An official website of the United States government
Teachable agents are pedagogical agents that employ the 'learning-by-teaching' strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a 'learning-by-teaching' experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.
more »
« less
Are You Feeling Happy? the Effect of Emotions on People’s Interaction Experience Toward Empathetic Chatbots
Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states.
more »
« less
- Award ID(s):
- 2105169
- PAR ID:
- 10437240
- Date Published:
- Journal Name:
- ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.more » « less
-
The overall goal of our research is to develop a system of intelligent multimodal affective pedagogical agents that are effective for different types of learners (Adamo et al., 2021). While most of the research on pedagogical agents tends to focus on the cognitive aspects of online learning and instruction, this project explores the less-studied role of affective (or emotional) factors. We aim to design believable animated agents that can convey realistic, natural emotions through speech, facial expressions, and body gestures and that can react to the students’ detected emotional states with emotional intelligence. Within the context of this goal, the specific objective of the work reported in the paper was to examine the extent to which the agents’ facial micro-expressions affect students’ perception of the agents’ emotions and their naturalness. Micro-expressions are very brief facial expressions that occur when a person either deliberately or unconsciously conceals an emotion being felt (Ekman &Friesen, 1969). Our assumption is that if the animated agents display facial micro expressions in addition to macro expressions, they will convey higher expressive richness and naturalness to the viewer, as “the agents can possess two emotional streams, one based on interaction with the viewer and the other based on their own internal state, or situation” (Queiroz et al. 2014, p.2).The work reported in the paper involved two studies with human subjects. The objectives of the first study were to examine whether people can recognize micro-expressions (in isolation) in animated agents, and whether there are differences in recognition based on the agent’s visual style (e.g., stylized versus realistic). The objectives of the second study were to investigate whether people can recognize the animated agents’ micro-expressions when integrated with macro-expressions, the extent to which the presence of micro + macro-expressions affect the perceived expressivity and naturalness of the animated agents, the extent to which exaggerating the micro expressions, e.g. increasing the amplitude of the animated facial displacements affects emotion recognition and perceived agent naturalness and emotional expressivity, and whether there are differences based on the agent’s design characteristics. In the first study, 15 participants watched eight micro-expression animations representing four different emotions (happy, sad, fear, surprised). Four animations featured a stylized agent and four a realistic agent. For each animation, subjects were asked to identify the agent’s emotion conveyed by the micro-expression. In the second study, 234 participants watched three sets of eight animation clips (24 clips in total, 12 clips per agent). Four animations for each agent featured the character performing macro-expressions only, four animations for each agent featured the character performing macro- + micro-expressions without exaggeration, and four animations for each agent featured the agent performing macro + micro-expressions with exaggeration. Participants were asked to recognize the true emotion of the agent and rate the emotional expressivity ad naturalness of the agent in each clip using a 5-point Likert scale. We have collected all the data and completed the statistical analysis. Findings and discussion, implications for research and practice, and suggestions for future work will be reported in the full paper. ReferencesAdamo N., Benes, B., Mayer, R., Lei, X., Meyer, Z., &Lawson, A. (2021). Multimodal Affective Pedagogical Agents for Different Types of Learners. In: Russo D., Ahram T., Karwowski W., Di Bucchianico G., Taiar R. (eds) Intelligent Human Systems Integration 2021. IHSI 2021. Advances in Intelligent Systems and Computing, 1322. Springer, Cham. https://doi.org/10.1007/978-3-030-68017-6_33Ekman, P., &Friesen, W. V. (1969, February). Nonverbal leakage and clues to deception. Psychiatry, 32(1), 88–106. https://doi.org/10.1080/00332747.1969.11023575 Queiroz, R. B., Musse, S. R., &Badler, N. I. (2014). Investigating Macroexpressions and Microexpressions in Computer Graphics Animated Faces. Presence, 23(2), 191-208. http://dx.doi.org/10.1162/more » « less
-
Chatbots are often designed to mimic social roles attributed to humans. However, little is known about the impact of using language that fails to conform to the associated social role. Our research draws on sociolinguistic to investigate how a chatbot’s language choices can adhere to the expected social role the agent performs within a context. We seek to understand whether chatbots design should account for linguistic register. This research analyzes how register differences play a role in shaping the user’s perception of the human-chatbot interaction. We produced parallel corpora of conversations in the tourism domain with similar content and varying register characteristics and evaluated users’ preferences of chatbot’s linguistic choices in terms of appropriateness, credibility, and user experience. Our results show that register characteristics are strong predictors of user’s preferences, which points to the needs of designing chatbots with register-appropriate language to improve acceptance and users’ perceptions of chatbot interactions.more » « less
-
In this paper we describe the iterative evaluation and refinement of a consent flow for a chatbot being developed by a large U.S. health insurance company. This chatbot’s use of a cloud service provider triggers a requirement for users to agree to a HIPAA authorization. We highlight remote usability study and online survey findings indicating that simplifying the interface and language of the consent flow can improve the user experience and help users who read the content understand how their data may be used. However, we observe that most users in our studies, even those using our improved consent flows, missed important information in the authorization until we asked them to review it again. We also show that many people are overconfident about the privacy and security of healthcare data and that many people believe HIPAA protects in far more contexts than it actually does. Given that our redesigns following best practices did not produce many meaningful improvements in informed consent, we argue for the need for research on alternate approaches to health data disclosures such as standardized disclosures; methods borrowed from clinical research contexts such as multimedia formats, quizzes, and conversational approaches; and automated privacy assistants.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2022-91059
