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Proceedings ArticleDOI

"Oh dear stacy!": social interaction, elaboration, and learning with teachable agents

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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Citations
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Proceedings ArticleDOI
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

Journal ArticleDOI
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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Proceedings ArticleDOI
19 Apr 2018
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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Proceedings ArticleDOI
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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Book ChapterDOI
28 Jun 2011
TL;DR: Trends indicate that students who played the game with the off-task interaction had a more positive experience of the game, and that they also learnt more, as reflected in the learning outcomes of their TAs.
Abstract: The paper discusses the addition of off-task socially oriented conversational abilities to an existing "teachable agent" (TA) in an educational game in mathematics. The purpose of this extension is to affect constructs known to promote learning, such as self-efficacy and engagement as well as enhance students' experiences of interacting with the game. A comparison of students that played the game with the off-task interaction to those who played without it, shows trends that indicate that students who played the game with off-task interaction had a more positive experience of the game, and that they also learnt more, as reflected in the learning outcomes of their TAs.

43 citations


""Oh dear stacy!": social interactio..." refers background in this paper

  • ...Some propose that bringing off-task social conversation into educational dialogues may allow for cognitive rest, increase engagement, provide memory cues, and promote trust and rapportbuilding with the agent [12]....

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  • ...[12] developed an interface where an embodied agent learns through either simply observing what the child is doing or requiring the child to explicitly explain the rules using a multiple choice dialogue interface....

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Proceedings Article
02 Jun 2010
TL;DR: This demo abstract presents an interactive tool for supporting error analysis for text mining, which is situated within the Summarization Integrated Development Environment (SIDE).
Abstract: This demo abstract presents an interactive tool for supporting error analysis for text mining, which is situated within the Summarization Integrated Development Environment (SIDE) This freely downloadable tool was designed based on repeated experience teaching text mining over a number of years, and has been successfully tested in that context as a tool for students to use in conjunction with machine learning projects

39 citations


""Oh dear stacy!": social interactio..." refers methods in this paper

  • ...Machine learning was performed using the SIDE text mining toolkit [14]....

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Book ChapterDOI
28 Jun 2011
TL;DR: It was found that students often use inappropriate problems to tutor SimStudent that did not effectively facilitate the tutor learning, and for students with insufficient training on the target problems, learning by teaching may have limited benefits compared to learning by tutored problem solving.
Abstract: This paper describes an application of a machine-learning agent, SimStudent, as a teachable peer learner that allows a student to learn by teaching. SimStudent has been integrated into APLUS (Artificial Peer Learning environment Using SimStudent), an on-line game-like learning environment. The first classroom study was conducted in local public high schools to test the effectiveness of APLUS for learning linear algebra equations. In the study, learning by teaching (i.e., APLUS) was compared with learning by tutored-problem solving (i.e., Cognitive Tutor). The results show that the prior knowledge has a strong influence on tutor learning - for students with insufficient training on the target problems, learning by teaching may have limited benefits compared to learning by tutored problem solving. It was also found that students often use inappropriate problems to tutor SimStudent that did not effectively facilitate the tutor learning.

37 citations


""Oh dear stacy!": social interactio..." refers methods in this paper

  • ...SIMSTUDENT DESCRIPTION Our study was carried out using the SimStudent platform [13]....

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Journal ArticleDOI
TL;DR: The Cognitive Tutor Algebra is extended with a reciprocal peer tutoring activity designed to increase conceptual learning, and peer tutors learned when they reflected on tutee problem-solving actions and tutees learned when the tutor's help was responsive to those actions.
Abstract: Intelligent tutoring systems have been successful at increasing student mathematics learning, but may be further improved with the addition of collaborative activities. We have extended the Cognitive Tutor Algebra, a successful intelligent tutoring system for individual learning, with a reciprocal peer tutoring activity designed to increase conceptual learning. While using our peer tutoring environment, students take on tutor and tutee roles, and engage in both problem-solving actions and dialogue. In a classroom study, we randomly assigned 62 participants to three conditions (adaptive assistance to peer tutoring, fixed assistance to peer tutoring, and individual learning). All conditions yielded significant learning gains, but there were no differences between conditions in final outcomes. There were significant process differences, however. We assessed student interaction using problem-solving information logged by the intelligent tutoring system and collaborative dialogue captured in a chat window. Our analysis integrated these multiple data sources in order to better understand how collaborative dialogue and problem-solving actions might lead to conceptual learning. This rich data sheds light on how students benefitted from the reciprocal peer tutoring activity: Peer tutors learned when they reflected on tutee problem-solving actions, and tutees learned when the tutor's help was responsive to those actions.

37 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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  • ...Among real children, while both tutors and tutees achieve significant learning gains from peer tutoring sessions, peer tutors learn more when their tutees struggle with the material [28]....

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  • ...Making errors is realistic, and there is evidence that in human-human peer tutoring, these are the places where tutors do the most learning [28]....

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  • ...[28] found that tutee errors, while helpful for tutor learning gains, generally lead to less learning for the tutee....

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Proceedings Article
24 Jun 2008
TL;DR: Three studies tested the hypothesis that the mere belief in having a social interaction with someone improves learning, more attention and higher arousal, and suggest that this was not due to social belief per se, but rather in the belief of taking a socially relevant action.
Abstract: Three studies tested the hypothesis that the mere belief in having a social interaction with someone improves learning, more attention and higher arousal. Participants studied a passage on fever mechanisms. They entered a virtual reality (VR) environment and met an embodied agent. The participant either read aloud or silently, scripted questions on the fever passage. In the avatar-aloud and avatar-silent conditions, participants were told that the virtual representation was controlled by a person. The agent condition was told that the virtual representation was a computer program. All interactions within VR were held constant, but the avatar conditions exhibited better learning, more attention, and higher arousal. Further results suggest that this was not due to social belief per se, but rather in the belief of taking a socially relevant action.

33 citations


""Oh dear stacy!": social interactio..." refers background in this paper

  • ...also explored whether the mere belief that a student was interacting with another real person makes a difference in learning gains [15]....

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  • ...On the other hand, previous literature has also hypothesized that it is social factors that motivate the tutor effect [3, 7, 11, 15]....

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