Contextual predictability shapes signal autonomy

Abstract Aligning on a shared system of communication requires senders and receivers reach a balance between simplicity, where there is a pressure for compressed representations, and informativeness, where there is a pressure to be communicatively functional. We investigate the extent to which these two pressures are governed by contextual predictability: the amount of contextual information that a sender can estimate, and therefore exploit, in conveying their intended meaning. In particular, we test the claim that contextual predictability is causally related to signal autonomy: the degree to which a signal can be interpreted in isolation, without recourse to contextual information. Using an asymmetric communication game, where senders and receivers are assigned fixed roles, we manipulate two aspects of the referential context: (i) whether or not a sender shares access to the immediate contextual information used by the receiver in interpreting their utterance; (ii) the extent to which the relevant solution in the immediate referential context is generalisable to the aggregate set of contexts. Our results demonstrate that contextual predictability shapes the degree of signal autonomy: when the context is highly predictable (i.e., the sender has access to the context in which their utterances will be interpreted, and the semantic dimension which discriminates between meanings in context is consistent across communicative episodes), languages develop which rely heavily on the context to reduce uncertainty about the intended meaning. When the context is less predictable, senders favour systems composed of autonomous signals, where all potentially relevant semantic dimensions are explicitly encoded. Taken together, these results suggest that our pragmatic faculty, and how it integrates information from the context in reducing uncertainty, plays a central role in shaping language structure

Context, cognition and communication in language

Questions pertaining to the unique structure and organisation of language have a long history in the field of linguistics. In recent years, researchers have explored cultural evolutionary explanations, showing how language structure emerges from weak biases amplified over repeated patterns of learning and use. One outstanding issue in these frameworks is accounting for the role of context. In particular, many linguistic phenomena are said to to be context-dependent; interpretation does not take place in a void, and requires enrichment from the current state of the conversation, the physical situation, and common knowledge about the world. Modelling the relationship between language structure and context is therefore crucial for developing a cultural evolutionary approach to language. One approach is to use statistical analyses to investigate large-scale, cross-cultural datasets. However, due to the inherent limitations of statistical analyses, especially with regards to the inadequacy of these methods to test hypotheses about causal relationships, I argue that experiments are better suited to address questions pertaining to language structure and context. From here, I present a series of artificial language experiments, with the central aim being to test how manipulations to context influence the structure and organisation of language. Experiment 1 builds upon previous work in iterated learning and communication games through demonstrating that the emergence of optimal communication systems is contingent on the contexts in which languages are learned and used. The results show that language systems gradually evolve to only encode information that is informative for conveying the intended meaning of the speaker - resulting in markedly different systems of communication. Whereas Experiment 1 focused on how context influences the emergence of structure, Experiments 2 and 3 investigate under what circumstances do manipulations to context result in the loss of structure. While the results are inconclusive across these two experiments, there is tentative evidence that manipulations to context can disrupt structure, but only when interacting with other factors. Lastly, Experiment 4 investigates whether the degree of signal autonomy (the capacity for a signal to be interpreted without recourse to contextual information) is shaped by manipulations to contextual predictability: the extent to which a speaker can estimate and exploit contextual information a hearer uses in interpreting an utterance. When the context is predictable, speakers organise languages to be less autonomous (more context-dependent) through combining linguistic signals with contextual information to reduce effort in production and minimise uncertainty in comprehension. By decreasing contextual predictability, speakers increasingly rely on strategies that promote more autonomous signals, as these signals depend less on contextual information to discriminate between possible meanings. Overall, these experiments provide proof-of-concept for investigating the relationship between language structure and context, showing that the organisational principles underpinning language are the result of competing pressures from context, cognition, and communication

The Legitimacy of Arbitral Reasoning: On Authority and Authorisation in International Investment Dispute Settlement

The institution of investment treaty arbitration fundamentally questions traditional correlations between legitimation procedures and legitimate authority, as well as between individual, case-to case arbitration and arbitration as a highly effective international adjudicative system. It oscillates between contractual autonomy in proceedings and traits of substantive public law with regards to grounds for and merits of claims. By engaging with an emerging scholarship on procedural and ethical determinants of investor-state dispute settlement, this article explores and argues for a scholarly sensitivity towards a structural co-originality of procedural authorisation and arbitral authority. Demonstrating that responsibility and accountability are decisive and still under-theorised procedural factors relating to legitimation and legitimacy perceptions, the article concludes with a normative account of the nature of legal reasoning in investment treaty arbitration. Accentuating the intrinsic correlation of internal to external, and autonomous to instrumental procedural objectives in the craftsmanship of writing arbitral awards adds meaningfully to what has been labelled a ‘jurisprudence constante’ and thus identified as legitimate corpus of arbitral decisions

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

Adults are more efficient in creating and transmitting novel signalling systems than children

Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5-7-year old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4-5-year old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems is unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution

Working outside the comfort of competence in a corrections centre: toward collective competence

Qualitative case study of the collective learning of staff working in a corrections centre under conditions of rapid organisational change. Conceptualises the notion of collective competence that is emergent and relationally constructed

A healthy office and healthy employees: a longitudinal case study with a salutogenic perspective in the context of the physical office environment

This two-wave study (time lag of six months and two years post-relocation) investigated ways in which employees’ perceptions of the office environment relate to their perceived health in the long term, drawing on the salutogenic approach to health and the sense of coherence theory (comprehensibility, manageability, and meaningfulness). A mixed-method approach was adopted. The data collection involved semi-structured interviews with employees, plus structured observations. The findings indicate that employees found the office environment less comprehensible and meaningful in Wave 2, while (somewhat) equally manageable. Comprehensibility was influenced by a lack of clear behavioural rules; manageability was influenced by a lack of control over the environment; and meaningfulness was influenced by social environment and lack of personalization. The contextual aspects of the office, including tasks, flexible working culture and the change processes were critical to these findings. This study has demonstrated that negative influences caused by poor design choices do not resolve themselves over time. When there is limited support for one component of sense of coherence, the initial observed benefits wear off and negative influences may spill over into other components. Therefore, office design should be approached with balanced attention to comprehensibility, manageability, and meaningfulness

Double elevation: Autonomous weapons and the search for an irreducible law of war

What should be the role of law in response to the spread of artificial intelligence in war? Fuelled by both public and private investment, military technology is accelerating towards increasingly autonomous weapons, as well as the merging of humans and machines. Contrary to much of the contemporary debate, this is not a paradigm change; it is the intensification of a central feature in the relationship between technology and war: Double elevation, above one's enemy and above oneself. Elevation above one's enemy aspires to spatial, moral, and civilizational distance. Elevation above oneself reflects a belief in rational improvement that sees humanity as the cause of inhumanity and de-humanization as our best chance for humanization. The distance of double elevation is served by the mechanization of judgement. To the extent that judgement is seen as reducible to algorithm, law becomes the handmaiden of mechanization. In response, neither a focus on questions of compatibility nor a call for a 'ban on killer robots' help in articulating a meaningful role for law. Instead, I argue that we should turn to a long-standing philosophical critique of artificial intelligence, which highlights not the threat of omniscience, but that of impoverished intelligence. Therefore, if there is to be a meaningful role for law in resisting double elevation, it should be law encompassing subjectivity, emotion and imagination, law irreducible to algorithm, a law of war that appreciates situated judgement in the wielding of violence for the collective

정신건강에서 사용자 내러티브와 자아성찰을 지원하는 대화형 에이전트 디자인

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2020. 8. 서봉원.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.최근 인공지능(Artificial Intelligence; AI) 기술은 우리 삶의 면면을 매우 빠르게 바꿔놓고 있다. 특히 애플의 시리(Siri)와 구글 어시스턴트 (Google Assistant) 등 자연어 인터페이스(natural language interfaces)의 확장은 곧 인공지능 에이전트와의 대화가 인터랙션의 주요 수단이 될 것임을 능히 짐작케 한다. 실상 인공지능 에이전트는 실생활에서 콘텐츠 추천과 온라인 쇼핑 등 다양한 서비스를 제공하고 있지만, 이들의 대부분은 과업-지향적이다. 즉 인공지능은 우리의 삶을 편리하게 하지만, 과연 편안하게 할 수 있는가? 본 연구는 편하지만 편하지 않은 현대인을 위한 기술의 역할을 고민하는 데에서 출발한다. 자아성찰(self-reflection), 즉 자신에 대해 깊이 생각해 보는 활동은 자기인식과 자기이해를 도모하고 배움과 목표의식을 고취하는 등 분야를 막론하고 널리 연구 및 적용되어 왔다. 하지만 자아성찰의 가장 큰 어려움은 스스로 건설적인 성찰을 도모하기 힘들다는 것이다. 특히, 부정적인 감정적 경험에 대한 자아성찰은 종종 우울감과 불안을 동반한다. 극복이 힘든 경우 상담 또는 치료를 찾을 수 있지만, 사회적 낙인과 잣대의 부담감으로 꺼려지는 경우가 다수이다. 성찰 디자인(Reflection Design)은 인간-컴퓨터상호작용(HCI)의 오랜 화두로, 그동안 효과적인 성찰을 도울 수 있는 디자인 전략들이 다수 연구되어 왔지만 대부분 다양한 사용자 데이터 수집 전략을 통해 과거 회상 및 해석을 돕는 데 그쳤다. 최근 소위 챗봇 상담사가 등장하여 심리상담과 치료 분야에 적용되고 있지만, 이 또한 성찰을 돕기보다는 효율적인 처치 도구에 머무르고 있을 뿐이다. 즉 기술은 치료 수단이거나 성찰의 대상이 되지만, 그 과정에 개입하는 경우는 제한적이라고 할 수 있다. 이에 본 연구는 성찰 동반자로서 대화형 에이전트인 챗봇을 디자인할 것을 제안한다. 이 챗봇의 역할은 사용자의 부정적인 감정적 경험 또는 트라우마에 대해 이야기할 수 있도록 도울 뿐 아니라, 그 과정에서 반추를 통제하여 건설적인 내러티브를 이끌어 내는 가이드를 제공하는 것이다. 이러한 챗봇을 설계하기 위해, 선행 연구를 기반으로 사용자의 자기노출(user self-disclosure)과 챗봇 가이드(guidance)를 두 축으로 한 디자인 공간(design space)을 정의하였다. 그리고 자기노출과 가이드의 정도에 따른 네 가지 자아성찰 경험을 분류하였다: 자기노출과 가이드가 최소화된 회상 공간, 자기노출이 위주이고 가이드가 최소화된 설명 공간, 자기노출과 챗봇이 이끄는 가이드가 혼합된 탐색 공간, 가이드를 적극 개입시켜 자기노출을 높이는 변화 공간이 그것이다. 본 연구의 목표는 상술된 디자인 공간에서의 성찰 경험과 과정을 돕는 챗봇을 구현하고, 사용자 실험을 통해 성찰 경험과 디자인 전략에 대한 반응을 수집 및 분석함으로써 챗봇 기반의 자아 성찰 인터랙션을 새롭게 제시하고 이에 대한 실증적 근거를 마련하는 것이다. 현재까지 많은 성찰 기술은 회상에 집중되어 있기에, 나머지 세 공간에서의 성찰을 지원하는 보노봇과 기본형반응형 일기봇을 디자인하였다. 또한, 사용자 평가를 바탕으로 도출한 연구결과를 통해 도래한 인간-인공지능 상호작용(human-AI interaction)의 맥락에서 성찰 동반자로서의 챗봇 기술이 갖는 의미와 역할을 탐구한다. 보노봇과 일기봇은 인간중심상담과 대화분석의 이론적 근거를 바탕으로 한 정서지능(emotional intelligence)과 절차지능(proecedural intelligence)을 핵심 축으로, 대화 흐름 제어(flow manager)와 발화 생성(response generator)을 핵심 모듈로 구현하였다. 먼저, 보노봇은 동기강화상담(motivational interviewing)을 기반으로 고민과 스트레스에 대한 내러티브를 이끌어내어, 이에 대한 해결을 위한 가이드 질문을 통해 변화를 위한 성찰을 돕는다. 챗봇의 구현을 위해, 동기강화상담의 네 단계 대화를 설정하고 각 단계를 구성할 수 있는 상담사 발화 행동을 관련문헌에서 수집 및 전처리 과정을 거쳐 스크립트화하였다. 또한, 사전 전처리된 문장이 맥락을 유지할 수 있는 대화에 쓰일 수 있도록, 대화의 주제는 대학원생의 어려움으로 한정하였다. 보노봇과의 대화가 사용자의 성찰에 미치는 영향과 이에 대한 인식을 탐색하기 위해 질적 연구방법을 사용하여 30명의 대학원생과 사용자 실험을 진행하였다. 실험결과, 사용자는 변화 대화를 유도할 수 있는 다양한 탐색 질문을 선호하였다. 또한, 사용자의 맥락에 정확히 들어맞는 질문과 피드백은 사용자를 더욱 적극적인 자기 노출로 이끌게 할 수 있음을 발견하였다. 그러나 챗봇이 마치 상담사처럼 대화를 이끌어갈 경우, 높아진 사용자의 기대 수준으로 인해 일부 사용자가 변화에 대한 동기를 표출하였음에도 불구하고 변화에 대한 자율성을 챗봇에 양도하려는 모습 또한 나타남을 분석하였다. 보노봇 연구를 바탕으로 일기봇은 챗봇 대신 사용자가 보다 적극적으로 성찰 내러티브를 전개할 수 있도록 디자인하였다. 일기봇은 트라우마에 대한 표현적 글쓰기를 지원하는 챗봇으로, 기본형 또는 반응형 대화를 제공한다. 기본형 대화는 트라우마에 대해 자유롭게 설명할 수 있는 대화 환경을 제공하고, 반응형 대화는 사용자가 작성한 내러티브에 대한 후속 인터랙션을 통해 과거의 경험을 재탐색하도록 하였다. 또한, 후속 인터랙션의 발화 행동은 다양한 상담치료에서 발췌하되 유저의 내러티브에서 추출한 감정어 및 인간관계 키워드를 활용하도록 하였다. 각 일기봇에 대한 반응을 비교 분석하기 위해, 챗봇 없이 도큐먼트에 표현적 글쓰기 활동만을 하는 대조군을 설정하고 30명의 사용자를 모집하여 각 조건에 랜덤으로 배정, 설문과 면담을 동반한 4일간의 글쓰기 실험을 진행하였다. 실험결과, 사용자는 일기봇과의 인터랙션을 통해 보이지 않는 가상의 청자를 상상함으로써 글쓰기를 대화 활동으로 인지하고 있음을 알 수 있었다. 특히, 반응형 대화의 후속 질문들은 사용자로 하여금 상황을 객관화하고 새로운 관점으로 생각해 볼 수 있는 효과를 거두었다. 반응형 대화에서 후속 인터랙션을 경험한 사용자는 일기봇의 인지된 즐거움과 사회성, 신뢰도와 재사용 의향에 대한 평가가 다른 두 조건에서보다 유의하게 높았다. 반면, 기본형 대화 참여자는 다른 두 조건에서보다 감정적 표현의 용이성과 글쓰기의 어려움을 각각 유의하게 높게, 그리고 낮게 평가하였다. 즉, 챗봇은 많은 인터랙션 없이도 청자의 역할을 수행할 수 있었지만, 후속 질문을 통한 인터랙션이 가능했던 반응형 대화는 더욱 적극적인 유저 참여(engagement)를 이끌어낼 수 있었다. 또한, 실험이 진행됨에 따라, 사용자가 반응형 일기봇의 알고리즘에 자신의 글쓰기 주제와 단어 선택 등을 맞게 바꾸어 가는 적응적(adaptive) 행동이 관찰되었다. 앞선 연구결과를 통해, 다양한 챗봇 디자인 전략을 바탕으로 사용자의 내러티브가 다르게 유도될 수 있으며, 따라서 서로 다른 유형의 성찰 경험을 이끌어낼 수 있음을 발견하였다. 또한, 자율적인 행위인 자아성찰이 기술과의 상호작용으로 호혜적 성질을 갖게 될 때 사용자의 자율성, 상호작용의 예측가능성과 디자인 투명성에서 발생할 수 있는 갈등관계(tensions)를 탐색하고 인공지능 에이전트의 알고리즘 어포던스(algorithmic affordances)를 논의하였다. 보이지 않는 챗봇 알고리즘에 의해 사용자의 성찰이 유도될 수 있다는 것은 기존의 인간-컴퓨터 상호작용에서 강조되는 사용자 제어와 디자인 투명성에서 전복을 초래하는 것처럼 보일 수 있으나, 상징적 상호작용(symbolic interaction)의 맥락에서 오히려 사용자가 알고리즘에 의해 지나간 과거에 대한 새로운 의미를 적극 탐색해나가는 과정이 될 수 있다. 본 연구는 이것을 새로운 디자인 메타포, 즉 의미-만들기(meaning-making)로 제안하고 알고리즘의 넛지(nudge)에 의한 사용자의 주관적 해석 경험(interpretive process)을 강조한다. 이것은 하나의 챗봇 알고리즘이라 할지라도 서로 다른 사용자의 다양한 성찰 경험을 유도해낼 수 있다는 것을 의미하며, 이러한 맥락에서 인공지능은 기존의 블랙 박스를 유지하면서도 사용자의 자율성을 보장할 수 있다. 본 연구는 우리와 협업하는 인공지능 챗봇 기술의 디자인에 대한 경험적 이해를 높이고, 이론을 기반으로 한 챗봇을 구현함으로써 디자인 전략에 대한 실증적 근거를 제시한다. 또한 자아 성찰 과정에 동행하는 동반자(companion)로서의 기술로 새로운 디자인 메타포를 제시함으로써 인간컴퓨터상호작용(HCI)의 이론적 확장에 기여하고, 사용자의 부정적 경험에 대한 의미 추구를 돕는 관계지향적 인공지능으로서 향후 현대인의 정신건강에 이바지할 수 있는 사회적, 산업적 의의를 갖는다.CHAPTER 1. INTRODUCTION １ 1.1. BACKGROUND AND MOTIVATION １ 1.2. RESEARCH GOAL AND QUESTIONS ５ 1.2.1. Research Goal ５ 1.2.2. Research Questions ５ 1.3. MAJOR CONTRIBUTIONS ８ 1.4. THESIS OVERVIEW ９ CHAPTER 2. LITERATURE REVIEW １１ 2.1. THE REFLECTING SELF １１ 2.1.1. Self-Reflection and Mental Wellbeing １１ 2.1.2. The Self in Reflective Practice １５ 2.1.3. Design Space ２２ 2.2. SELF-REFLECTION IN HCI ２６ 2.2.1. Reflection Design in HCI ２６ 2.2.2. HCI for Mental Wellbeing ３６ 2.2.3. Design Opportunities ４０ 2.3. CONVERSATIONAL AGENT DESIGN ４２ 2.3.1. Theoretical Background ４２ 2.3.2. Technical Background ４７ 2.3.3. Design Strategies ４９ 2.4. SUMMARY ６９ CHAPTER 3. DESIGNING CHATBOT FOR TRANSFORMATIVE REFLECTION ７１ 3.1. DESIGN GOAL AND DECISIONS ７１ 3.2. CHATBOT IMPLEMENTATION ７６ 3.2.1. Emotional Intelligence ７６ 3.2.2. Procedural Intelligence ７７ 3.3. EXPERIMENTAL USER STUDY ７９ 3.3.1. Participants ７９ 3.3.2. Task ８０ 3.3.3. Procedure ８０ 3.3.4. Ethics Approval ８０ 3.3.5. Surveys and Interview ８１ 3.4. RESULTS ８２ 3.4.1. Survey Findings ８２ 3.4.2. Qualitative Findings ８３ 3.5. IMPLICATIONS ８８ 3.5.1. Articulating Hopes and Fears ８９ 3.5.2. Designing for Guidance ９１ 3.5.3. Rethinking Autonomy ９２ 3.6. SUMMARY ９４ CHAPTER 4. DESIGNING CHATBOTS FOR EXPLAINING AND EXPLORING REFLECTIONS ９６ 4.1. DESIGN GOAL AND DECISIONS ９６ 4.1.1. Design Decisions for Basic Chat ９８ 4.1.2. Design Decisions for Responsive Chat ９８ 4.2. CHATBOT IMPLEMENTATION １０２ 4.2.1. Emotional Intelligence １０３ 4.2.2. Procedural Intelligence １０５ 4.3. EXPERIMENTAL USER STUDY １０６ 4.3.1. Participants １０６ 4.3.2. Task １０７ 4.3.3. Procedure １０７ 4.3.4. Safeguarding of Study Participants and Ethics Approval １０８ 4.3.5. Surveys and Interviews １０８ 4.4. RESULTS １１１ 4.4.1. Quantitative Findings １１１ 4.4.2. Qualitative Findings １１８ 4.5. IMPLICATIONS １２７ 4.5.1. Telling Stories to a Chatbot １２８ 4.5.2. Designing for Disclosure １３０ 4.5.3. Rethinking Predictability and Transparency １３２ 4.6. SUMMARY １３３ CHAPTER 5. DESIGNING CHATBOTS FOR SELF-REFLECTION: SUPPORTING GUIDED DISCLOSURE １３５ 5.1. DESIGNING FOR GUIDED DISCLOSURE １３９ 5.1.1. Chatbots as Virtual Confidante １３９ 5.1.2. Routine and Variety in Interaction １４１ 5.1.3. Reflection as Continued Experience １４４ 5.2. TENSIONS IN DESIGN １４５ 5.2.1. Adaptivity １４５ 5.2.2. Autonomy １４７ 5.2.3. Algorithmic Affordance １４８ 5.3. MEANING-MAKING AS DESIGN METAPHOR １５０ 5.3.1. Meaning in Reflection １５１ 5.3.2. Meaning-Making as Interaction １５３ 5.3.3. Making Meanings with AI １５５ CHAPTER 6. CONCLUSION １５８ 6.1. RESEARCH SUMMARY １５８ 6.2. LIMITATIONS AND FUTURE WORK １６１ 6.3. FINAL REMARKS １６３ BIBLIOGRAPHY １６５ ABSTRACT IN KOREAN １９２Docto