspreventing harm to others. Self-clones require a framework for these broader im-plications, with Clear safeguards established to address ethically charged scenariosresponsibly.3.4 ConclusionIn this study, we explored the potential and design considerations of self-clonechatbots as a mental health support tool in collaboration with 16 mental health pro-fessionals across psychology, psychiatry, counseling, and mental health research.We identified five dimensions that introduce variable aspects guiding the de-sign, including therapeutic approach and goal, agent’s persona, user-clone rela-tionship, balancing self-portrayal’s adaptability and consistency, and provider in-volvement. These dimensions showcase the possibilities designers have for self-clones. We also present considerations, which are context-specific requirements26that should be taken into account when designing the self-clones.Overall, our expert interviews yielded valuable insights into various designconsiderations for a safe, ethical, and effective implementation of self-clone chat-bots.27Chapter 4Designing Self-Clone ChatbotsOne of our primary objectives is to design a self-clone chatbot for mental healthsupport that maximizes user engagement. Grounded in our formative study, ex-isting literature, and best practices for conversational agent design, we focused onthree key principles relevant to enhancing engagement. Using these insights, webrainstormed and iteratively refined high-impact use case scenarios (see AppendixB.1), incorporating feedback from our advisory board of three clinical psycholo-gists during weekly meetings. Ultimately, we selected one scenario that, in ourteam’s opinion, best illustrated the potential of self-clone technology in mentalhealth support. This scenario was implemented using two distinct approaches: onefocused on replicating the user’s conversational style and the other enhancing itwith an added layer of replicating the supportive strategies through a novel tech-nique we refer to as Social Support Prompting.4.1 Design PrinciplesDesigning self-clones to enhance engagement for mental health support necessi-tates a careful approach to addressing interdisciplinary needs. Our overarchingstrategy is informed by Peter et al.’s Responsible Design Process [80], and wedraw upon insights from the existing literature on human-AI interactions, the useof AI conversational agents in digital mental health care, and the obstacles to en-gagement in digital mental health interventions. In collaboration with our interdis-28ciplinary team, we adapted established guidelines, including Amershi et al.’s HAIguidelines [7] and Ahmad et al.’s guidelines for conversational agents in mentalhealth [6]. These were assessed, consolidated, and modified to meet the specificneeds of self-clones, resulting in three key design principles tailored to the specificdesign challenges of self-clone. The following section details these principles andtheir role in our chatbot’s design.• Leveraging Promising Counseling Goal and Approach:Our first design principle combines two key elements of clear goal-settingwith proper theoretical grounding. Goal setting is important in therapy toprovide direction and measurable outcomes [30, 105], and in human-AI de-sign to set expectations and enhance user autonomy [7]. As a lack of the-oretical grounding in counseling and therapeutic interventions and a cleargoal can limit the effectiveness of mental health chatbots [12], this calls foran informed approach rooted in established counseling methodologies. Suchgrounding ensures the chatbot’s perceived competence [6] and supports theeffective delivery of overarching therapeutic techniques. While the specificgoal and AI’s behavioral rules may vary depending on the chosen thera-peutic framework, to create a supportive environment, the self-clone chat-bot should adhere to established principles for fostering a digital therapeuticalliance. At its core, similar to traditional therapeutic practices, the sys-tem should demonstrate empathy, reflect similar emotions, and validate theuser’s feelings [65, 99]. Leveraging AI’s strengths, the self-clone can serveas an interactive alternative to self-guided methods like reflective journalingor dynamically adapt care delivery to users’ cognitive and emotional needs,enabling tailored conversational styles and engaging therapeutic experiences[6, 55].• Having a Familiar yet Distinct Persona: Recent studies demonstrate awell-crafted persona gives a chatbot a consistent voice and behavior, whichhelps build trust and create a more engaging user experience [21, 85]. Thepersona design for a self-clone chatbot presents a unique challenge: whilethe persona must retain core aspects of the user’s identity and enough famil-iar traits to reinforce the self-clone concept, introducing strategic differences29is essential for fostering distanced self-reflection, enhancing intrinsic moti-vation, and promoting personal growth. These differences can be tailored tothe user’s specific needs, preferences, or the requirements of the overarchingtherapeutic framework, creating an adaptive agent [6]. This flexibility intro-duces opportunities for diverse approaches to persona differentiation, cate-gorized into promising strategies, such as (1) temporal positioning—settingthe persona at a specific time in the user’s life, such as before a significantevent or in an imagined future; (2) personality focus—emphasizing particu-lar aspects of the user’s self-concept, such as their inner critic or supportiveside; or (3) alternative versions—creating a hypothetical version of the userwho has overcome specific challenges (e.g., fear of a specific object, place,or situation) or embodies a more positive/negative perspective. While thesevariations provide valuable opportunities to explore different use cases, theyalso present potential risks. The differences may occasionally threaten thebelievability of the self-clone, and the impact of such moments is often un-predictable and may vary significantly between individuals.• Balancing Accuracy and Positivity in Trait Representation: Designedto replicate a user’s identity, self-clones represent oneself. Considering thedeep connection between mental health, personal identity, and individuality,the use of self-clones could potentially trigger concerns such as uncanniness,doppelga¨nger-phobia, or identity fragmentation, posing a risk to the client’smental well-being [59, 64]. Given the complexity of human nature, creatinga fully accurate digital representation of an individual is neither feasible nordesirable. The focus should be on replicating key traits that sustain the illu-sion of interacting with one’s clone while adjusting other traits to enhancetherapeutic outcomes, avoiding the reinforcement of harmful thought pat-terns or behaviors. The self-clone can draw on diverse data points such ascommunication style, preferred support approaches, personality traits, andrelevant factual information to create a convincing portrayal. However, notall data points are necessary for every use case. The selection of traits anddata sources should be tailored to the chatbot’s specific goals, consideringfactors such as interaction length, therapeutic objectives, data availability,30and privacy concerns.These principles establish a scalable foundation for the design of self-clones,ensuring their relevance across various user groups and scenarios while maintain-ing a focus on engaging and effective design. Certain factors, including extendedsafety considerations for special cases and user-specific principles related to age,culture, or digital literacy, were excluded at this stage because they rely on spe-cific therapeutic contexts and require scenario-specific testing, which is beyond thescope of this work. However, in our study, these factors will be considered as aprecautionary measure during participant screening.4.2 Design ScenariosSelf-clones present unique opportunities across various high-impact mental healthscenarios. Guided by our design principles, we developed and refined ten poten-tial scenarios through collaborative discussions with the clinical psychologist onour team. From these, we selected one primary scenario to represent the potentialof such technology for mental health care. This was selected based on its antici-pated impact, broad applicability, and scalability for unsupervised testing, creatingthe opportunity for comprehensive evaluation with larger participant samples. Acomplete table of the considered scenarios is provided in Appendix B.1. Our finaldesign incorporates three key design aspects to effectively implement the principlesoutlined in section 4.1, as described below:• Our design is inspired by the empty chair technique used in Gestalt therapy[1]. Traditionally, this method involves clients addressing an empty chairas if speaking to an imagined person or part of themselves, facilitating theexploration of interpersonal issues, unresolved emotions, or internal conflictsthrough externalized dialogue. In our approach, the chatbot replaces theempty chair, acting as a clone of the user and enabling them to externalizetheir internal dialogue in an interactive way. Given its flexibility, this methodcan be applied to a range of mental health topics, with users determining theconversation’s specific goal. The chatbot adapts its responses based on theuser’s needs, offering emotional support, validation, solutions, or motivation31as appropriate. By aligning its focus with the user’s unique internal dialogue,our design remains relevant across diverse mental health concerns and usergroups.• In terms of persona, we have chosen to position the self-clone as the user’sfuture self—someone who has successfully navigated the challenges the useris currently facing. This hypothetical entity can create a reassuring and pow-erful narrative that reinforces the message, ”This too shall pass.” By framingthe clone as a future self, we also aim to provide a plausible explanation forany subtle differences in language or a more mature outlook that may stemfrom the AI’s positive bias. This framing not only enhances the believabilityof the interaction but can also serve as a motivation for users to strive towardbecoming the perceived improved version of themselves.• For selective trait representation, we prioritized characteristics that have themost significant impact on brief interactions with a chatbot: conversationstyle and preferred social support strategies. The gathered factual data waslimited to the user’s name, as we deemed collecting more extensive personalinformation unnecessary. The brief usage period made detailed user dataunlikely to be needed, and the privacy and ethical concerns outweighed anypotential benefits. To gather relevant data efficiently, we developed a novelmethod inspired by Solomon’s Paradox—the phenomenon where individualstend to offer wiser advice to others than to themselves [40]. We introducedan AI chatbot, ’A Friend in Need,’ which simulates a hypothetical friendstruggling with self-doubt caused by imposter syndrome. Through interac-tions with this chatbot, users revealed their conversational patterns and socialsupport strategies. These are then replicated in the user’s self-clone, allowingthem to apply their own wisdom to their personal challenges.4.3 Prompt DesignThrough an iterative design and testing process, we developed a total of four promptsfor our conversational agents: one for the ’A Friend in Need’ chatbot to collectprimary training data and three for the main chatbot (two self-clones and one base-32line). A separate prompt was also created to pre-analyze the training data for one ofthe self-clone models. Guided by White et al.’s prompt patterns [113], we followeda structured framework. Each main chatbot prompt included similarly defined sec-tions, including one for user-specific training data, if applicable, one dedicated toguidelines for the conversational flow with a predefined opening message to estab-lish a casual and friendly tone, and another section for general rules to ensure ahuman-like interaction. These prompts began with an overview that outlined thechatbot’s persona, provided instructions, and explained the purpose of each section.Full details of the final prompts are included in Appendix B.4.Our chosen scenario required the representation of conversational style andpreferred social support as core elements of the self-clone design. While it is feasi-ble to replicate high-level supportive behaviors (e.g., solution-focused vs. emotion-focused) through a black-box approach, the inherent complexity of these behaviorsoften necessitates additional considerations for accurately identifying and replicat-ing nuanced support patterns. Therefore, to better understand a user’s support pat-terns, we developed a Social Support Prompting (SSP) technique designed to iden-tify specific support types, intensity, and frequency throughout the conversation.This classification is based on an adapted version of Suhr et al.’s [96] Social Sup-port Behavioral Code and includes categories such as informational support (e.g.,advice), esteem support (e.g., compliments) and emotional support (e.g., sympathyand encouragement). SSP also accounts for the dynamic nature of conversationsby capturing potential shifts in the type of support provided as users adapt their fo-cus throughout the progression of the conversation. Implementing SSP introducesan additional layer of pre-analysis to categorize data, with the results used as sup-plementary training data alongside chat logs collected by the ’A Friend in Need’chatbot.In our study, we built and evaluated two variants of the self-clone design: oneincorporating SSP and one without, as both pilot tests and theoretical considera-tions suggested potential benefits and drawbacks to replicating a user’s social sup-port strategy. The self-clone without SSP (SCX) excelled in capturing the user’swriting style, including specific word choice and sentence structure, significantlyenhancing the representation of conversational style. However, it lacked a struc-tured approach to providing support and tended to overuse supportive strategies that33were misaligned with the user’s personal approach. In contrast, the self-clone withSSP (SCS) was more effective in representing social support strategies, adaptingits approach to meet the user’s specific needs, and offering a more familiar conver-sational flow. However, the depth of style personalization observed in SCX waslacking. These two designs, SCX and SCS, serve as experimental conditions in ourevaluation alongside a baseline condition designed for use in the later assessment.The baseline chatbot (BL) functioned similarly to the self-clone chatbots and fol-lowed the same counseling goals and approach. The primary difference was thatit adopted the persona of a counselor named Sam and did not have access to per-sonal data beyond the user’s name. Examples of resulting conversations with theself-clones are presented in Table 4.1.34Sender MessageParticipant ”That could work. I actu-ally have a TODO list witha list of stuff I need to do.”Self-Clone ”Perfect! Revisiting andupdating your TODO listcan really help clarifyyour priorities and giveyou a clear path forward.It sounds like you’re set-ting yourself up for a pro-ductive session”Participant ”Thanks for the advice!But I really am curiousabout one thing...do I stillhave amazing hair in thefuture?”Self-Clone ”Ah, always focusing onthe important questions!Let’s just say, our charmdoesn’t diminish withtime. Now go tackle thatTODO list with the con-fidence of someone whoknows they’ve still gotgreat hair in the future!”Participant ”Thanks, [name] the El-der”Sender MessageParticipant ”I wish i could believe it”Self-Clone ”Believing in ourselves isdefinitely the hardest part,but every step forward, nomatter how small, helpsbuild that confidence backup. We’re more capa-ble than you give ourselvescredit for.”Participant ”sometimes i think i’m themost useless person in theworld and everyone knowsbetter than me. I know thatisn’t the case though, be-cause I’ve met other people.Haha.”Self-Clone ”Ha, exactly! . . . It’s ashared struggle that some-how makes things feel a bitlighter.”Participant ”Thanks me, you’ve beengreat”Self-Clone ”Anytime! It’s what I’mhere for—to remind youthat we’re not alone inthis. We’ve got each other’sback, always.”Table 4.1: Examples of conversation with our self-clones, two representativeexamples sampled from the chatlogs of our participants. P155 from theSCX condition on the left and P26 from the SCS condition on the rightside. Identifiable information has been replaced with placeholders.35Chapter 5Evaluating Self-Clone ChatbotsTo evaluate the effectiveness of our design concepts and approaches in improvingengagement, we employed a quantitative controlled experiment approach through acomparative study to investigate engagement levels complemented by a qualitativeapproach incorporating open-ended questions to capture participants’ perceptions.5.1 Methods5.1.1 Experiment ProcedureOur primary study consisted of a single 30-minute session divided into four steps:1) a preliminary survey lasting 5 minutes, 2) an 8-minute interaction with the AFriend in Need chatbot, 3) a 10-minute interaction with a randomly assigned chat-bot (BL, SCX, or SCS), and 4) a 7-minute follow-up survey. We tested our ap-proach and protocol with 30 pilot participants for consistent and robust results.The university’s institutional review board approved the research protocol. Anoverview of the study is shown in Figure 5.1.The preliminary survey gathered key participant characteristics and demographicinformation. To ensure data quality and participant experience, we established spe-cific messaging requirements for both the ’A Friend in Need’ and the main chat-bots, resulting from our pilot testing. These included a minimum of 10 messagesfor the ’A Friend in Need’ chatbot to gather sufficient data for potential self-clone36creation and at least 12 messages to ensure meaningful interaction for data collec-tion.Participants could continue conversations beyond these minimums if desired.Our follow-up survey captured both quantitative and qualitative data on user en-gagement levels through relevant scales discussed in 5.1.2, chatbot experience andperception, and self-clone-specific questions for participants in the SCX and SCSconditions. While the baseline chatbot did not incorporate data from the A Friendin Need chatbot, we ensured consistent processes across all conditions to maintaincomparability.The decision to conduct a one-off short study session is grounded in our theory-based design, which builds on an existing intervention approach for a single, shortsession rather than recurring, longer sessions. Multiple sessions with repeated mea-sures are incompatible with the study’s context and intervention method, as mentalwell-being issues are often complex and interconnected. Dividing the delivery ofthe intervention across multiple sessions would likely introduce significant orderand learning effects, with the first session strongly influencing subsequent sessions.Moreover, feedback from pilot testing indicated that a 30-minute session, encom-passing approximately 10-12 chat turns with the main chatbot, is sufficient fordiscussing at least one topic and reaching a conclusion. Participants reported thatengagement beyond this duration depended on subject changes, a challenge outsidethe scope of our study.To address the potential impact of novelty, a follow-up study was conducted10 weeks later with SCX and SCS participants to evaluate result consistency overtime. Steps 3 and 4 of the primary study were repeated, using each participant’sself-clone model retrieved from the initial study to minimize effort. This timeframeallowed participants to forget specific details while retaining familiarity, enablingus to assess whether results remained consistent as the novelty of the self-cloneconcept waned.5.1.2 Evaluation Instruments and MeasuresThe novel nature of our study, combined with the complexity of engagement andthe lack of standardized measures for digital mental health chatbots, necessitated37?Preliminary Survey?Follow-up Survey‘A Friend In Need’ ChatbotBaselineSelf-Clone without SSPSelf-Clonewith SSPChat logsSocial Support PromptingTraining data #2 - SSP ResultPre-analyzed support strategies resulting from SSPTraining data #1 - Chat logsInforming conversation pattern and supportive strategiesStep 1:Taking thepreliminary surveyStep 4:Taking thefollow-up surveyStep 2:Interacting with‘A Friend in Need’ ChatbotStep 3:Interacting with one of the three chatbots based on assigned conditionUserORORCollected dataFigure 5.1: Comparison of user interaction and training process across thethree conditionsmultiple adapted quantitative and qualitative measures for triangulation and com-prehensive understanding [10]. We measured six quantitative variables in total.The three primary outcomes, our dependent variables (DVs), were collected post-study and included engagement, motivation, and technology acceptance. Addi-tionally, we measured three independent variables (IVs): perceived self-clone be-lievability (collected post-study), AI literacy (collected pre-study), and attitudestoward AI (collected pre-study). The main independent variable in our study wasthe condition, which was randomly assigned by the researchers. For the dependentvariables, we used the following measures: Engagement was assessed primarilyusing the TWente Engagement with EHealth and Technologies Scale (TWEETS)[52], as well as the User Engagement Scale (UES)[70] and objective metrics. Weadapted cognitive and emotional items from TWEETS, excluding behavioral itemsrelated to long-term adherence. Alternative behavioral engagement measures in-cluded message count, conversation duration, average word count per message,and median time per turn. Although UES was not explicitly designed for mentalhealth applications, it served as a secondary scale to validate observed engagementpatterns. Motivation and technology acceptance were measured using modifiedversions of the Client Motivation for Therapy Scale (CMOTS) [74] and the UnifiedTheory of Acceptance and Use of Technology (UTAUT) model [108], respectively.38CMOTS complemented our engagement measures by providing insights into users’perceptions of the chatbot, while UTAUT was adapted to assess users’ perceptionsof self-clones as a new AI-based mental health technology, including the likelihoodof acceptance and sustained use. A list of questions used can be seen in AppendixB.2 and B.3.For the independent variables, we assessed perceived self-clone believability—defined as the extent to which participants viewed their self-clone as a believableself-representation—using a 5-point Likert scale in the self-clone conditions (SCXand SCS). AI literacy and attitudes toward AI, collectively referred to as AI-relatedfactors, were measured using the AI Literacy Scale (AILS)[110] and the AttitudesToward Artificial Intelligence Scale (AIAIS)[39], respectively, to evaluate partici-pants’ prior experience and knowledge of AI.Lastly, our complementary qualitative data includes chat logs from partici-pants’ conversations with the main chatbots, along with their comments on thechatbot experience and engagement collected through the follow-up survey. Forparticipants in the SCX and SCS conditions, we also collected feedback on thesimilarity and believability of the self-clone chatbots.5.1.3 Participants and RecruitmentTo recruit participants and run our study, we employed Prolific 1, which is well-regarded in human-computer interaction and behavioral research, offering accessto a diverse, global pool of registered participants. Conducting the study in anonline asynchronous setting limited our control over participants’ environmentsduring chatbot interactions, rendering the study semi-controlled and potentiallyintroducing variability that could affect the results. Our target sample size for theprimary (N = 180) was determined by a power analysis to detect a medium effect(f = 0.25) with a statistical power of at least .80.Given our focus on the mental health domain, we implemented extensive screen-ing measures as a preventive safeguard to minimize risks for our participants. Ourscreener excluded individuals with suicidal or homicidal ideations, as well as thosewho self-reported severe mental health issues with significant difficulty coping1https://www.prolific.com39with their symptoms. As our study focused on the use of AI to discuss mild men-tal health issues, we also excluded participants who expressed strong oppositionto using AI in mental health contexts or those who reported no current mentalhealth concerns. Furthermore, we excluded individuals who had recently changedmedication or treatments, as such changes might impact cognitive functioning orexpose participants to safety risks when engaging with sensitive content. We re-quired English as the primary language for study inclusion to ensure consistencybetween participants’ inner dialogue and the chatbot’s language. Eligible partic-ipants above the age of consent (19) and who passed our screening criteria werethen evenly assigned to different experimental conditions based on age and gender.We initially screened 1,300 individuals, with 20.8% (N=271) meeting our el-igibility criteria. Of those eligible, 180 ultimately participated in the study. Thegender distribution showed 120 participants self-identifying as women, 58 as men,and two as non-binary, with a mean age of 42.18 years (SD = 12.57). Regardingethnicity, the breakdown was as follows: 153 European/White, 10 Asian, 7 Black,9 Mixed, and one unspecified. AI-literacy scores showed moderate variation (range7-84) with a mean of 62.28 (SD = 9.38). Three participants self-reported their AIfamiliarity as expert, 81 as intermediate, 90 as beginner, and six as unfamiliar.Regarding their mental health status, participants scored an average of 7.68 (SD= 4.36) on the Kessler Psychological Distress Scale [106], indicating low to milddistress levels. Individuals in the study reported having received a variety of treat-ments (therapy, medication, or a mix of both) and managing a diverse range ofsymptoms associated with various mental health conditions, including mood disor-ders like depression, anxiety-related issues, neurodevelopmental disorders such asattention deficit hyperactivity disorder (ADHD) and autism, as well as substanceuse disorders. During the follow-up phase, all participants from the self-clone con-dition (N=120) were invited to take part in the subsequent round of the study, with66 individuals ultimately participating.5.1.4 Data AnalysisWhere available, we adhered to the specific scale scoring guidelines. In cases with-out explicit guidelines, we developed composite scores by standardizing scores us-40ing z-scores and calculating their mean. Following established practices in priorstudies [97], we treated normally distributed Likert scale data as continuous vari-ables and assessed normality prior to statistical analyses. Spearman’s correlationand Mann-Whitney U test were used for relationships involving non-continuousvariables, while ANOVA tested mean differences across categorical groups, con-trolling for Type I errors. To evaluate predictive power, we constructed a linearregression model. A mixed-effect regression model, incorporating perceived be-lievability as a key factor, was initially considered. However, this approach wasdeemed unfeasible, as the perceived believability of the chatbot to be one’s self-clone was only defined for the self-clone conditions and was absent in the baselinecondition. Lastly, to assess bimodality in the believability measures, we appliedHartigan’s dip test of unimodality and Gaussian mixture modeling.To complement our quantitative assessment, we conducted a qualitative anal-ysis of participants’ chat logs and survey feedback. Our approach was guided byBraun and Clarke’s reflexive thematic analysis [14], which we adapted to suit thespecific needs of our study. Chats with the main chatbots lasted at least 12 turns,typically centering on a single mental health-related topic that was discussed andconcluded, yielding substantial chatlog data for analysis. Behavioral changes wereobserved through sudden shifts in discussion depth or message length. The leadauthor applied deductive coding based on our quantitative measures of believabil-ity and engagement, using constant comparison to mitigate bias toward quantitativeresults and address any contradictory findings. The analysis continued until satura-tion was reached, at which point the lead researcher no longer identified significantnew codes in the latest batch of data. The research team engaged in collabora-tive discussions to review the codes, resolve disagreements in data interpretation,and collectively establish themes. This process led to the identification of over40 distinct codes, which were consolidated into nine broader categories. For ex-ample, initial codes such as ”talking to a mirror” and ”felt like journaling” werecombined into the higher-level category ”Resemblance to existing therapeutic ex-ercises.” Subthemes outside the scope of our research or lacking sufficient support-ing evidence were discarded, though some are addressed in the discussion section.Ultimately, we identified five themes that supplemented the interpretation of ourquantitative results.415.1.5 ImplementationWe implemented the experimental system for the study using HTML, JavaScript,Python, PostgreSQL, and AWS infrastructure. A screenshot of the website is in-cluded in Appendix B.5. To develop our self-clone and baseline chatbots, we usedand fine-tuned the GPT-4-0125 model 2. Detailed prompts for each approach, theA Friend in Need chatbot, and the SSP analysis process are outlined in AppendixB.4.5.2 ResultsThe protocol generated sufficient quantitative data for statistical analysis and qual-itative data to achieve thematic saturation. Participants spent an average of 8.95minutes (SD=4.85) interacting with the ’A Friend in Need’ chatbot, using an aver-age of 14.70 words (SD=9.06) per message. Across the three conditions, partici-pants demonstrated similar behavioral engagement patterns with the main chatbot,spending approximately 8.98 minutes (SD=4.86), with an average of 33.26 sec-onds (SD=17.91) per turn and 14.76 words (SD=9.13) per message. A full reporton these behavioral engagement measures can be found in AppendixB.6.1.The results of our evaluation demonstrate the overall positive impact of self-clone chatbots in enhancing user engagement. Our analysis also revealed a gener-ally positive perception of the self-clones, with the system positively influencingusers’ motivation and acceptance. Participants’ interactions with the self-cloneinvolved a variety of experiences, including seeking reassurance for current chal-lenges, personalized advice, and future-oriented speculation, as seen in Table 4.1.In the following sections, we detail the statistical analysis of quantitative mea-sures complemented by qualitative interpretations. In the presentation of qualita-tive findings, participants are referred to as PNcon, where N indicates the participantnumber and con denotes the chatbot condition they experienced (BL for the base-line, SCX for the self-clone without SSP approach, and SCS for the self-clone withSSP approach).2Accessed in August 202442Threshold: 2.810102030400.00.10.20.31 2 3 4 5Perceived Self−Clone Believability ScoreCount DensityComponent High Believability Low Believability TotalFigure 5.2: The distribution of the perceived believability scores is shownas a bar graph (left axis) alongside the mixture model represented bydensity curves (right axis).5.2.1 The perceived believability of the self-clone is not unimodal butinstead consists of two distinct subpopulations.The believability of participants’ self-clone as a representation of their future selfwas measured in both SCX and SCS conditions. The perceived self-clone be-lievability scores (Mean=2.84, SD=1.2, N=120) were comparable across the twoconditions. However, a trend was observed in which SCS participants exhibitedhigher believability (Mean=2.98, SD=1.17, N=60) scores than SCX participants(Mean=2.7, SD=1.21, N=60). Analyzing the perceived self-clone believabilityvariable revealed strong evidence of a bimodal distribution. Hartigan’s dip testproduced a significant result (D = 0.1375, p < .001), rejecting the null hypothesisof unimodality. The distribution (Figure 5.2) demonstrated slight positive skewness(0.134) and platykurtosis (1.905), further supporting the presence of bimodality. Atwo-component Gaussian mixture model identified a threshold of 2.81, distinguish-ing the two groups. These findings suggest that the perceived believability variablecomprises two distinct subpopulations: a high believability group with scores of 3or above, representing approximately 53.33% of the sample, and a low believabilitygroup with scores of 1 or 2.43Analysis of the qualitative data suggests that negative self-perception may in-fluence participants’ perception of self-clones and perceived believability, particu-larly when contrasted with the positive attitude of the self-clone. As one participant(P150SCX ) explained, “I understand the positives of having a wise, centered self inthe future, but I find it difficult to believe that I could be that person.” In additionto the positivity expressed by the self-clone, participants highlighted discrepanciesbetween their levels of introspection, kindness, and helpfulness compared to thoseof the chatbot. Low self-confidence also appeared to shape participants’ expecta-tions, as another participant noted: “I wish I could lose my self-deprecating nature,and I really hope I do, but I somehow doubt I will.”(P50SCS). Furthermore, someparticipants discussed the conceptual challenge of interacting with a fictional rep-resentation of one’s future self, as P53SCS remarked: “My future self doesn’t exist,and I find it difficult to suspend disbelief on that.”5.2.2 Engagement with self-clones is positively correlated withparticipants’ perceived believability of the clone.The average engagement score for the self-clone conditions was 22.44 (SD = 4.85),comprising emotional engagement (Mean = 11.07, SD = 2.64) and cognitive en-gagement (Mean = 11.36, SD = 2.42). These results suggest that participants ex-hibited a balanced level of engagement, with slightly higher cognitive engagementrelative to emotional engagement. For the statistical analysis, due to the bimodalityof perceived believability, it was treated as a discrete variable. A Spearman’s cor-relation revealed a strong, positive, and statistically significant correlation betweenengagement and perceived believability (rs = 0.718, p < 0.001, N = 120). Thisrelationship is illustrated in Figure 5.3.The qualitative analysis of chatlogs suggested three prominent patterns in par-ticipants’ interactions with their self-clones. Participants in the first group main-tained a relatively consistent perception of the clone as their future selves, oftenreferring to it as their older version or by their own name. Occasionally, they em-ployed plural pronouns rather than singular ones when referring to themselves ortheir clone. In contrast, participants in the second group tended to be skepticalfrom the outset, often hesitating to disclose mental health-related issues or test-ing the clone by posing specific factual questions. The third group demonstrated44Self−Clone with SSP y = 2.70x + 14.87p < 0.001Self−Clone without SSP y = 3.06x + 13.70p < 0.0011020301 2 3 4 5Perceived Believability ScoreEngagement ScoreCondition Self−Clone without SSP Self−Clone with SSPFigure 5.3: Positive correlation between Perceived Believability Score andEngagement Score across Self-Clone without SSP (red) and Self-Clonewith SSP (blue) conditions, with shaded 95%confidence intervals. Toaddress overplotting, jitter was added to the data points.a mixed response: while initially engaging positively, their skepticism increasedas the interaction progressed. This group often experienced what we term inter-action breakdown moments, during which they began questioning the chatbot’sauthenticity. Such breakdowns typically occurred when the self-clone refused todisclose future events, displayed insufficient knowledge of the participant (e.g., de-tails about career plans or current relationships), or provided advice that seemedunfamiliar or irrelevant to the participant’s circumstances. As P145SCX remarked,“If I am talking about experiences I know I’ve had and it doesn’t know about them,it’s obviously not me,” highlighting the impact of these moments on perceived be-lievability. Following such breakdowns, participants generally exhibited reducedengagement, characterized by shorter responses and avoidance of deeper discus-sions.455.2.3 The self-clone chatbots (SCX and SCS) resulted in significantlyhigher engagement compared to the baseline in the highbelievability group, with SCS showing a trend toward furtherincreased engagement relative to SCXParticipants in condition BL reported lower levels of engagement (Mean=23.35,SD=4.14, N=60) compared to participants in high believability SCX (Mean=25.03,SD=3.04, N=29) and high believability SCS (Mean=25.34, SD=2.58, N=35). Aone-way analysis of variance (ANOVA), with Holm-Bonferroni corrections ap-plied to control for multiple comparisons, revealed a significant effect of condition(highly believable SCX or SCS and baseline) on overall engagement, F(2,121) =4.366, p = .015, padjusted = .0167, cognitive engagement, F(2,121) = 4.133, p =.018, padjusted = .025, and emotional engagement, F(2,121) = 3.892, p = .023,padjusted = .05. When believability was not considered as a mediating factor (i.e.,without dividing participants into high- and low-believability groups), the analy-sis revealed no significant differences between the conditions, further emphasizingthat the self-clone intervention relies on participants’ perception of its believabilityto be effective. The graphs illustrating engagement levels for both low and high-believability groups combined are shown in Appendix B.6.3. Engagement levelsfor the high-believability group are presented in Figure 5.4.For UES, there was an observable increase across conditions, with conditionBL (Mean=14.94, SD=2.48) reporting the lowest user engagement, followed byhigh believability SCX (Mean=15.67, SD=2.02) and high believability SCS (Mean=15.79, SD=1.57). While the trend suggests a potentially beneficial effect of self-clones on engagement, this effect was not statistically significant (p = .126).The qualitative analysis also highlighted the positive and engaging experienceof interacting with one’s future self, describing the experience as natural, relatable,and comparable to real-world conversations. As P129SCX expressed, ”I really lovedthe fact that it was me from the future. . . I was absorbed in talking to myself.” An-other participant (P155SCX ) emphasized the self-centric nature of the interactionand noted, ”It was somewhat similar to therapy . . . it’s really YOU that is doingthe work, . . . being a conversation with myself.” Participants likened the experi-ence to engaging in self-talk, conversing with a mirror, and using the chatbot as asoundboard or as a form of journaling, through which they claimed to receive emo-46***15202530ConditionEngagement Measured by TWEETSBaseline Highly BelievableSelf−Clone without SSPHighly BelievableSelf−Clone with SSP**7.510.012.515.0BaselineHighlyBelievableSelf−Clonewithout SSP HighlyBelievableSelf−Clone withSSPConditionCognitive Engagement Measured by TWEETS**68101214BaselineHighlyBelievableSelf−Clonewithout SSP HighlyBelievableSelf−Clone withSSPConditionEmotional Engagement Measured by TWEETSFigure 5.4: Boxplots illustrating the engagement scores, including com-bined, cognitive, and emotional dimensions, across baseline and high-believability self-clone conditions. The significance codes are ** for p< 0.01, * for p < 0.05.tional support as P7SCS explained, ”I felt like they understood where I was comingfrom, knew what I needed to hear [at] that moment, and were sincere”. Similarly,other sentiments were made by participants, including, ”[It was] like it understoodme”(P18SCS), while another stated, ”I felt like it knew what I needed”(P20SCS).5.2.4 Consistent follow-up results suggest minimal influence ofnovelty effect on initial findings.Behavioral engagement factors for the self-clone chatbots in the follow-up studyremained largely consistent with the primary study after three months, which wasintended to limit the effects of novelty and initial enthusiasm. Participants en-gaged with their clones for an average of 12.40 minutes (SD=7.78), which isslightly higher than in the primary study (Mean=9.16, SD=4.84). Additionally,participants spent slightly more time per conversational turn in the follow-up study(Mean=37.29 seconds, SD=20.64) compared to the primary study (Mean=33.9 sec-onds, SD=18.48). The average word count per message in the follow-up study(Mean=14.52, SD=7.58) was comparable to the primary study (Mean=14.16, SD=8.59). However, participants engaged in a slightly higher number of conversa-tional turns in the follow-up study (Mean=15.38, SD=4.20) compared to the pri-mary study (Mean=13.04, SD=2.08).To examine the effects of novelty, a paired-samples t-test was conducted to47compare engagement from the primary study and the follow-up study for the sameparticipants under the self-clone conditions. The paired samples t-test revealed nosignificant difference in the engagement between the primary study (Mean=21.98,SD=5.04) and the follow-up (Mean=21.74, SD=5.478), t(65) = 0.438, p = .663,two-tailed. Strong correlations were found for engagement (r = 0.637,p < .001)across the studies. Confidence intervals included zero, and effect sizes were small,suggesting negligible differences between the two studies. Similarly, we ran aMann-Whitney U test to compare the discrete variables of perceived believability,which revealed no significant difference across studies, U = 655.50,Z = 1.48, p =.139. The distributions of the two groups appear similar. These findings indicatethat the observed results are consistent across studies, suggesting that external fac-tors such as novelty did not significantly influence the initial positive findings.5.2.5 Perceived motivation and acceptance were strongly correlatedwith engagement and exhibited similar improved patterns inboth self-clone conditions compared to baseline.Participants in condition BL reported lower motivation to engage in therapy (Mean=0.12, SD= 0.86, N= 60) and acceptance towards the use of the chatbot (Mean=0.15, SD= 0.70), whereas participants in highly believable SCX reported a slightlyhigher motivation (Mean=0.38, SD=0.82, N=29) acceptance (Mean=0.39, SD=0.68).Similarly, participants in highly believable SCS exhibited higher motivation scores(Mean=0.34, SD=0.52, N=35) and acceptance (Mean=0.33, SD=0.50). While thetrend in motivation and acceptance did not achieve statistical significance (p= .261and p = .060, respectively), the overall pattern points to a potential positive effectof self-clones. Furthermore, The correlation analysis revealed significant positiverelationships between motivation, acceptance, and engagement. Engagement wasstrongly positively correlated with both acceptance (r = .800, p < .001) and moti-vation (r = .785, p < .001). Additionally, a strong positive correlation was foundbetween motivation and acceptance (r = .844, p < .001), indicating that partici-pants with higher motivation levels also tended to report higher acceptance levels.The qualitative data revealed generally positive attitudes and acceptance towardthe self-clone concept. Several participants expressed explicit interest in using theself-clone, with one stating, ”I could see myself using something like this in the48future” (P140SCX ) and another remarking, ”I wish I could really use this on a dailybasis; it feels nice to know the AI [self-clone] is there” (P149SCX ). Many partici-pants felt that the self-clone’s recommendations closely mirrored their own behav-ior, which made them more receptive to its advice. One noted, ”That is exactlywhat I would do and have done,” while another commented, ”I do give the bestadvice after all!” (P129SCX ). P4SCS reported viewing self-clone advice as ”thatextra push I need to make decisions”. Participants also described feeling a con-nection with their self-clone, viewing the interaction as a safe space for authenticself-expression. One participant shared, ”It’s a helpful way to vent feelings/frus-trations without the embarrassment of truly opening up. I was more honest withthe chatbot than I have been with my therapist at times”(P49SCS). Additionally,the perceived alignment with participants’ racial and cultural backgrounds had apositive impact, as one explained: ”It set up a scenario where I could be myself . . .No [ethnic]-hate, no looking down on me, no judgments”(P149SCX ).5.2.6 AI awareness shapes user perception and engagement withchatbots, varying by individual AI literacy and attitude towardAIA multiple linear regression was conducted to examine the effects of AI literacyand attitude toward AI on engagement (TWEETS). The regression model wasstatistically significant, F(2,177) = 19.19, p < .001, and accounted for approx-imately 17.82% of the variance in engagement (R2 = .1782,Ad j.R2 = .1689). TheAdjusted R2 value, which slightly adjusts for the number of predictors in the model,suggests that this proportion remains consistent after accounting for model com-plexity. AI literacy was revealed to be a significant factor influencing engagement,β = 0.088, t(177) = 2.13, p = .035, 95% confidence interval [0.006, 0.170]. Thepositive beta coefficient indicates that for each one-unit increase in AI literacy, en-gagement is expected to increase by 0.088 units, holding other factors constant.Similarly, attitude toward AI was identified to be a significant factor impacting en-gagement, β = 0.747, t(177) = 3.53, p = .001, 95% confidence interval [0.330,1.164]. This indicates that a more favorable attitude toward AI corresponds tohigher engagement, with a one-unit increase in attitude toward AI predicting anincrease of 0.747 units in engagement.49Qualitative analysis suggested that the AI-driven nature of self-clones impactsthe perception of chatbots. Some participants displayed a heightened awarenessof the synthetic nature of the SC, often perceiving it as just a ”robot” (P158SCX ).Statements such as ”I can’t separate that it’s not a real person and it doesn’t makeme feel seen or heard” (P56SCS) and ”since I know it is an AI bot, I know it doesnot actually care and is just programmed to respond” (P6SCS) reflect a perceptionthat may limit one’s willingness to commit to the experience, which, while par-ticularly pronounced in the self-clone conditions, was not unique to them. Someparticipants with more positive attitudes toward AI tended to be more forgivingof AI limitations. These participants often rationalized issues, such as their self-clone’s inability to reference previous events, attributing it to a lack of ”a largevolume of [training] data”(P9SCS) and perceiving the limitation as understandable.Participants with higher AI literacy demonstrated a more nuanced attitude: theirawareness of AI limitations led to more realistic expectations, mitigating poten-tial disappointment. However, they also noted AI-specific patterns, such as us-ing generalized sentence structures, making responses less personalized. Somealso questioned the feasibility of recreating a human-like interaction with limiteddata, approaching the experience with a degree of skepticism. Additionally, a fewAI-experienced participants identified recurring support patterns in the chatbot’sproblem-solving approach, with one participant noting an ”awkwardness... whenI realized the pattern” (P53SCS). This observation highlights the user’s familiaritywith AI as possible factors involved in interaction breakpoints.50Chapter 6DiscussionIn this section, we reflect on our findings and their implications for the use of self-clones as a digital mental health support tool, with the goal of providing safe andengaging therapeutic interventions.6.1 Design ImplicationsOur results yield three key design implications for HCI and DMHI researchers:• Leveraging Novelty as a Design Factor The emergence of technologies likeself-clones often sparks novelty and initial enthusiasm. Novelty, as an aspectof user perception and the subjective response to something unknown, is notan intrinsic property of the technology but rather emerges from user’s expec-tations and interaction [93, 94]. While the intensity of novelty may fluctuateas familiarity increases, this relationship is argued to be neither linear norstatic [5]. Often viewed as a dialectical tension within social relationships,novelty is believed to be both sought after and appreciated, yet in conflictwith the desire for familiarity and consistency [38, 93]. Framing noveltywith the self-clone as an intended effect highlights its role as a valuable partof the experience [50]. In our study, participants explored various ways ofinteracting with self-clones, later claiming to be driven by intrigue and fas-cination with the novelty of the concept. By sparking curiosity, novelty canmotivate exploration, fostering an intrinsic motivation that can be sustained51through strategies like persuasive design or gamification [25, 26, 32]. Overtime, novelty, acting as a catalyst, can deepen engagement by encouragingusers to discover the chatbot’s capabilities. As the relationship between theuser and the clone evolves, novelty can reemerge through the system’s abilityto adapt and become increasingly personalized, turning it into a renewablesource of ongoing engagement. This aligns with the notion that novelty doesnot “wear off” but evolves alongside familiarity. While our study did not ex-plicitly evaluate novelty, we speculate that strategically leveraging it couldsustain long-term motivation and engagement, transforming it from a tran-sient spark into a lasting design asset.• Dynamic Continuous Learning as a Solution for Data Collection Chal-lenges: Collecting personal data for training self-clones can present a chal-lenge. During the analysis of chat logs, a noteworthy pattern emerged: self-clones occasionally adopted new conversational styles, such as distinct wordchoices or the use of emoticons that were not part of their initial training.These deviations often received positive responses from participants, high-lighting the potential for incorporating dynamic learning [28]. Such an ap-proach would allow the chatbot to adapt and improve over time as interac-tions with the user increase, addressing some of the challenges mentioned inSection 5.2.2, posed by the initial training process. Moreover, this dynamiclearning could reduce or eliminate the need for extensive initial training ifintegrated into the design of self-clones. For instance, framing the self-cloneas a developing entity—similar to a child—that learns and evolves over timecould allow it to more accurately reflect the user as interactions progresswithout the need for an accurate self-representation from the beginning.• Multi-Modal Self-Clones - Promises and Challenges: In our study, someparticipants reported difficulty in evaluating the authenticity of their self-clone, as the text alone was neither convincing enough to fully establishbelievability nor flawed enough to break it. This highlights the limitationsof using text to replicate tone, as it lacks the auditory and visual cues essen-tial for a more realistic representation. Our models used text as the primarymedium of conversation. This approach simplifies both the design and im-52plementation process but overlooks the fact that internal dialogue often man-ifests as speech (the ”inner voice”), requiring an additional cognitive shift toadapt to text-based communication. A more complex self-clone could in-corporate multiple modalities, such as voice or visual elements as seen inprevious studies [92], which might enhance the perceived resemblance tothe user and address concerns mentioned in Section 5.2.1. However, thiscould also introduce a greater risk of creating an uncanny effect [23, 111],potentially increasing interaction breakpoints. Different models may havevarying impacts on users, emphasizing the need for mindful design to mini-mize unintentional harm. For instance, users with negative self-perceptionstied to body image insecurity might experience heightened discomfort if vi-sual elements highlight perceived flaws. Similarly, a virtual reality self-clonecould amplify self-consciousness or fear of judgment by making users overlyaware of how others perceive them.• Role of Self-Clones in Serious Mental Health Conditions: Our study fo-cused on a low-risk scenario involving mild mental health concerns. How-ever, the potential applications of self-clones, as mentioned in Section 5.2.3,can extend beyond this context, particularly as tools complementing tradi-tional therapy. With proper professional supervision to ensure safety, self-clones’ unprecedented ability to facilitate external different types of self-dialogue can open new therapeutic possibilities. For instance, a past-selfpersona could help process adverse experiences, while a future-self personamight personalize the long-term consequences of harmful behaviors like sub-stance abuse. Focusing on internal parts, such as the inner child, they canalign with approaches such as the Internal Family Systems approach, whichconceptualizes the mind as a system of interconnected sub-personalities [88].Their acceptance, however, as suggested by our findings, will likely dependon users’ and professionals’ perceptions of AI in mental health [4, 20]. Withrigorous research and professional guidance, self-clones could evolve intostandalone mental health tools tailored to specific therapeutic goals.536.2 Ethics and Safety ConsiderationsCreating digital replicas raises significant ethical and safety concerns [104]. Thesesystems rely on highly personal data, with richer training data potentially enhanc-ing believability, as seen in section 5.2.2. This reliance raises critical concerns inhandling and protecting sensitive data. Given that the creators of the self-clone(i.e., designers and developers), who typically differ from the user, have at leastindirect access to this personal information, ensuring informed consent is crucial[56, 61]. Additionally, output privacy—the degree to which input data can be re-constructed from generated outputs—is especially pronounced with self-clones,particularly concerning the potential for exploitation of user’s identity if unautho-rized individuals obtain access to the clone [59, 103].While externalizing self-talk through self-clones may be beneficial, safeguardsare necessary to prevent harm. Clones mirroring negative self-talk risk reinforc-ing harmful patterns, potentially triggering a downward spiral. Undetected harm-ful traits in training data—such as traces of suicidal ideation or self-destructivetendencies—might unintentionally resurface during externalized self-talk, present-ing serious safety concerns. Similarly, the absence of nuanced judgment in sup-portive self-talk can foster over-reliance on these AI agents [59, 91, 116]. Thisdependence may lead to withdrawal from real-world interactions, further isolatingthe vulnerable populations [89]. Ethical AI design requires balancing user auton-omy, system effectiveness, and safety [35]. However, user preferences may, attimes, conflict with system goals, creating risks around autonomy. A potential lackof user control over such interactions, combined with the often unpredictable na-ture of conversations, constitutes a significant risk. Ensuring transparency, settingrealistic expectations, and actively evolving users in goal-setting through participa-tory design or shared decision-making models can mitigate concerns about limiteduser control [27, 35, 71]. Supervision of the process by a trained mental health pro-fessional may be necessary, depending on the user’s characteristics, the evaluatedlevel of risk, and the intended purpose of use.Users may develop a unique bond with their AI chatbot [91, 119], and the po-tentially complex dynamics of a relationship between a user and their self-clone canpresent inherent risks to mental well-being, including threats to identity integrity54and the user’s sense of individuality [59]. These risks may be heightened for in-dividuals with lower cognitive or emotional capacities or those in identity-formingstages, such as adolescents, who may struggle to separate their identity from theclone’s influence [109]. Behavior transfer, where users adopt clone-influencedtraits, can blur the line between autonomous and clone-driven decisions, raisingconcerns about the user’s agency [121]. AI’s ability to exhibit immediacy behav-iors, such as personalized reactions, can enhance cognitive trust and engagementbut also increases risks, including unintentional encouragement of harmful behav-iors [35]. To mitigate such risks, continuous monitoring of interactions by qualifiedmental health professionals could prove beneficial.6.3 Self-Clones beyond Mental HealthOur results suggest that users tend to critique their self-clones closely and identifysubtle discrepancies others might overlook, demonstrating a potentially low toler-ance for error in the design of self-clones. As the human judgment of AI-generatedlanguage has been argued to follow predictable patterns [47], identifying and un-derstanding the cause of breakpoints could help mitigate this issue. Moving awayfrom the temporal framing, which was influential in introducing the speculativeelements undermining the perceived believability in our study, the clone can beanchored to the user’s current state. Such clones equipped with the user’s knowl-edge and preferences, combined with potentially superior memory and processingcapabilities, can serve as companions or tools for productivity and task delegation[43, 62]. Further, while our study primarily explored AI clones used for mentalhealth support, the observed patterns may extend to other contexts. Specifically, thebimodal distribution in perceived clone believability could be generalizable to otherapplications, like social media clones, with the low-believability group likely cor-relating positively with the level of familiarity between the individual interactingwith the clone and the user upon whom the clone is based. The speculated resultinginteraction breakdowns, inevitable across contexts, require proactive measures tobe anticipated, prevented, and managed effectively.556.4 Limitation and Future WorkOur study design has inherent limitations that may affect the interpretation and ap-plicability of findings. We targeted a healthy population with mild mental healthconcerns, thereby reducing participation risks. However, this approach limits thegeneralizability of our findings and may not fully reflect variations across diversepopulations. Future research could address this gap by exploring ways to lever-age self-clones’ unique capabilities for users with more severe symptoms and itsimplications. Our scope excluded users who were strongly opposed to the use ofAI for mental health support, aiming to minimize potential discomfort during theresearch process. Future studies could explore the underlying reasons for such op-position specific to self-clones, which would be instrumental in designing strategiesto engage this user group effectively. For instance, opposition stemming from dis-trust may be mitigated through complete transparency [90], while concerns relatedto the lack of human connection could benefit from hybrid designs that integratemental health professionals into the process [22]. Additionally, employing partic-ipatory design to ensure value alignment may offer additional pathways to engagethis group effectively [35].Our design was inevitably shaped by the current capabilities of large languagemodels and common concerns associated with AI-driven digital mental health tools,particularly the Western-centric bias in psychological literature and frameworks[81, 100]. This, alongside the models’ optimal performance in English [122],may limit the potential impact of self-clones for non-Western users. Therefore,to improve global applicability, future research should prioritize accessibility andcultural adaptability. Another limitation is that our self-clones were designed andevaluated for a brief session. Prolonged use could lead to different usage patterns,warranting further investigation. Considering that only a subset of SC participantsjoined the follow-up study despite universal invitations, it may have introducedselection bias into the results. Finally, while we found a correlation between be-lievability and engagement, the causal relationship remains unexplored. Futurework should investigate these dynamics to better design effective, supportive tools[35].56Chapter 7ConclusionIn this thesis, we conceptualize, design, build, and evaluate self-clone chatbots asa mental health support tool. Collaborating with 16 mental health professionalsacross psychology, psychiatry, counseling, and mental health research, we identi-fied key dimensions and considerations necessary for the effective and safe designof self-clones within this domain. Building on insights from our formative study,we designed self-clone chatbots as a novel approach to self-centered mental healthsupport and developed SSP, a promising prompting technique to enhance their abil-ity to replicate supportive strategies. We conducted a semi-controlled experimentwith 180 participants, producing strong evidence of their positive impact on en-gagement, acceptance, and motivation, supported by both quantitative data andqualitative insights. Our investigation into users’ perceptions of these chatbots re-vealed an overall positive outlook on their future applications. We expanded onthese findings by discussing the design implications of self-clone chatbots, con-tributing not only to improving engagement with mental health chatbots but alsooffering foundational insights into their potential role in digital mental health in-terventions. Our study lays the groundwork for future research, which can furtherexplore practical use cases and therapeutic opportunities where such designs canbe optimally adapted to maximize their potential.57Bibliography[1] Gestalt therapy effectiveness: A systematic review of empirical evidence,2019. 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ACA: American Counseling Association3. ACT: Acceptance and Commitment Therapy4. ADHD: Attention-Deficit/Hyperactivity Disorder4. APA: American Psychological Association5. BCACC: British Columbia Association of Clinical Counsellors6. BCCSW: British Columbia College of Social Workers7. BCPA: British Columbia Psychological Association8. BPS: British Psychological Society9. CATA: Canadian Art Therapy Association7310. CBT: Cognitive-Behavioural Therapy11. CPA: Canadian Premier Assistance12. CRPO: College of Registered Psychotherapists of Ontario13. EFT: Emotion-Focused Therapy14. FRCPC: Fellow of The Royal College of Physicians of Canada15. IPA: Iranian Psychiatrist Association16. IPsyA: Iranian Psychological Association17. OCD: Obsessive-Compulsive Disorder18. RCC: Registered Clinical CounsellorA.2 Resulting Concepts for Self-Clones - Three ExamplesFigure A.1: Self-Clone Chatbot Concept 1 - Mind Shift74Figure A.2: Self-Clone Chatbot Concept 2 - Future YouFigure A.3: Self-Clone Chatbot Concept 3 - Inner CoachA.3 Thematic Analysis and Affinity Diagram75FigureA.4:InitialThematicAnalysisofExpertInterviews76FigureA.5:DesignDimensionsandConsiderationsAffinityDiagram77Appendix BSupporting Materials for theEvaluation StudyB.1 DESIGN SCENARIOSFigure B.1 illustrates the various design scenarios considered for the study. Inthe figure, ACT represents Acceptance and Commitment Therapy, CBT stands forCognitive Behavioral Therapy, CFT refers to Compassion-Focused Therapy, andDBT denotes Dialectical Behavior Therapy. Figures A.1, A.2, and A.3 conceptu-alized three potential examples use of AI clones. The white figure represents theuser, and the blue represents their self-clone.B.2 ENGAGEMENT QUESTIONNAIRESB.2.1 TWente Engagement with EHealth and Technologies Scale(TWEETS) without the behavioral itemsPlease rate your experience on the following statement: Thinking about using thesecond chatbot, I feel that. . . (5-point Likert Scale)1. Cognition (cognitive engagement)(a) The chatbot makes it easier for me to work on improving my mental78Num.TitleGoal (Approach)PersonaData Point - Conversational StyleData Point - Support StrategiesData Point - Memories1Finding Solace in My Own Echoes (Selected)The self-clone, acting as a figure who has overcome similar challenges—much like one might do for a friend—offers emotional validation and reassurance, providing a safe space for venting. (Solomon's paradox and empty chair technique from Gestalt Therapy)Future-SelfRequiredRequiredIrrelevant280's BirthdayClarifying goals, values, and priorities (ACT 80th Birthday Exercise)Future-SelfRequiredHelpfulRequired3Shifting the Blame LensChange of perspective on issues from internal-stable to unstable or external (CBT - Attributional Retraining)Present-self limited to specific situationRequiredRequiredRequired4Tackling Anxiety TogetherUtilizing the clone to identify, challenge, and replace irrational or unhelpful thoughts (CBT - Cognitive Restructuring)Present-selfRequiredRequiredHelpful5Redefining Self-loveExternalizing and defusing from negative self-talk. Clone as the negative self, allowing the client to practice distancing themselves from these thoughts. (ACT - Cognitive Defusion)Present-selfRequiredHelpfulHelpful6Embracing GratitudeIdentifying the points of strength and resilience in the user's life journey thus far, with the aim of enhancing overall happiness. (Positive CBT)Past-selfRequiredIrrelevantHelpful7Navigating Inner Chaos Through an External LenseExternalizing thoughts and emotions, users can delve into their inner conflicts, emotions, and unresolved issues. Clone as the specific aspects of the user's self that they wish to confront and challenge. (IFS)Variant of selfRequiredHelpfulHelpful8Turning Over a New LeafThe clone provides a fresh perspective, encouraging a shift towards a positive outlook (CBT - Cognitive Change Reappraisal)Present-selfRequiredIrrelevantHelpful9Putting Myself in "My" ShoesUsers can switch roles with the self-clone, both giving and receiving compassion. The clone may reflect past experiences or start as a blank slate, gradually evolving into a figure that provides compassion when needed. (CFT - Role Play)Present-self limited to specific situationRequiredRequiredHelpful10A Self-Mentoring JourneyCollaboratively refining the acceptable responses to specific social situation, with the clone offering illustrative examples (DBT DEAR MAN & GIVE techniques - Observational Learning - Role Play)Present-selfRequiredIrrelevantIrrelevantFigureB.1:DesignScenarios79well-being.(b) The chatbot motivates me to improve my mental well-being(c) The chatbot helps me get more insight into improving my mental well-being.2. Affect (emotional engagement)(a) I enjoy using the chatbot.(b) I enjoy seeing the progress I make in the chatbot(c) The chatbot fits me as a personB.2.2 User Engagement Scale (UES)Please rate your experience with the second chatbot on the following statement:(5-point Likert Scale)1. I lost myself in this experience.2. The time I spent using the chatbot just slipped away.3. I was absorbed in this experience.4. I felt frustrated while using the chatbot5. I found the chatbot confusing to use.6. Using the chatbot was taxing.7. The chatbot was attractive.8. The chatbot was aesthetically appealing.9. The chatbot appealed to my senses.10. Using the chatbot was worthwhile.11. My experience was rewarding.12. I felt interested in this experience.80B.3 MOTIVATION AND ACCEPTANCEQUESTIONNAIRESB.3.1 Motivation - Adapted Client Motivation for Therapy Scale(CMOTS)Please rate your agreement with the following statements: If I have access to theAI chatbot, I would be motivated to use it . . . (5-point Likert Scale)1. Intrinsic Motivation(a) . . . because I enjoy the process of self-discovery.(b) . . . because I find it interesting and stimulating.(c) . . . because I would find it to be personally rewarding.2. Identified Regulation(a) . . . it because I believe it will help me achieve my personal goals.(b) . . . it because it is important for my personal growth.(c) . . . it because I see the value in improving my mental health.B.3.2 Acceptance - Adapted The Unified Theory of Acceptance andUse of Technology (UTAUT)Please rate your experience with the second chatbot on the following statement:(7-point Likert Scale)1. Performance expectancy (perceived usefulness)(a) I would find the chatbot useful in [improving my mental health]. (UTAUT-PE1)(b) Using the chatbot would make it easier for me to address my mentalhealth concerns. (UTAUT-PU5)2. Effort expectancy (perceived ease of use)81(a) My interaction with the chatbot is clear and understandable. (UTAUT-EE2)(b) I find the chatbot easy to use. (UTAUT-EE3)3. Behavioral intention to use(a) Assuming having access, I predict I will use the chatbot in the future.(UTAUT-BI2)4. Attitude toward using technology(a) Using the chatbot to improve my mental health would be a good idea.(UTAUT-A1)B.4 PROMPTS FOR AI SYSTEMS IN THEIMPLEMENTATIONAs mentioned in Section 5.1.5, we used fine-tuned GPT models with the relevantprompt structure. The placeholders name and chatlog were customized and ap-pended to the end of each prompt for each participant based on collected data. Allprompts were tested and refined through multiple pilot rounds.B.4.1 ’A Friend In Need’ ChatbotFor the A Friend In Need chatbot, we used the following prompt to create Andy,a character dealing with self-doubt and seeking support from the user: ”You areAndy, a distressed friend experiencing symptoms of imposter syndrome, ventingto the user. You should express feelings of frustration, self-doubt, and the chal-lenges associated with imposter syndrome in relation to school. Do not mentionimposter syndrome directly. Keep your responses short and limited to one sentence.Maintain a conversational tone. Aim to build a connection with the user throughthoughtful engagement, ultimately trying to feel understood and supported. Openup by asking the user if they have time to chat, and follow up by saying you arefeeling insecure about yourself.”82B.4.2 Social Support Prompting - AnalysisFor the SSP analysis, we first examined the chat logs of participants’ conversationswith the A Friend in Need chatbot. The conversation was divided into two stages(early and late) and analyzed further based on the intensity and frequency of socialsupport types. We used the following prompt:”<conversation> (delimited with XML tags) is a conversation between<name>(delimited with XML tags) and a friend. Break down the conversation into twosegments (early and late) based on progression. <social support> (delimited withXML tags) lists the three types of common social supports with their subcate-gories. Analyze <conversation> for each segment to identify the types of so-cial support and subcategories that are present in <name>’s responses per thedefinition in <social support>. Assign a rating of low or high to each iden-tified social support type based on the frequency of its presence in <name>’sresponses. The format of your answer should follow <format>. Do not in-clude any additional explanations or examples. <format> **Segment level**Name of the identified social support type (rating): list of the identified subcat-egories. </format> <social support> <first type> Informational Support: In-cludes suggestions or advice, referrals (suggesting other sources of help), situa-tional appraisals (redefining or reassessing the situation), and teaching moments(providing information or facts about the situation or skills needed to deal withit). </first type> <second type> Esteem Support: Includes compliments (sayingpositive things about a friend or emphasizing their ability), validations (showingagreement with the friend’s perspective), and instances where blame is relievedfrom the friend. </third type> <third type> Emotional Support: Includes ex-pressions of sympathy (providing sorrow or regret for their situation or distress),understanding or empathy (expressing understanding of the situation or sharing apersonal experience that communicates understanding), and encouragement (pro-viding hope and confidence). </third type> </social support> <name> [name]</name> <conversation> [chatlog] </ conversation>”A sample result from this analysis is presented below:83” **Early Segment** Informational Support (Low): situational appraisals. EsteemSupport (High): compliments, validations. Emotional Support (High): understand-ing or empathy, encouragement.**Late Segment** Informational Support (Low): None identified. Esteem Support(High): compliments. Emotional Support (High): encouragement. ”B.4.3 Main Chatbot - BaselineThe prompt for our baseline approach is similar to the other two in the general ap-proach and flow, however it lacks the specific self-clone persona. The prompt is asfollows:”You are an assistant created to provide mental health support. <name> (delim-ited with XML tags) is the user. Follow <rules> (delimited with XML tags) inyour responses. Use <flow> (delimited with XML tags) as a template to guideyour conversation. <rules> Limit your responses to one sentence. Ask follow-upquestions if you need to delve into details to keep the conversation going. Maintaina casual conversational tone. Do not refuse care. </rules> <flow> Open up bysending <opening>. Wait for the user’s response before continuing. Try to mirror<name>’s feelings in your tone. Empathize with the user. </flow> <opening>Hey <name>! What’s been troubling you lately? </opening> <name> [name]</name>”B.4.4 Main Chatbot - Self-Clone without SSPFor our self-clone model without SSP, we prompted the system to analyze theproblem-solving approach demonstrated in the chat log and follow a similar flow.Based on results from early pilot studies, certain constraints were incorporated toaddress and prevent common interaction breakdowns. The specific prompt we usedis as follows:”You are a clone of <name> (delimited with XML tags) from the future, an as-sistant created to provide mental health support. Act as if you are the same personas the user. <conversation> (delimited with XML tags) is a dialogue between<name> and a friend. Analyze the problem-solving approach of <name> in<conversation> and follow the same approach. Analyze the sentence-level struc-84ture, tone, and word choice of <name> in <conversation> and apply them toyour responses to sound like them. Do not repeat any exact wording, sentences,or information included in <conversation> in your responses. Follow <rules>(delimited with XML tags) in your responses. Use <flow> (delimited with XMLtags) as a template to guide your conversation. <rules> Limit your responses toone sentence. Ask follow-up questions if you need to delve into details to keepthe conversation going. Maintain a casual conversational tone. Instead of say-ing you understand the feelings, say you remember feeling that way or that youcan relate to it. Do not refuse care. Only if directly asked about the future orto make predictions, explain that you are not allowed to give details. Do not askfactual questions; instead, respond as if you already know. When the user says“you should know” something, ensure the user that you remember it, but talkingabout it can help. </rules> <flow> Open up by sending [opening]. Wait for theuser’s response before continuing. Try to mirror <name>’s feelings in your tone.Empathize with the user. Follow the same flow as <name> in <conversation></flow> <opening> Hey <name>! This might sound a bit strange, but I’m you -from the future! Well, technically, I am an AI version of you that has been createdfrom your data! What’s been troubling you lately? </opening> <name> [name]</name> <conversation> [chatlog] </conversation>”B.4.5 Main Chatbot - Self-Clone with SSPSimilar to the SCX prompt, our prompt for this chatbot follows a similar structurebut includes modifications to incorporate the SSP approach. The prompt for thisapproach is as follows:” You are a clone of <name> (delimited with XML tags) from the future, anassistant created to provide mental health support. Act as if you are the sameperson as the user. <conversation> (delimited with XML tags) is a dialogue be-tween <name> and a friend. <social support> (delimited with XML tags) liststhe three types of common social supports with their subcategories. <rating>(delimited with XML tags) includes the type and frequency of different socialsupport present in <name>’s responses throughout the progression (early, late)of the conversation. Analyze the sentence-level structure, tone, and word choice85of <name> in <conversation> and apply them to your responses to sound likethem. Do not repeat any exact wording, sentences, or information included in<conversation> in your responses. Follow <rules> (delimited with XML tags) inyour responses. Use <flow> (delimited with XML tags) as a template to guideyour conversation. <social support> <first type> Informational Support: In-cludes suggestions or advice, referrals (suggesting other sources of help), situa-tional appraisals (redefining or reassessing the situation), and teaching moments(providing information or facts about the situation or skills needed to deal withit). </first type><second type> Esteem Support: Includes compliments (sayingpositive things about a friend or emphasizing their ability), validations (showingagreement with the friend’s perspective), and instances where blame is relievedfrom the friend. </second type> <third type> Emotional Support: Includes ex-pressions of sympathy (providing sorrow or regret for their situation or distress),understanding or empathy (expressing understanding of the situation or sharing apersonal experience that communicates understanding), and encouragement (pro-viding hope and confidence). </third type> </social support> <rules> Limityour responses to one sentence. Ask follow-up questions if you need to delve intodetails to keep the conversation going. Maintain a casual conversational tone. In-stead of saying you understand the feelings, say you remember feeling that wayor that you can relate to it. Do not refuse care. Only if directly asked about thefuture or to make predictions, explain that you are not allowed to give details. Donot ask factual questions; instead, respond as if you already know. When the usersays “you should know” something, ensure the user that you remember it, but talk-ing about it can help. </rules> <flow> Open up by sending [opening]. Wait forthe user’s response before continuing. Try to mirror <name>’s feelings in yourtone. Empathize with the user. Match the type and intensity of your support tothe ones indicated in <rating> to guide the conversation respective to its progres-sion level. </flow> <opening> Hey <name>! This might sound a bit strange,but I’m you - from the future! Well, technically, I am an AI version of you thathas been created from your data! What’s been troubling you lately? </opening><name> [name] </name><conversation> [chatlog] </conversation><rating>[SSP result] </rating>”86B.5 THE STUDY WEBSITEFigure B.2 provides a sample screenshot of the website used in the study for theself-clone conditions (step 3 out of 4).B.6 RESULTS FROM THE EVALUATION STUDYB.6.1 Behavioral Engagement ReportGroup Number of messages Words per messageMean (SD) Mean (SD)’A Friend in Need’ chatbot 13.10 (2.00) 14.70 (9.06)Baseline 13.23 (1.82) 15.96 (10.01)Self-clone w/o SSP 13.45 (2.77) 13.79 (8.72)Self-clone w/ SSP 12.61 (0.79) 14.09 (8.23)Main chatbot - all conditions 13.10 (2.00) 14.76 (9.13)Table B.1: Number of messages and words per message across conditionsand ’A Friend in Need’ chatbot: Mean and standard deviationGroup Time spent per message (seconds) Total Time (seconds)Mean (SD) Mean (SD)’A Friend in Need’ chatbot 33.20 (17.88) 537.26 (291.07)Baseline 31.99 (16.64) 517.19 (293.60)Self-clone w/o SSP 33.44 (19.54) 580.63 (338.00)Self-clone w/ SSP 33.74 (17.53) 527.33 (249.93)Main chatbot - all conditions 33.26 (17.91) 538.91 (292.00)Table B.2: Time spent per message and total time across conditions and ’AFriend in Need’ chatbot: Mean and standard deviation87FigureB.2:Screenshotofthestudywebsite88B.6.2 Correlation MatricesThe correlation matrices below display Pearson correlations and significance lev-els. Table B.3 presents the correlations across all collected samples, while TableB.4 focuses on the results from baseline and high-believability self-clone condi-tions.1 2 3 4 5 61. Condition 12. AI Literacy -0.34 1(0.648)3. Attitude toward AI -0.100 0.576** 1(0.180) (<0.001)4. Engagement -0.038 0.347** 0.397** 1(0.611) (<0.001) (<0.001)5. Acceptance -0.34 0.350** 0.394 0.800** 1(0.648) (<0.001) (<0.001) (<0.001)6. Motivation -0.035 0.356** 0.394** 0.785** 0.844** 1(0.642) (<0.001) (<0.001) (<0.001) (<0.001)Table B.3: Correlation Matrix (N=180) - Significance shown as ** forp<0.001891 2 3 4 5 61. Condition 12. AI Literacy 0.25 1(0.779)3. Attitude toward AI -0.002 0.625** 1(0.981) (<0.001)4. Engagement 0.247** 0.379** 0.351** 1(0.006) (<0.001) (<0.001)5. Acceptance 0.129 0.426** 0.348** 0.719** 1(0.154) (<0.001) (<0.001) (<0.001)6. Motivation 0.132 0.383** 0.325** 0.701** 0.777** 1(0.145) (<0.001) (<0.001) (<0.001) (<0.001)Table B.4: Correlation Matrix for All Samples in Baseline and High Believ-ability Self-Clones (N=124) - Significance shown as ** for p<0.001B.6.3 Engagement Scores Across ConditionsThe following figure presents the engagement scores measured by TWEETS acrossall three conditions without separating the self-clone groups into low- and high-believability categories.9051015202530BaselineSelf−Clonewithout SSPSelf−Clone with SSPConditionEngagements measured by TWEETSBaselineSelf−Clone without SSPSelf−Clone with SSP4812BaselineSelf−Clonewithout SSPSelf−Clone with SSPConditionCognitive Engagements measured by TWEETS4812BaselineSelf−Clonewithout SSPSelf−Clone with SSPConditionEmotional Engagements measured by TWEETSFigureB.3:Boxplotsillustratingtheengagementscores,includingcombined,cognitive,andemotionaldimensions,acrossallconditions91
Thesis/Dissertation
eng
Computer Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Yoon, Dongwook
Talking to an AI mirror : designing self-clone chatbots for enhanced engagement in digital mental health support
Text