Affective Expressions in Conversational Agents for Learning Environments: Effects of curiosity, humour, and expressive auditory gestures

Conversational agents -- systems that imitate natural language discourse -- are becoming an increasingly prevalent human-computer interface, being employed in various domains including healthcare, customer service, and education. In education, conversational agents, also known as pedagogical agents, can be used to encourage interaction; which is considered crucial for the learning process. Though pedagogical agents have been designed for learners of diverse age groups and subject matter, they retain the overarching goal of eliciting learning outcomes, which can be broken down into cognitive, skill-based, and affective outcomes. Motivation is a particularly important affective outcome, as it can influence what, when, and how we learn. Understanding, supporting, and designing for motivation is therefore of great importance for the advancement of learning technologies. This thesis investigates how pedagogical agents can promote motivation in learners. Prior research has explored various features of the design of pedagogical agents and what effects they have on learning outcomes, and suggests that agents using social cues can adapt the learning environment to enhance both affective and cognitive outcomes. One social cue that is suggested to be of importance for enhancing learner motivation is the expression or simulation of affect in the agent. Informed by research and theory across multiple domains, three affective expressions are investigated: curiosity, humour, and expressive auditory gestures -- each aimed at enhancing motivation by adapting the learning environment in different ways, i.e., eliciting contagion effects, creating a positive learning experience, and strengthening the learner-agent relationship, respectively. Three studies are presented in which each expression was implemented in a separate type of agent: physically-embodied, text-based, and voice-based; with all agents taking on the role of a companion or less knowledgeable peer to the learner. The overall focus is on how each expression can be displayed, what the effects are on perception of the agent, and how it influences behaviour and learning outcomes. The studies result in theoretical contributions that add to our understanding of conversational agent design for learning environments. The findings provide support for: the simulation of curiosity, the use of certain humour styles, and the addition of expressive auditory gestures, in enhancing motivation in learners interacting with conversational agents; as well as indicating a need for further exploration of these strategies in future work

Towards Measuring states of curiosity through Electroencephalography and body sensors responses

International audienceThe neurophysiological mechanisms underlying curiosity and intrinsic motivation are currently not well understood. However, being able to identify objectively, from neurophysiological signals, the curiosity level of a user, would bring a very useful tool both to neuroscientists and psychologists, to understand curiosity deeper, as well as to designers of human-computer interaction, in order to trigger curiosity or to adapt an interaction to the curiosity levels of its users. A first step to do that, is to collect neurophysiological signals during known states of curiosity, in order to develop signal processing/machine learning tools to recognize those states from such signals. We propose an experimental protocol, that has been designed but has not been tested so far, in order to measure both brain activity through Electroencephalography (EEG) and physiological responses (heart rate, skin conductance, Electrocardiogram) when subjects are induced into different states of curiosity. During the experiment, fun facts will be presented to subjects to induce different levels of curiosity. We obtained those fun facts using the Google functionality "I’m feeling curious" as well as crowdsourcing. A subject will be able to choose a fun fact that makes him curious, and push forward with a 4-to-10 questions chain on this theme. For each question on a given theme, a subject will be able to reveal the answer (interpreted as a curious state) or to skip it (interpreted as a non-curious state). Skipping an answer will automatically break the chain and will point the subject to the next fun fact. Neurophysiological signals will be collected between a question and the choice of revealing the answer. Then the subject will grade the question on a 1-to-7 curiosity level scale. Neurophysiological measures during these states of curiosity will be recorded and we expect to find biological markers of curiosity by analyzing such information

Identifying Functions and Behaviours of Social Robots for In-Class Learning Activities: Teachers’ Perspective

International audienceWith advances in artificial intelligence, research is increasingly exploring the potential functions that social robots can play in education. As teachers are a critical stakeholder in the use and application of educational technologies, we conducted a study to understand teachers' perspectives on how a social robot could support a variety of learning activities in the classroom. Through interviews, robot puppeteering, and group brainstorming sessions with five elementary and middle school teachers, we take a sociotechnical perspective to conceptualize possible robot functions and behaviours, and the effects they may have on the current way learning activities are designed, planned, and executed. Using activity theory to analyze learning activities as an activity system illustrated a number of tensions that currently exist between the components of the system. Overall, the teachers responded positively to the idea of introducing a social robot as a technological tool for learning activities, envisioning differences in usage for teacher-robot and student-robot interactions. We discuss the fine-grained functions and behaviours envisioned by teachers, and how they address the current tensions-providing suggestions for improving the design of social robots for learning activities

Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour

International audienceCuriosity -the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can aect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classication, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion eects in participants

Towards measuring states of epistemic curiosity through electroencephalographic signals

International audienceUnderstanding the neurophysiological mechanisms underlying curiosity and therefore being able to identify the curiosity level of a person, would provide useful information for researchers and designers in numerous fields such as neuroscience, psychology, and computer science. A first step to uncovering the neural correlates of curiosity is to collect neurophysiological signals during states of curiosity, in order to develop signal processing and machine learning (ML) tools to recognize the curious states from the non-curious ones. Thus, we ran an experiment in which we used electroencephalography (EEG) to measure the brain activity of participants as they were induced into states of curiosity, using trivia question and answer chains. We used two ML algorithms, i.e. Filter Bank Common Spatial Pattern (FBCSP) coupled with a Linear Discriminant Algorithm (LDA), as well as a Filter Bank Tangent Space Classifier (FBTSC), to classify the curious EEG signals from the non-curious ones. Global results indicate that both algorithms obtained better performances in the 3-to-5s time windows, suggesting an optimal time window length of 4 seconds (63.09% classification accuracy for the FBTSC, 60.93% classification accuracy for the FBCSP+LDA) to go towards curiosity states estimation based on EEG signals

The discrimination of voice cues in simulations of bimodal electro-acoustic cochlear-implant hearing

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Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour

International audienceCuriosity -the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can aect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classication, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion eects in participants