"Oh dear stacy!": social interaction, elaboration, and learning with teachable agents
05 May 2012-pp 39-48
TL;DR: Treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or the authors rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains.
Abstract: Understanding how children perceive and interact with teachable agents (systems where children learn through teaching a synthetic character embedded in an intelligent tutoring system) can provide insight into the effects of so-cial interaction on learning with intelligent tutoring systems. We describe results from a think-aloud study where children were instructed to narrate their experience teaching Stacy, an agent who can learn to solve linear equations with the student's help. We found treating her as a partner, primarily through aligning oneself with Stacy using pronouns like you or we rather than she or it significantly correlates with student learning, as do playful face-threatening comments such as teasing, while elaborate explanations of Stacy's behavior in the third-person and formal tutoring statements reduce learning gains. Additionally, we found that the agent's mistakes were a significant predictor for students shifting away from alignment with the agent.
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08 Jun 2018
TL;DR: A study with 16 first-time chatbot users interacting with eight chatbots over multiple sessions on the Facebook Messenger platform revealed that users preferred chatbots that provided either a 'human-like' natural language conversation ability, or an engaging experience that exploited the benefits of the familiar turn-based messaging interface.
Abstract: Text messaging-based conversational agents (CAs), popularly called chatbots, received significant attention in the last two years. However, chatbots are still in their nascent stage: They have a low penetration rate as 84% of the Internet users have not used a chatbot yet. Hence, understanding the usage patterns of first-time users can potentially inform and guide the design of future chatbots. In this paper, we report the findings of a study with 16 first-time chatbot users interacting with eight chatbots over multiple sessions on the Facebook Messenger platform. Analysis of chat logs and user interviews revealed that users preferred chatbots that provided either a 'human-like' natural language conversation ability, or an engaging experience that exploited the benefits of the familiar turn-based messaging interface. We conclude with implications to evolve the design of chatbots, such as: clarify chatbot capabilities, sustain conversation context, handle dialog failures, and end conversations gracefully.
213 citations
01 Jan 2001
TL;DR: In this paper, the authors developed computer programs called PALs (Personal A_ssistants for L_earning) in which computers and students alternately coach each other.
Abstract: Our attempts to improve physics instruction have led us to analyze thought processes needed to apply scientific principles to problems—and to recognize that reliable performance requires the basic cognitive functions of deciding, implementing, and assessing. Using a reciprocal-teaching strategy to teach such thought processes explicitly, we have developed computer programs called PALs (P_ersonal A_ssistants for L_earning) in which computers and students alternately coach each other. These computer-implemented tutorials make it practically feasible to provide students with individual guidance and feedback ordinarily unavailable in most courses. We constructed PALs specifically designed to teach the application of Newton’s laws. In a comparative experimental study these computer tutorials were found to be nearly as effective as individual tutoring by expert teachers—and considerably more effective than the instruction provided in a well-taught physics class. Furthermore, almost all of the students using the PALs perceived them as very helpful to their learning. These results suggest that the proposed instructional approach could fruitfully be extended to improve instruction in various practically realistic contexts.
140 citations
TL;DR: The overlap between HCI and sense of agency for computer input modalities and system feedback, computer assistance, and joint actions between humans and computers is explored.
Abstract: The sense of agency is the experience of controlling both one's body and the external environment. Although the sense of agency has been studied extensively, there is a paucity of studies in applied "real-life" situations. One applied domain that seems highly relevant is human-computer-interaction (HCI), as an increasing number of our everyday agentive interactions involve technology. Indeed, HCI has long recognized the feeling of control as a key factor in how people experience interactions with technology. The aim of this review is to summarize and examine the possible links between sense of agency and understanding control in HCI. We explore the overlap between HCI and sense of agency for computer input modalities and system feedback, computer assistance, and joint actions between humans and computers. An overarching consideration is how agency research can inform HCI and vice versa. Finally, we discuss the potential ethical implications of personal responsibility in an ever-increasing society of technology users and intelligent machine interfaces.
134 citations
Cites background from ""Oh dear stacy!": social interactio..."
...Such agents have been investigated in application areas including education (Cassell, 2004; Ogan et al., 2012), healthcare (Bickmore and Gruber, 2010) and entertainment (Lim and Reeves, 2010)....
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TL;DR: By studying a field deployment of a Human Resource chatbot, data is reported on users' interest areas in conversational interactions to inform the development of CAs, and rich signals in Conversational interactions are highlighted for inferring user satisfaction with the instrumental usage and playful interactions with the agent.
Abstract: Many conversational agents (CAs) are developed to answer users' questions in a specialized domain. In everyday use of CAs, user experience may extend beyond satisfying information needs to the enjoyment of conversations with CAs, some of which represent playful interactions. By studying a field deployment of a Human Resource chatbot, we report on users' interest areas in conversational interactions to inform the development of CAs. Through the lens of statistical modeling, we also highlight rich signals in conversational interactions for inferring user satisfaction with the instrumental usage and playful interactions with the agent. These signals can be utilized to develop agents that adapt functionality and interaction styles. By contrasting these signals, we shed light on the varying functions of conversational interactions. We discuss design implications for CAs, and directions for developing adaptive agents based on users' conversational behaviors.
73 citations
Cites background from ""Oh dear stacy!": social interactio..."
...Other studies showed that students engaged in playful interactions such as making face-threatening comments with tutoring agents, and found them to improve learning experience [32]....
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...Recent studies considered this kind of behaviors as playful interactions and a key aspect of the adoption of CAs [28, 32, 43], through which users explore the system and seek satisfaction from a sense of social contact....
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04 Jun 2016
TL;DR: A 17-day field study of a prototype of a personal AI agent that helps employees find work-related information is conducted and it is found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals.
Abstract: Personal agent software is now in daily use in personal devices and in some organizational settings. While many advocate an agent sociality design paradigm that incorporates human-like features and social dialogues, it is unclear whether this is a good match for professionals who seek productivity instead of leisurely use. We conducted a 17-day field study of a prototype of a personal AI agent that helps employees find work-related information. Using log data, surveys, and interviews, we found individual differences in the preference for humanized social interactions (social-agent orientation), which led to different user needs and requirements for agent design. We also explored the effect of agent proactive interactions and found that they carried the risk of interruption, especially for users who were generally averse to interruptions at work. Further, we found that user differences in social-agent orientation and aversion to agent proactive interactions can be inferred from behavioral signals. Our results inform research into social agent design, proactive agent interaction, and personalization of AI agents.
66 citations
References
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TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Abstract: CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of measurement obtainable is nominal scaling (Stevens, 1951, pp. 2526), i.e. placement in a set of k unordered categories. Because the categorizing of the units is a consequence of some complex judgment process performed by a &dquo;two-legged meter&dquo; (Stevens, 1958), it becomes important to determine the extent to which these judgments are reproducible, i.e., reliable. The procedure which suggests itself is that of having two (or more) judges independently categorize a sample of units and determine the degree, significance, and
34,965 citations
""Oh dear stacy!": social interactio..." refers methods in this paper
...Reliability is given for each coding category below in a Cohen’s K [8]....
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01 Jan 1987
TL;DR: Gumperz as discussed by the authors discusses politeness strategies in language and their implications for language studies, including sociological implications and implications for social sciences. But he does not discuss the relationship between politeness and language.
Abstract: Symbols and abbreviations Foreword John J. Gumperz Introduction to the reissue Notes 1. Introduction 2. Summarized argument 3. The argument: intuitive bases and derivative definitions 4. On the nature of the model 5. Realizations of politeness strategies in language 6. Derivative hypotheses 7. Sociological implications 8. Implications for language studies 9. Conclusions Notes References Author index Subject index.
9,542 citations
Book•
01 Jan 1987TL;DR: This paper presents an argument about the nature of the model and its implications for language studies and Sociological implications and discusses the role of politeness strategies in language.
Abstract: This study is about the principles for constructing polite speeches. The core of it first appeared in Questions and Politeness, edited by Esther N. Goody (now out of print). It is here reissued with a fresh introduction that surveys the considerable literature in linguistics, psychology and the social sciences that the original extended essay stimulated, and suggests distinct directions for research. The authors describe and account for some remarkable parallelisms in the linguistic construction of utterances with which people express themselves in different languages and cultures. A motive for these parallels is isolated and a universal model is constructed outlining the abstract principles underlying polite usages. This is based on the detailed study of three unrelated languages and cultures: the Tamil of South India, the Tzeltal spoken by Mayan Indians in Chiapas, Mexico, and the English of the USA and England. This volume will be of special interest to students in linguistic pragmatics, sociolinguistics, applied linguistics, anthropology, and the sociology and social psychology of interaction.
9,053 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...face-threatening, by which is meant dialogue moves that threaten the other person’s identity management, or positive sense of him or herself [4]....
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Book•
01 Jan 1996TL;DR: This chapter discusses the media equation, which describes the role media and personality play in the development of a person's identity and aims at clarifying these roles.
Abstract: Part I. Introduction: 1. The media equation Part II. Media and Manners: 2. Politeness 3. Interpersonal distance 4. Flattery 5. Judging others and ourselves Part III. Media and Personality: 6. Personality of characters 7. Personality of interfaces 8. Imitating a personality Part IV. Media and emotion: 9. Good versus bad 10. Negativity 11. Arousal Part V. Media and Social Roles: 12. Specialists 13. Teammates 14. Gender 15. Voices 16. Source orientation Part VI. Media and Form: 17. Image size 18. Fidelity 19. Synchrony 20. Motion 21. Scene changes 22. Subliminal images Part VII. Final Words: 23. Conclusions about the media equation References.
4,690 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...Students who made many formal tutoring moves and few social moves often used outside-aligned speech to discuss what Stacy did and did not know, which we hypothesize is because it would be face-threatening to discuss her incompetencies with her in detail, along the lines described by Reeves and Nass [16]....
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...HYPOTHESES Cognitive hypotheses of learning by teaching suggest that tutors will engage in more mental organization of the material and perform more self-explanation as they tutor, leading to learning gains [10,11,16,20,25]....
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...Given conflicting prior work on whether social relationships can be formed with virtual agents [5,16,17,18] we chose to look at the type of language students used when referring to the agent as a clue to their social stance....
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TL;DR: Cooperative learning is one of the greatest success stories in the history of educational research as discussed by the authors, and the most frequent objective of this research is to determine the effects of cooperative learning on student achievement.
1,563 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...For example, researchers have proposed that there are substantial social aspects of peer tutoring that are responsible for evoking tutor learning effects, such as a strong feeling of accountability for ensuring the tutee is learning the proper information [24], as well as a desire to avoid the face-threat of not being able to fully respond to tutee questions [28]....
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