"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 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.
130 citations
01 Jan 1993
109 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...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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29 Jun 2007
TL;DR: The authors investigate the role of increasing friendship in dialogue and propose a computational model of long-term relationships in language use between humans and embodied conversational agents, and present a model of deepening rapport which would enable an ECA to begin to model patterns of human relationships.
Abstract: We investigate the role of increasing friendship in dialogue, and propose a first step towards a computational model of the role of long-term relationships in language use between humans and embodied conversational agents. Data came from a study of friends and strangers, who either could or could not see one another, and who were asked to give directions to one-another, three subsequent times. Analysis focused on differences in the use of dialogue acts and non-verbal behaviors, as well as co-occurrences of dialogue acts, eye gaze and head nods, and found a pattern of verbal and nonverbal behavior that differentiates the dialogue of friends from that of strangers, and differentiates early acquaintances from those who have worked together before. Based on these results, we present a model of deepening rapport which would enable an ECA to begin to model patterns of human relationships.
86 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...In fact, according to the theory of rapport proposed by [26], as applied by [6], positive statements in a relationship decrease over time, which may indicate that students felt more comfortable with the agent and thus had less need for positive statements....
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TL;DR: In this article, the effectiveness of a supervised cross-age tutoring program in mathematics operations was assessed using 51 grade-six children and 25 grade-five children who tutored 51 grade three and 25 level two children on a one-to-one basis for 30 minutes per day, four days per week over five weeks.
Abstract: The effectiveness of a supervised cross-age tutoring program in mathematics operations was assessed using 51 grade-six children and 25 grade-five children who tutored 51 grade-three and 25 grade-two children on a one-to-one basis for 30 minutes per day, four days per week over five weeks. Twenty-four grade-six and 31 grade-three children (within-school controls) and 18 grade-six, 21 grade-five, 32 grade-three and 32 grade-two children (noncontact school controls) did not take part in the tutoring program. Assessments were undertaken for the 152 experimental group children and the 158 controls prior to, and following the tutoring program to measure mathematics-operations and other mathematics achievements. Self-esteem of tutors and nontutors, and the reactions of tutors, tutees, relevant teachers and parents were also assessed. The fifth and sixth grade tutors effectively increased the operational mathematics achievements of their tutees: the increases in tutors’ and tutees’ mathematics achievements being ...
60 citations
""Oh dear stacy!": social interactio..." refers background or result in this paper
...Additionally, the explicit selfexplanation that must occur in order to teach someone else has also been hypothesized as the main factor responsible for tutor learning [23]....
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...This may even lead to the tutor learning additional domain material not explicitly covered in the session [23]....
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...Cognitive elaboration and outside alignment Because previous human-human peer tutoring research reports that increased elaboration is associated with learning gains [23], we conducted further analysis to understand why elaborations were negatively correlated with learning in our study....
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...A number of theories have been proposed to explain this effect, including increased motivation to learn the material [23], increased reflection on already learned material [19], and increased effort turning knowledge into coherent, communicable ideas [10,11,29]....
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...DISCUSSION A number of authors have posited that human peer tutoring is successful because of the increased elaboration of material that is necessitated by interaction between tutor and tutee [22, 23]....
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01 Jan 2003
TL;DR: This article examined the role of Why questions and other open ended questions in creating opportunities for student learning as well as explicit negative feedback for wrong answers in human tutoring dialogues and found that Why questions correlate significantly with learning gains.
Abstract: It is undoubtedly true that one prominent component of effective human tutoring is collaborative dialogue between student and tutor [7, 12]. Nevertheless, many important questions remain to be answered about which features of human tutorial dialogue make it effective and how the most effective human tutoring strategies can be implemented in a tutorial dialogue system. In this paper we present an analysis of a corpus of human tutoring dialogues where we examine which features correlate significantly with learning gains. In particular we explore the role of Why questions and other open ended questions in creating opportunities for student learning as well as the role of explicit negative feedback for wrong answers.
45 citations
""Oh dear stacy!": social interactio..." refers background in this paper
...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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...Instead they tend to answer questions with short keywords, providing no explanation [20, 21]....
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