23,367 research outputs found

    Fostering reflection in the training of speech-receptive action

    Get PDF
    Dieser Aufsatz erรถrtert Mรถglichkeiten und Probleme der Fรถrderung kommunikativer Fertigkeiten durch die Unterstรผtzung der Reflexion eigenen sprachrezeptiven Handelns und des Einsatzes von computerunterstรผtzten Lernumgebungen fรผr dessen Fรถrderung. Kommunikationstrainings widmen sich meistens der Fรถrderung des beobachtbaren sprachproduktiven Handelns (Sprechen). Die individuellen kognitiven Prozesse, die dem sprachrezeptiven Handeln (Hรถren und Verstehen) zugrunde liegen, werden hรคufig vernachlรคssigt. Dies wird dadurch begrรผndet, dass sprachrezeptives Handeln in einer kommunikativen Situation nur schwer zugรคnglich und die Fรถrderung der individuellen Prozesse sprachrezeptiven Handelns sehr zeitaufwรคndig ist. Das zentrale Lernprinzip - die Reflexion des eigenen sprachlich-kommunikativen Handelns - wird aus verschiedenen Perspektiven diskutiert. Vor dem Hintergrund der Reflexionsmodelle wird die computerunterstรผtzte Lernumgebung CaiManยฉ vorgestellt und beschrieben. Daran anschlieรŸend werden sieben Erfolgsfaktoren aus der empirischen Forschung zur Lernumgebung CaiManยฉ abgeleitet. Der Artikel endet mit der Vorstellung von zwei empirischen Studien, die Mรถglichkeiten der Reflexionsunterstรผtzung untersucheThis article discusses the training of communicative skills by fostering the reflection of speech-receptive action and the opportunities for using software for this purpose. Most frameworks for the training of communicative behavior focus on fostering the observable speech-productive action (i.e. speaking); the individual cognitive processes underlying speech-receptive action (hearing and understanding utterances) are often neglected. Computer-supported learning environments employed as cognitive tools can help to foster speech-receptive action. Seven success factors for the integration of software into the training of soft skills have been derived from empirical research. The computer-supported learning environment CaiManยฉ based on these ideas is presented. One central learning principle in this learning environment reflection of one's own action will be discussed from different perspectives. The article concludes with two empirical studies examining opportunities to foster reflecti

    Alexa as an Active Listener: How Backchanneling Can Elicit Self-Disclosure and Promote User Experience

    Full text link
    Active listening is a well-known skill applied in human communication to build intimacy and elicit self-disclosure to support a wide variety of cooperative tasks. When applied to conversational UIs, active listening from machines can also elicit greater self-disclosure by signaling to the users that they are being heard, which can have positive outcomes. However, it takes considerable engineering effort and training to embed active listening skills in machines at scale, given the need to personalize active-listening cues to individual users and their specific utterances. A more generic solution is needed given the increasing use of conversational agents, especially by the growing number of socially isolated individuals. With this in mind, we developed an Amazon Alexa skill that provides privacy-preserving and pseudo-random backchanneling to indicate active listening. User study (N = 40) data show that backchanneling improves perceived degree of active listening by smart speakers. It also results in more emotional disclosure, with participants using more positive words. Perception of smart speakers as active listeners is positively associated with perceived emotional support. Interview data corroborate the feasibility of using smart speakers to provide emotional support. These findings have important implications for smart speaker interaction design in several domains of cooperative work and social computing.Comment: To appear in Proceedings of the ACM on Human-Computer Interaction (PACM HCI). The paper will be presented in CSCW 2022 (https://cscw.acm.org/2022

    Examination of Eco-Behavioral Assessments Designed for Understanding Complex Behaviors and Environments.

    Get PDF
    Second-generation intervention research requires methods for overcoming challenges to understanding complex learning ecologies and interactions of students. Eco-behavioral assessments (EBAs) are one solution to past intervention research challenges. EBAs record the effects of ecological variables in studentsโ€™ behavior and daily interactions. The utility of EBAs in second-generation research has increased substantially. Numerous EBAs now exist for use with all ages of learners and provide a valid, reliable, and cost effective method for intervention research. This paper examines 18 EBAs as well as software systems designed to support and enhance the use of EBAs. The examination serves as a comprehensive resource to better understand how EBAs can be used in answering complex questions about studentsโ€™ learning and for advancing second-generation research

    THE ROLE OF ENGLISH COMMUNICATION COMPETENCE TOWARD STAR HOTEL STAFFSโ€™ WORKING ACHIEVEMENT IN PALEMBANG

    Get PDF
    Considering the implementation of ASEAN Economic Community and Asian Games 2018, potential hotels are urged to prepare qualified and competitive human resource. Language is believed as a means of communication among people involved in these international events. People around the world would stay at certain hotel, and English communication competence might relate the hotel guests and the employees as service provider agent. This study aims to excavate the role of English communication competence toward employeesโ€™ working achievement. About three hundred and fifty-three employes from forty-four different star hotels in Palembang were involved as the sample. The data was collected by administering two kinds of instruments, namely speaking test (to obtain the condition of their communication competence) and questionnaires concerning superiorโ€™s judgement on employeesโ€™ working achievement. The results were obtained through the descriptive, percentage, and statistical analysis which was aimed to find out the significant influence of English communication competence toward employee achievement. The findings showed that there was positive influence of English communication competence toward employeesโ€™ achievement

    Influence of Selected Factors on a Counselor\u27s Attention Level to and Counseling Performance with a Virtual Human in a Virtual Counseling Session

    Get PDF
    Virtual humans serve as role-players in social skills training environments simulating situational face-to-face conversations. Previous research indicates that virtual humans in instructional roles can increase a learner\u27s engagement and motivation towards the training. Left unaddressed is if the learner is looking at the virtual human as one would in a human-to-human, face-to-face interaction. Using a modified version of the Emergent Leader Immersive Training Environment (ELITE-Lite), this study tracks visual attention and other behavior of 120 counselor trainees counseling a virtual human role-playing counselee. Specific study elements include: (1) the counselor\u27s level of visual attention toward the virtual counselee; (2) how changes to the counselor\u27s viewpoint may influence the counselor\u27s visual focus; and (3) how levels of the virtual human\u27s behavior may influence the counselor\u27s visual focus. Secondary considerations include aspects of learner performance, acceptance of the virtual human, and impacts of age and rank. Result highlights indicate that counselor visual attentional behavior could be separated into two phases: when the virtual human was speaking and when not speaking. When the virtual human is speaking, the counselor\u27s primary visual attention is on the counselee, but is also split toward pre-scripted responses required for the training session. During the non-speaking phase, the counselor\u27s visual focus was on pre-scripted responses required for training. Some of the other findings included that participants did not consider this to be like a conversation with a human, but they indicated acceptance of the virtual human as a partner with the training environment and they considered the simulation to be a useful experience. Additionally, the research indicates behavior may differ due to age or rank. Future study and design considerations for enhancements to social skills training environments are provided

    ์ •์‹ ๊ฑด๊ฐ•์—์„œ ์‚ฌ์šฉ์ž ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์™€ ์ž์•„์„ฑ์ฐฐ์„ ์ง€์›ํ•˜๋Š” ๋Œ€ํ™”ํ˜• ์—์ด์ „ํŠธ ๋””์ž์ธ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2020. 8. ์„œ๋ด‰์›.In the advent of artificial intelligence (AI), we are surrounded by technological gadgets, devices and intelligent personal assistant (IPAs) that voluntarily take care of our home, work and social networks. They help us manage our life for the better, or at least that is what they are designed for. As a matter of fact, few are, however, designed to help us grapple with the thoughts and feelings that often construct our living. In other words, technologies hardly help us think. How can they be designed to help us reflect on ourselves for the better? In the simplest terms, self-reflection refers to thinking deeply about oneself. When we think deeply about ourselves, there can be both positive and negative consequences. On the one hand, reflecting on ourselves can lead to a better self-understanding, helping us achieve life goals. On the other hand, we may fall into brooding and depression. The sad news is that the two are usually intertwined. The problem, then, is the irony that reflecting on oneself by oneself is not easy. To tackle this problem, this work aims to design technology in the form of a conversational agent, or a chatbot, to encourage a positive self-reflection. Chatbots are natural language interfaces that interact with users in text. They work at the tip of our hands as if SMS or instant messaging, from flight reservation and online shopping to news service and healthcare. There are even chatbot therapists offering psychotherapy on mobile. That machines can now talk to us creates an opportunity for designing a natural interaction that used to be humans own. This work constructs a two-dimensional design space for translating self-reflection into a human-chatbot interaction, with user self-disclosure and chatbot guidance. Users confess their thoughts and feelings to the bot, and the bot is to guide them in the scaffolding process. Previous work has established an extensive line of research on the therapeutic effect of emotional disclosure. In HCI, reflection design has posited the need for guidance, e.g. scaffolding users thoughts, rather than assuming their ability to reflect in a constructive manner. The design space illustrates different reflection processes depending on the levels of user disclosure and bot guidance. Existing reflection technologies have most commonly provided minimal levels of disclosure and guidance, and healthcare technologies the opposite. It is the aim of this work to investigate the less explored space by designing chatbots called Bonobot and Diarybot. Bonobot differentiates itself from other bot interventions in that it only motivates the idea of change rather than direct engagement. Diarybot is designed in two chat versions, Basic and Responsive, which create novel interactions for reflecting on a difficult life experience by explaining it to and exploring it with a chatbot. These chatbots are set up for a user study with 30 participants, to investigate the user experiences of and responses to design strategies. Based on the findings, challenges and opportunities from designing for chatbot-guided reflection are explored. The findings of this study are as follows. First, participants preferred Bonobots questions that prompted the idea of change. Its responses were also appreciated, but only when they conveyed accurate empathy. Thus questions, coupled with empathetic responses, could serve as a catalyst for disclosure and even a possible change of behavior, a motivational boost. Yet the chatbot-led interaction led to surged user expectations for the bot. Participants demanded more than just the guidance, such as solutions and even superhuman intelligence. Potential tradeoff between user engagement and autonomy in designing human-AI partnership is discussed. Unlike Bonobot, Diarybot was designed with less guidance to encourage users own narrative making. In both Diarybot chats, the presence of a bot could make it easier for participants to share the most difficult life experiences, compared to a no-chatbot writing condition. Yet an increased interaction with the bot in Responsive chat could lead to a better user engagement. On the contrary, more emotional expressiveness and ease of writing were observed with little interaction in Basic chat. Coupled with qualitative findings that reveal user preference for varied interactions and tendency to adapt to bot patterns, predictability and transparency of designing chatbot interaction are discussed in terms of managing user expectations in human-AI interaction. In sum, the findings of this study shed light on designing human-AI interaction. Chatbots can be a potential means of supporting guided disclosure on lifes most difficult experiences. Yet the interaction between a machine algorithm and an innate human cognition bears interesting questions for the HCI community, especially in terms of user autonomy, interface predictability, and design transparency. Discussing the notion of algorithmic affordances in AI agents, this work proposes meaning-making as novel interaction design metaphor: In the symbolic interaction via language, AI nudges users, which inspires and engages users in their pursuit of making sense of lifes agony. Not only does this metaphor respect user autonomy but also it maintains the veiled workings of AI from users for continued engagement. This work makes the following contributions. First, it designed and implemented chatbots that can provide guidance to encourage user narratives in self-reflection. Next, it offers empirical evidence on chatbot-guided disclosure and discusses implications for tensions and challenges in design. Finally, this work proposes meaning-making as a novel design metaphor. It calls for the responsible design of intelligent interfaces for positive reflection in pursuit of psychological wellbeing, highlighting algorithmic affordances and interpretive process of human-AI interaction.์ตœ๊ทผ ์ธ๊ณต์ง€๋Šฅ(Artificial Intelligence; AI) ๊ธฐ์ˆ ์€ ์šฐ๋ฆฌ ์‚ถ์˜ ๋ฉด๋ฉด์„ ๋งค์šฐ ๋น ๋ฅด๊ฒŒ ๋ฐ”๊ฟ”๋†“๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์• ํ”Œ์˜ ์‹œ๋ฆฌ(Siri)์™€ ๊ตฌ๊ธ€ ์–ด์‹œ์Šคํ„ดํŠธ (Google Assistant) ๋“ฑ ์ž์—ฐ์–ด ์ธํ„ฐํŽ˜์ด์Šค(natural language interfaces)์˜ ํ™•์žฅ์€ ๊ณง ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ์™€์˜ ๋Œ€ํ™”๊ฐ€ ์ธํ„ฐ๋ž™์…˜์˜ ์ฃผ์š” ์ˆ˜๋‹จ์ด ๋  ๊ฒƒ์ž„์„ ๋Šฅํžˆ ์ง์ž‘์ผ€ ํ•œ๋‹ค. ์‹ค์ƒ ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ๋Š” ์‹ค์ƒํ™œ์—์„œ ์ฝ˜ํ…์ธ  ์ถ”์ฒœ๊ณผ ์˜จ๋ผ์ธ ์‡ผํ•‘ ๋“ฑ ๋‹ค์–‘ํ•œ ์„œ๋น„์Šค๋ฅผ ์ œ๊ณตํ•˜๊ณ  ์žˆ์ง€๋งŒ, ์ด๋“ค์˜ ๋Œ€๋ถ€๋ถ„์€ ๊ณผ์—…-์ง€ํ–ฅ์ ์ด๋‹ค. ์ฆ‰ ์ธ๊ณต์ง€๋Šฅ์€ ์šฐ๋ฆฌ์˜ ์‚ถ์„ ํŽธ๋ฆฌํ•˜๊ฒŒ ํ•˜์ง€๋งŒ, ๊ณผ์—ฐ ํŽธ์•ˆํ•˜๊ฒŒ ํ•  ์ˆ˜ ์žˆ๋Š”๊ฐ€? ๋ณธ ์—ฐ๊ตฌ๋Š” ํŽธํ•˜์ง€๋งŒ ํŽธํ•˜์ง€ ์•Š์€ ํ˜„๋Œ€์ธ์„ ์œ„ํ•œ ๊ธฐ์ˆ ์˜ ์—ญํ• ์„ ๊ณ ๋ฏผํ•˜๋Š” ๋ฐ์—์„œ ์ถœ๋ฐœํ•œ๋‹ค. ์ž์•„์„ฑ์ฐฐ(self-reflection), ์ฆ‰ ์ž์‹ ์— ๋Œ€ํ•ด ๊นŠ์ด ์ƒ๊ฐํ•ด ๋ณด๋Š” ํ™œ๋™์€ ์ž๊ธฐ์ธ์‹๊ณผ ์ž๊ธฐ์ดํ•ด๋ฅผ ๋„๋ชจํ•˜๊ณ  ๋ฐฐ์›€๊ณผ ๋ชฉํ‘œ์˜์‹์„ ๊ณ ์ทจํ•˜๋Š” ๋“ฑ ๋ถ„์•ผ๋ฅผ ๋ง‰๋ก ํ•˜๊ณ  ๋„๋ฆฌ ์—ฐ๊ตฌ ๋ฐ ์ ์šฉ๋˜์–ด ์™”๋‹ค. ํ•˜์ง€๋งŒ ์ž์•„์„ฑ์ฐฐ์˜ ๊ฐ€์žฅ ํฐ ์–ด๋ ค์›€์€ ์Šค์Šค๋กœ ๊ฑด์„ค์ ์ธ ์„ฑ์ฐฐ์„ ๋„๋ชจํ•˜๊ธฐ ํž˜๋“ค๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ํŠนํžˆ, ๋ถ€์ •์ ์ธ ๊ฐ์ •์  ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์ž์•„์„ฑ์ฐฐ์€ ์ข…์ข… ์šฐ์šธ๊ฐ๊ณผ ๋ถˆ์•ˆ์„ ๋™๋ฐ˜ํ•œ๋‹ค. ๊ทน๋ณต์ด ํž˜๋“  ๊ฒฝ์šฐ ์ƒ๋‹ด ๋˜๋Š” ์น˜๋ฃŒ๋ฅผ ์ฐพ์„ ์ˆ˜ ์žˆ์ง€๋งŒ, ์‚ฌํšŒ์  ๋‚™์ธ๊ณผ ์žฃ๋Œ€์˜ ๋ถ€๋‹ด๊ฐ์œผ๋กœ ๊บผ๋ ค์ง€๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋‹ค์ˆ˜์ด๋‹ค. ์„ฑ์ฐฐ ๋””์ž์ธ(Reflection Design)์€ ์ธ๊ฐ„-์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ(HCI)์˜ ์˜ค๋žœ ํ™”๋‘๋กœ, ๊ทธ๋™์•ˆ ํšจ๊ณผ์ ์ธ ์„ฑ์ฐฐ์„ ๋„์šธ ์ˆ˜ ์žˆ๋Š” ๋””์ž์ธ ์ „๋žต๋“ค์ด ๋‹ค์ˆ˜ ์—ฐ๊ตฌ๋˜์–ด ์™”์ง€๋งŒ ๋Œ€๋ถ€๋ถ„ ๋‹ค์–‘ํ•œ ์‚ฌ์šฉ์ž ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ ์ „๋žต์„ ํ†ตํ•ด ๊ณผ๊ฑฐ ํšŒ์ƒ ๋ฐ ํ•ด์„์„ ๋•๋Š” ๋ฐ ๊ทธ์ณค๋‹ค. ์ตœ๊ทผ ์†Œ์œ„ ์ฑ—๋ด‡ ์ƒ๋‹ด์‚ฌ๊ฐ€ ๋“ฑ์žฅํ•˜์—ฌ ์‹ฌ๋ฆฌ์ƒ๋‹ด๊ณผ ์น˜๋ฃŒ ๋ถ„์•ผ์— ์ ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์ด ๋˜ํ•œ ์„ฑ์ฐฐ์„ ๋•๊ธฐ๋ณด๋‹ค๋Š” ํšจ์œจ์ ์ธ ์ฒ˜์น˜ ๋„๊ตฌ์— ๋จธ๋ฌด๋ฅด๊ณ  ์žˆ์„ ๋ฟ์ด๋‹ค. ์ฆ‰ ๊ธฐ์ˆ ์€ ์น˜๋ฃŒ ์ˆ˜๋‹จ์ด๊ฑฐ๋‚˜ ์„ฑ์ฐฐ์˜ ๋Œ€์ƒ์ด ๋˜์ง€๋งŒ, ๊ทธ ๊ณผ์ •์— ๊ฐœ์ž…ํ•˜๋Š” ๊ฒฝ์šฐ๋Š” ์ œํ•œ์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ๋Š” ์„ฑ์ฐฐ ๋™๋ฐ˜์ž๋กœ์„œ ๋Œ€ํ™”ํ˜• ์—์ด์ „ํŠธ์ธ ์ฑ—๋ด‡์„ ๋””์ž์ธํ•  ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ์ฑ—๋ด‡์˜ ์—ญํ• ์€ ์‚ฌ์šฉ์ž์˜ ๋ถ€์ •์ ์ธ ๊ฐ์ •์  ๊ฒฝํ—˜ ๋˜๋Š” ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•ด ์ด์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์šธ ๋ฟ ์•„๋‹ˆ๋ผ, ๊ทธ ๊ณผ์ •์—์„œ ๋ฐ˜์ถ”๋ฅผ ํ†ต์ œํ•˜์—ฌ ๊ฑด์„ค์ ์ธ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ด๋Œ์–ด ๋‚ด๋Š” ๊ฐ€์ด๋“œ๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ์ฑ—๋ด‡์„ ์„ค๊ณ„ํ•˜๊ธฐ ์œ„ํ•ด, ์„ ํ–‰ ์—ฐ๊ตฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์‚ฌ์šฉ์ž์˜ ์ž๊ธฐ๋…ธ์ถœ(user self-disclosure)๊ณผ ์ฑ—๋ด‡ ๊ฐ€์ด๋“œ(guidance)๋ฅผ ๋‘ ์ถ•์œผ๋กœ ํ•œ ๋””์ž์ธ ๊ณต๊ฐ„(design space)์„ ์ •์˜ํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ž๊ธฐ๋…ธ์ถœ๊ณผ ๊ฐ€์ด๋“œ์˜ ์ •๋„์— ๋”ฐ๋ฅธ ๋„ค ๊ฐ€์ง€ ์ž์•„์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค: ์ž๊ธฐ๋…ธ์ถœ๊ณผ ๊ฐ€์ด๋“œ๊ฐ€ ์ตœ์†Œํ™”๋œ ํšŒ์ƒ ๊ณต๊ฐ„, ์ž๊ธฐ๋…ธ์ถœ์ด ์œ„์ฃผ์ด๊ณ  ๊ฐ€์ด๋“œ๊ฐ€ ์ตœ์†Œํ™”๋œ ์„ค๋ช… ๊ณต๊ฐ„, ์ž๊ธฐ๋…ธ์ถœ๊ณผ ์ฑ—๋ด‡์ด ์ด๋„๋Š” ๊ฐ€์ด๋“œ๊ฐ€ ํ˜ผํ•ฉ๋œ ํƒ์ƒ‰ ๊ณต๊ฐ„, ๊ฐ€์ด๋“œ๋ฅผ ์ ๊ทน ๊ฐœ์ž…์‹œ์ผœ ์ž๊ธฐ๋…ธ์ถœ์„ ๋†’์ด๋Š” ๋ณ€ํ™” ๊ณต๊ฐ„์ด ๊ทธ๊ฒƒ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉํ‘œ๋Š” ์ƒ์ˆ ๋œ ๋””์ž์ธ ๊ณต๊ฐ„์—์„œ์˜ ์„ฑ์ฐฐ ๊ฒฝํ—˜๊ณผ ๊ณผ์ •์„ ๋•๋Š” ์ฑ—๋ด‡์„ ๊ตฌํ˜„ํ•˜๊ณ , ์‚ฌ์šฉ์ž ์‹คํ—˜์„ ํ†ตํ•ด ์„ฑ์ฐฐ ๊ฒฝํ—˜๊ณผ ๋””์ž์ธ ์ „๋žต์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์ฑ—๋ด‡ ๊ธฐ๋ฐ˜์˜ ์ž์•„ ์„ฑ์ฐฐ ์ธํ„ฐ๋ž™์…˜์„ ์ƒˆ๋กญ๊ฒŒ ์ œ์‹œํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ์‹ค์ฆ์  ๊ทผ๊ฑฐ๋ฅผ ๋งˆ๋ จํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํ˜„์žฌ๊นŒ์ง€ ๋งŽ์€ ์„ฑ์ฐฐ ๊ธฐ์ˆ ์€ ํšŒ์ƒ์— ์ง‘์ค‘๋˜์–ด ์žˆ๊ธฐ์—, ๋‚˜๋จธ์ง€ ์„ธ ๊ณต๊ฐ„์—์„œ์˜ ์„ฑ์ฐฐ์„ ์ง€์›ํ•˜๋Š” ๋ณด๋…ธ๋ด‡๊ณผ ๊ธฐ๋ณธํ˜•๋ฐ˜์‘ํ˜• ์ผ๊ธฐ๋ด‡์„ ๋””์ž์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž ํ‰๊ฐ€๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๋„์ถœํ•œ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋„๋ž˜ํ•œ ์ธ๊ฐ„-์ธ๊ณต์ง€๋Šฅ ์ƒํ˜ธ์ž‘์šฉ(human-AI interaction)์˜ ๋งฅ๋ฝ์—์„œ ์„ฑ์ฐฐ ๋™๋ฐ˜์ž๋กœ์„œ์˜ ์ฑ—๋ด‡ ๊ธฐ์ˆ ์ด ๊ฐ–๋Š” ์˜๋ฏธ์™€ ์—ญํ• ์„ ํƒ๊ตฌํ•œ๋‹ค. ๋ณด๋…ธ๋ด‡๊ณผ ์ผ๊ธฐ๋ด‡์€ ์ธ๊ฐ„์ค‘์‹ฌ์ƒ๋‹ด๊ณผ ๋Œ€ํ™”๋ถ„์„์˜ ์ด๋ก ์  ๊ทผ๊ฑฐ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•œ ์ •์„œ์ง€๋Šฅ(emotional intelligence)๊ณผ ์ ˆ์ฐจ์ง€๋Šฅ(proecedural intelligence)์„ ํ•ต์‹ฌ ์ถ•์œผ๋กœ, ๋Œ€ํ™” ํ๋ฆ„ ์ œ์–ด(flow manager)์™€ ๋ฐœํ™” ์ƒ์„ฑ(response generator)์„ ํ•ต์‹ฌ ๋ชจ๋“ˆ๋กœ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋จผ์ €, ๋ณด๋…ธ๋ด‡์€ ๋™๊ธฐ๊ฐ•ํ™”์ƒ๋‹ด(motivational interviewing)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ณ ๋ฏผ๊ณผ ์ŠคํŠธ๋ ˆ์Šค์— ๋Œ€ํ•œ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ด๋Œ์–ด๋‚ด์–ด, ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ์„ ์œ„ํ•œ ๊ฐ€์ด๋“œ ์งˆ๋ฌธ์„ ํ†ตํ•ด ๋ณ€ํ™”๋ฅผ ์œ„ํ•œ ์„ฑ์ฐฐ์„ ๋•๋Š”๋‹ค. ์ฑ—๋ด‡์˜ ๊ตฌํ˜„์„ ์œ„ํ•ด, ๋™๊ธฐ๊ฐ•ํ™”์ƒ๋‹ด์˜ ๋„ค ๋‹จ๊ณ„ ๋Œ€ํ™”๋ฅผ ์„ค์ •ํ•˜๊ณ  ๊ฐ ๋‹จ๊ณ„๋ฅผ ๊ตฌ์„ฑํ•  ์ˆ˜ ์žˆ๋Š” ์ƒ๋‹ด์‚ฌ ๋ฐœํ™” ํ–‰๋™์„ ๊ด€๋ จ๋ฌธํ—Œ์—์„œ ์ˆ˜์ง‘ ๋ฐ ์ „์ฒ˜๋ฆฌ ๊ณผ์ •์„ ๊ฑฐ์ณ ์Šคํฌ๋ฆฝํŠธํ™”ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์ „ ์ „์ฒ˜๋ฆฌ๋œ ๋ฌธ์žฅ์ด ๋งฅ๋ฝ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ™”์— ์“ฐ์ผ ์ˆ˜ ์žˆ๋„๋ก, ๋Œ€ํ™”์˜ ์ฃผ์ œ๋Š” ๋Œ€ํ•™์›์ƒ์˜ ์–ด๋ ค์›€์œผ๋กœ ํ•œ์ •ํ•˜์˜€๋‹ค. ๋ณด๋…ธ๋ด‡๊ณผ์˜ ๋Œ€ํ™”๊ฐ€ ์‚ฌ์šฉ์ž์˜ ์„ฑ์ฐฐ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ์ด์— ๋Œ€ํ•œ ์ธ์‹์„ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•ด ์งˆ์  ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ 30๋ช…์˜ ๋Œ€ํ•™์›์ƒ๊ณผ ์‚ฌ์šฉ์ž ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ, ์‚ฌ์šฉ์ž๋Š” ๋ณ€ํ™” ๋Œ€ํ™”๋ฅผ ์œ ๋„ํ•  ์ˆ˜ ์žˆ๋Š” ๋‹ค์–‘ํ•œ ํƒ์ƒ‰ ์งˆ๋ฌธ์„ ์„ ํ˜ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์‚ฌ์šฉ์ž์˜ ๋งฅ๋ฝ์— ์ •ํ™•ํžˆ ๋“ค์–ด๋งž๋Š” ์งˆ๋ฌธ๊ณผ ํ”ผ๋“œ๋ฐฑ์€ ์‚ฌ์šฉ์ž๋ฅผ ๋”์šฑ ์ ๊ทน์ ์ธ ์ž๊ธฐ ๋…ธ์ถœ๋กœ ์ด๋Œ๊ฒŒ ํ•  ์ˆ˜ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ฑ—๋ด‡์ด ๋งˆ์น˜ ์ƒ๋‹ด์‚ฌ์ฒ˜๋Ÿผ ๋Œ€ํ™”๋ฅผ ์ด๋Œ์–ด๊ฐˆ ๊ฒฝ์šฐ, ๋†’์•„์ง„ ์‚ฌ์šฉ์ž์˜ ๊ธฐ๋Œ€ ์ˆ˜์ค€์œผ๋กœ ์ธํ•ด ์ผ๋ถ€ ์‚ฌ์šฉ์ž๊ฐ€ ๋ณ€ํ™”์— ๋Œ€ํ•œ ๋™๊ธฐ๋ฅผ ํ‘œ์ถœํ•˜์˜€์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋ณ€ํ™”์— ๋Œ€ํ•œ ์ž์œจ์„ฑ์„ ์ฑ—๋ด‡์— ์–‘๋„ํ•˜๋ ค๋Š” ๋ชจ์Šต ๋˜ํ•œ ๋‚˜ํƒ€๋‚จ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณด๋…ธ๋ด‡ ์—ฐ๊ตฌ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ผ๊ธฐ๋ด‡์€ ์ฑ—๋ด‡ ๋Œ€์‹  ์‚ฌ์šฉ์ž๊ฐ€ ๋ณด๋‹ค ์ ๊ทน์ ์œผ๋กœ ์„ฑ์ฐฐ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๋ฅผ ์ „๊ฐœํ•  ์ˆ˜ ์žˆ๋„๋ก ๋””์ž์ธํ•˜์˜€๋‹ค. ์ผ๊ธฐ๋ด‡์€ ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•œ ํ‘œํ˜„์  ๊ธ€์“ฐ๊ธฐ๋ฅผ ์ง€์›ํ•˜๋Š” ์ฑ—๋ด‡์œผ๋กœ, ๊ธฐ๋ณธํ˜• ๋˜๋Š” ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๊ธฐ๋ณธํ˜• ๋Œ€ํ™”๋Š” ํŠธ๋ผ์šฐ๋งˆ์— ๋Œ€ํ•ด ์ž์œ ๋กญ๊ฒŒ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ™” ํ™˜๊ฒฝ์„ ์ œ๊ณตํ•˜๊ณ , ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋Š” ์‚ฌ์šฉ์ž๊ฐ€ ์ž‘์„ฑํ•œ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์— ๋Œ€ํ•œ ํ›„์† ์ธํ„ฐ๋ž™์…˜์„ ํ†ตํ•ด ๊ณผ๊ฑฐ์˜ ๊ฒฝํ—˜์„ ์žฌํƒ์ƒ‰ํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ›„์† ์ธํ„ฐ๋ž™์…˜์˜ ๋ฐœํ™” ํ–‰๋™์€ ๋‹ค์–‘ํ•œ ์ƒ๋‹ด์น˜๋ฃŒ์—์„œ ๋ฐœ์ทŒํ•˜๋˜ ์œ ์ €์˜ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ์—์„œ ์ถ”์ถœํ•œ ๊ฐ์ •์–ด ๋ฐ ์ธ๊ฐ„๊ด€๊ณ„ ํ‚ค์›Œ๋“œ๋ฅผ ํ™œ์šฉํ•˜๋„๋ก ํ•˜์˜€๋‹ค. ๊ฐ ์ผ๊ธฐ๋ด‡์— ๋Œ€ํ•œ ๋ฐ˜์‘์„ ๋น„๊ต ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด, ์ฑ—๋ด‡ ์—†์ด ๋„ํ๋จผํŠธ์— ํ‘œํ˜„์  ๊ธ€์“ฐ๊ธฐ ํ™œ๋™๋งŒ์„ ํ•˜๋Š” ๋Œ€์กฐ๊ตฐ์„ ์„ค์ •ํ•˜๊ณ  30๋ช…์˜ ์‚ฌ์šฉ์ž๋ฅผ ๋ชจ์ง‘ํ•˜์—ฌ ๊ฐ ์กฐ๊ฑด์— ๋žœ๋ค์œผ๋กœ ๋ฐฐ์ •, ์„ค๋ฌธ๊ณผ ๋ฉด๋‹ด์„ ๋™๋ฐ˜ํ•œ 4์ผ๊ฐ„์˜ ๊ธ€์“ฐ๊ธฐ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ, ์‚ฌ์šฉ์ž๋Š” ์ผ๊ธฐ๋ด‡๊ณผ์˜ ์ธํ„ฐ๋ž™์…˜์„ ํ†ตํ•ด ๋ณด์ด์ง€ ์•Š๋Š” ๊ฐ€์ƒ์˜ ์ฒญ์ž๋ฅผ ์ƒ์ƒํ•จ์œผ๋กœ์จ ๊ธ€์“ฐ๊ธฐ๋ฅผ ๋Œ€ํ™” ํ™œ๋™์œผ๋กœ ์ธ์ง€ํ•˜๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํŠนํžˆ, ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”์˜ ํ›„์† ์งˆ๋ฌธ๋“ค์€ ์‚ฌ์šฉ์ž๋กœ ํ•˜์—ฌ๊ธˆ ์ƒํ™ฉ์„ ๊ฐ๊ด€ํ™”ํ•˜๊ณ  ์ƒˆ๋กœ์šด ๊ด€์ ์œผ๋กœ ์ƒ๊ฐํ•ด ๋ณผ ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ๋ฅผ ๊ฑฐ๋‘์—ˆ๋‹ค. ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”์—์„œ ํ›„์† ์ธํ„ฐ๋ž™์…˜์„ ๊ฒฝํ—˜ํ•œ ์‚ฌ์šฉ์ž๋Š” ์ผ๊ธฐ๋ด‡์˜ ์ธ์ง€๋œ ์ฆ๊ฑฐ์›€๊ณผ ์‚ฌํšŒ์„ฑ, ์‹ ๋ขฐ๋„์™€ ์žฌ์‚ฌ์šฉ ์˜ํ–ฅ์— ๋Œ€ํ•œ ํ‰๊ฐ€๊ฐ€ ๋‹ค๋ฅธ ๋‘ ์กฐ๊ฑด์—์„œ๋ณด๋‹ค ์œ ์˜ํ•˜๊ฒŒ ๋†’์•˜๋‹ค. ๋ฐ˜๋ฉด, ๊ธฐ๋ณธํ˜• ๋Œ€ํ™” ์ฐธ์—ฌ์ž๋Š” ๋‹ค๋ฅธ ๋‘ ์กฐ๊ฑด์—์„œ๋ณด๋‹ค ๊ฐ์ •์  ํ‘œํ˜„์˜ ์šฉ์ด์„ฑ๊ณผ ๊ธ€์“ฐ๊ธฐ์˜ ์–ด๋ ค์›€์„ ๊ฐ๊ฐ ์œ ์˜ํ•˜๊ฒŒ ๋†’๊ฒŒ, ๊ทธ๋ฆฌ๊ณ  ๋‚ฎ๊ฒŒ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฆ‰, ์ฑ—๋ด‡์€ ๋งŽ์€ ์ธํ„ฐ๋ž™์…˜ ์—†์ด๋„ ์ฒญ์ž์˜ ์—ญํ• ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ์—ˆ์ง€๋งŒ, ํ›„์† ์งˆ๋ฌธ์„ ํ†ตํ•œ ์ธํ„ฐ๋ž™์…˜์ด ๊ฐ€๋Šฅํ–ˆ๋˜ ๋ฐ˜์‘ํ˜• ๋Œ€ํ™”๋Š” ๋”์šฑ ์ ๊ทน์ ์ธ ์œ ์ € ์ฐธ์—ฌ(engagement)๋ฅผ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ์‹คํ—˜์ด ์ง„ํ–‰๋จ์— ๋”ฐ๋ผ, ์‚ฌ์šฉ์ž๊ฐ€ ๋ฐ˜์‘ํ˜• ์ผ๊ธฐ๋ด‡์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์ž์‹ ์˜ ๊ธ€์“ฐ๊ธฐ ์ฃผ์ œ์™€ ๋‹จ์–ด ์„ ํƒ ๋“ฑ์„ ๋งž๊ฒŒ ๋ฐ”๊พธ์–ด ๊ฐ€๋Š” ์ ์‘์ (adaptive) ํ–‰๋™์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์•ž์„  ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด, ๋‹ค์–‘ํ•œ ์ฑ—๋ด‡ ๋””์ž์ธ ์ „๋žต์„ ๋ฐ”ํƒ•์œผ๋กœ ์‚ฌ์šฉ์ž์˜ ๋‚ด๋Ÿฌํ‹ฐ๋ธŒ๊ฐ€ ๋‹ค๋ฅด๊ฒŒ ์œ ๋„๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋”ฐ๋ผ์„œ ์„œ๋กœ ๋‹ค๋ฅธ ์œ ํ˜•์˜ ์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ์ด๋Œ์–ด๋‚ผ ์ˆ˜ ์žˆ์Œ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ž์œจ์ ์ธ ํ–‰์œ„์ธ ์ž์•„์„ฑ์ฐฐ์ด ๊ธฐ์ˆ ๊ณผ์˜ ์ƒํ˜ธ์ž‘์šฉ์œผ๋กœ ํ˜ธํ˜œ์  ์„ฑ์งˆ์„ ๊ฐ–๊ฒŒ ๋  ๋•Œ ์‚ฌ์šฉ์ž์˜ ์ž์œจ์„ฑ, ์ƒํ˜ธ์ž‘์šฉ์˜ ์˜ˆ์ธก๊ฐ€๋Šฅ์„ฑ๊ณผ ๋””์ž์ธ ํˆฌ๋ช…์„ฑ์—์„œ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐˆ๋“ฑ๊ด€๊ณ„(tensions)๋ฅผ ํƒ์ƒ‰ํ•˜๊ณ  ์ธ๊ณต์ง€๋Šฅ ์—์ด์ „ํŠธ์˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜ ์–ดํฌ๋˜์Šค(algorithmic affordances)๋ฅผ ๋…ผ์˜ํ•˜์˜€๋‹ค. ๋ณด์ด์ง€ ์•Š๋Š” ์ฑ—๋ด‡ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ์‚ฌ์šฉ์ž์˜ ์„ฑ์ฐฐ์ด ์œ ๋„๋  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ๊ธฐ์กด์˜ ์ธ๊ฐ„-์ปดํ“จํ„ฐ ์ƒํ˜ธ์ž‘์šฉ์—์„œ ๊ฐ•์กฐ๋˜๋Š” ์‚ฌ์šฉ์ž ์ œ์–ด์™€ ๋””์ž์ธ ํˆฌ๋ช…์„ฑ์—์„œ ์ „๋ณต์„ ์ดˆ๋ž˜ํ•˜๋Š” ๊ฒƒ์ฒ˜๋Ÿผ ๋ณด์ผ ์ˆ˜ ์žˆ์œผ๋‚˜, ์ƒ์ง•์  ์ƒํ˜ธ์ž‘์šฉ(symbolic interaction)์˜ ๋งฅ๋ฝ์—์„œ ์˜คํžˆ๋ ค ์‚ฌ์šฉ์ž๊ฐ€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ์˜ํ•ด ์ง€๋‚˜๊ฐ„ ๊ณผ๊ฑฐ์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์˜๋ฏธ๋ฅผ ์ ๊ทน ํƒ์ƒ‰ํ•ด๋‚˜๊ฐ€๋Š” ๊ณผ์ •์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ด๊ฒƒ์„ ์ƒˆ๋กœ์šด ๋””์ž์ธ ๋ฉ”ํƒ€ํฌ, ์ฆ‰ ์˜๋ฏธ-๋งŒ๋“ค๊ธฐ(meaning-making)๋กœ ์ œ์•ˆํ•˜๊ณ  ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ๋„›์ง€(nudge)์— ์˜ํ•œ ์‚ฌ์šฉ์ž์˜ ์ฃผ๊ด€์  ํ•ด์„ ๊ฒฝํ—˜(interpretive process)์„ ๊ฐ•์กฐํ•œ๋‹ค. ์ด๊ฒƒ์€ ํ•˜๋‚˜์˜ ์ฑ—๋ด‡ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๋ผ ํ• ์ง€๋ผ๋„ ์„œ๋กœ ๋‹ค๋ฅธ ์‚ฌ์šฉ์ž์˜ ๋‹ค์–‘ํ•œ ์„ฑ์ฐฐ ๊ฒฝํ—˜์„ ์œ ๋„ํ•ด๋‚ผ ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•˜๋ฉฐ, ์ด๋Ÿฌํ•œ ๋งฅ๋ฝ์—์„œ ์ธ๊ณต์ง€๋Šฅ์€ ๊ธฐ์กด์˜ ๋ธ”๋ž™ ๋ฐ•์Šค๋ฅผ ์œ ์ง€ํ•˜๋ฉด์„œ๋„ ์‚ฌ์šฉ์ž์˜ ์ž์œจ์„ฑ์„ ๋ณด์žฅํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์šฐ๋ฆฌ์™€ ํ˜‘์—…ํ•˜๋Š” ์ธ๊ณต์ง€๋Šฅ ์ฑ—๋ด‡ ๊ธฐ์ˆ ์˜ ๋””์ž์ธ์— ๋Œ€ํ•œ ๊ฒฝํ—˜์  ์ดํ•ด๋ฅผ ๋†’์ด๊ณ , ์ด๋ก ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ฑ—๋ด‡์„ ๊ตฌํ˜„ํ•จ์œผ๋กœ์จ ๋””์ž์ธ ์ „๋žต์— ๋Œ€ํ•œ ์‹ค์ฆ์  ๊ทผ๊ฑฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ ์ž์•„ ์„ฑ์ฐฐ ๊ณผ์ •์— ๋™ํ–‰ํ•˜๋Š” ๋™๋ฐ˜์ž(companion)๋กœ์„œ์˜ ๊ธฐ์ˆ ๋กœ ์ƒˆ๋กœ์šด ๋””์ž์ธ ๋ฉ”ํƒ€ํฌ๋ฅผ ์ œ์‹œํ•จ์œผ๋กœ์จ ์ธ๊ฐ„์ปดํ“จํ„ฐ์ƒํ˜ธ์ž‘์šฉ(HCI)์˜ ์ด๋ก ์  ํ™•์žฅ์— ๊ธฐ์—ฌํ•˜๊ณ , ์‚ฌ์šฉ์ž์˜ ๋ถ€์ •์  ๊ฒฝํ—˜์— ๋Œ€ํ•œ ์˜๋ฏธ ์ถ”๊ตฌ๋ฅผ ๋•๋Š” ๊ด€๊ณ„์ง€ํ–ฅ์  ์ธ๊ณต์ง€๋Šฅ์œผ๋กœ์„œ ํ–ฅํ›„ ํ˜„๋Œ€์ธ์˜ ์ •์‹ ๊ฑด๊ฐ•์— ์ด๋ฐ”์ง€ํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌํšŒ์ , ์‚ฐ์—…์  ์˜์˜๋ฅผ ๊ฐ–๋Š”๋‹ค.CHAPTER 1. INTRODUCTION ๏ผ‘ 1.1. BACKGROUND AND MOTIVATION ๏ผ‘ 1.2. RESEARCH GOAL AND QUESTIONS ๏ผ• 1.2.1. Research Goal ๏ผ• 1.2.2. Research Questions ๏ผ• 1.3. MAJOR CONTRIBUTIONS ๏ผ˜ 1.4. THESIS OVERVIEW ๏ผ™ CHAPTER 2. LITERATURE REVIEW ๏ผ‘๏ผ‘ 2.1. THE REFLECTING SELF ๏ผ‘๏ผ‘ 2.1.1. Self-Reflection and Mental Wellbeing ๏ผ‘๏ผ‘ 2.1.2. The Self in Reflective Practice ๏ผ‘๏ผ• 2.1.3. Design Space ๏ผ’๏ผ’ 2.2. SELF-REFLECTION IN HCI ๏ผ’๏ผ– 2.2.1. Reflection Design in HCI ๏ผ’๏ผ– 2.2.2. HCI for Mental Wellbeing ๏ผ“๏ผ– 2.2.3. Design Opportunities ๏ผ”๏ผ 2.3. CONVERSATIONAL AGENT DESIGN ๏ผ”๏ผ’ 2.3.1. Theoretical Background ๏ผ”๏ผ’ 2.3.2. Technical Background ๏ผ”๏ผ— 2.3.3. Design Strategies ๏ผ”๏ผ™ 2.4. SUMMARY ๏ผ–๏ผ™ CHAPTER 3. DESIGNING CHATBOT FOR TRANSFORMATIVE REFLECTION ๏ผ—๏ผ‘ 3.1. DESIGN GOAL AND DECISIONS ๏ผ—๏ผ‘ 3.2. CHATBOT IMPLEMENTATION ๏ผ—๏ผ– 3.2.1. Emotional Intelligence ๏ผ—๏ผ– 3.2.2. Procedural Intelligence ๏ผ—๏ผ— 3.3. EXPERIMENTAL USER STUDY ๏ผ—๏ผ™ 3.3.1. Participants ๏ผ—๏ผ™ 3.3.2. Task ๏ผ˜๏ผ 3.3.3. Procedure ๏ผ˜๏ผ 3.3.4. Ethics Approval ๏ผ˜๏ผ 3.3.5. Surveys and Interview ๏ผ˜๏ผ‘ 3.4. RESULTS ๏ผ˜๏ผ’ 3.4.1. Survey Findings ๏ผ˜๏ผ’ 3.4.2. Qualitative Findings ๏ผ˜๏ผ“ 3.5. IMPLICATIONS ๏ผ˜๏ผ˜ 3.5.1. Articulating Hopes and Fears ๏ผ˜๏ผ™ 3.5.2. Designing for Guidance ๏ผ™๏ผ‘ 3.5.3. Rethinking Autonomy ๏ผ™๏ผ’ 3.6. SUMMARY ๏ผ™๏ผ” CHAPTER 4. DESIGNING CHATBOTS FOR EXPLAINING AND EXPLORING REFLECTIONS ๏ผ™๏ผ– 4.1. DESIGN GOAL AND DECISIONS ๏ผ™๏ผ– 4.1.1. Design Decisions for Basic Chat ๏ผ™๏ผ˜ 4.1.2. Design Decisions for Responsive Chat ๏ผ™๏ผ˜ 4.2. CHATBOT IMPLEMENTATION ๏ผ‘๏ผ๏ผ’ 4.2.1. Emotional Intelligence ๏ผ‘๏ผ๏ผ“ 4.2.2. Procedural Intelligence ๏ผ‘๏ผ๏ผ• 4.3. EXPERIMENTAL USER STUDY ๏ผ‘๏ผ๏ผ– 4.3.1. Participants ๏ผ‘๏ผ๏ผ– 4.3.2. Task ๏ผ‘๏ผ๏ผ— 4.3.3. Procedure ๏ผ‘๏ผ๏ผ— 4.3.4. Safeguarding of Study Participants and Ethics Approval ๏ผ‘๏ผ๏ผ˜ 4.3.5. Surveys and Interviews ๏ผ‘๏ผ๏ผ˜ 4.4. RESULTS ๏ผ‘๏ผ‘๏ผ‘ 4.4.1. Quantitative Findings ๏ผ‘๏ผ‘๏ผ‘ 4.4.2. Qualitative Findings ๏ผ‘๏ผ‘๏ผ˜ 4.5. IMPLICATIONS ๏ผ‘๏ผ’๏ผ— 4.5.1. Telling Stories to a Chatbot ๏ผ‘๏ผ’๏ผ˜ 4.5.2. Designing for Disclosure ๏ผ‘๏ผ“๏ผ 4.5.3. Rethinking Predictability and Transparency ๏ผ‘๏ผ“๏ผ’ 4.6. SUMMARY ๏ผ‘๏ผ“๏ผ“ CHAPTER 5. DESIGNING CHATBOTS FOR SELF-REFLECTION: SUPPORTING GUIDED DISCLOSURE ๏ผ‘๏ผ“๏ผ• 5.1. DESIGNING FOR GUIDED DISCLOSURE ๏ผ‘๏ผ“๏ผ™ 5.1.1. Chatbots as Virtual Confidante ๏ผ‘๏ผ“๏ผ™ 5.1.2. Routine and Variety in Interaction ๏ผ‘๏ผ”๏ผ‘ 5.1.3. Reflection as Continued Experience ๏ผ‘๏ผ”๏ผ” 5.2. TENSIONS IN DESIGN ๏ผ‘๏ผ”๏ผ• 5.2.1. Adaptivity ๏ผ‘๏ผ”๏ผ• 5.2.2. Autonomy ๏ผ‘๏ผ”๏ผ— 5.2.3. Algorithmic Affordance ๏ผ‘๏ผ”๏ผ˜ 5.3. MEANING-MAKING AS DESIGN METAPHOR ๏ผ‘๏ผ•๏ผ 5.3.1. Meaning in Reflection ๏ผ‘๏ผ•๏ผ‘ 5.3.2. Meaning-Making as Interaction ๏ผ‘๏ผ•๏ผ“ 5.3.3. Making Meanings with AI ๏ผ‘๏ผ•๏ผ• CHAPTER 6. CONCLUSION ๏ผ‘๏ผ•๏ผ˜ 6.1. RESEARCH SUMMARY ๏ผ‘๏ผ•๏ผ˜ 6.2. LIMITATIONS AND FUTURE WORK ๏ผ‘๏ผ–๏ผ‘ 6.3. FINAL REMARKS ๏ผ‘๏ผ–๏ผ“ BIBLIOGRAPHY ๏ผ‘๏ผ–๏ผ• ABSTRACT IN KOREAN ๏ผ‘๏ผ™๏ผ’Docto
    • โ€ฆ
    corecore