A Reusable Interaction Management Module: Use case for Empathic Robotic Tutoring

We demonstrate the workings of a stochastic Interaction Management and showcase this working as part of a learning environment that includes a robotic tutor who interacts with students, helping them through a pedagogical task

Towards more humane machines: creating emotional social robots

Robots are now widely used in industrial settings, and today the world has woken up to the impact that they will have in our society. But robots have been limited to repetitive, industrial tasks. However, recent platforms are becoming more secure to operate amongst humans, and research in Human-Robot Interaction (HRI) is preparing robots for use in schools, public services and eventually everyone’s home. If we aim for a robot flexible enough to work around humans and decide autonomously how to act in complex situations, a notion of morality is needed for their decision making. In this chapter we argue that we can achieve some level of moral decision making in social robots if they are endowed with empathy capabilities. We then discuss how to build artificial empathy in robots, giving some concrete examples of how these implementations can guide the path to creating moral social robots in the future.info:eu-repo/semantics/acceptedVersio

The interaction between voice and appearance in the embodiment of a robot tutor

Robot embodiment is, by its very nature, holistic and understanding how various aspects contribute to the user perception of the robot is non-trivial. A study is presented here that investigates whether there is an interaction effect between voice and other aspects of embodiment, such as movement and appearance, in a pedagogical setting. An on-line study was distributed to children aged 11–17 that uses a modified Godspeed questionnaire. We show an interaction effect between the robot embodiment and voice in terms of perceived lifelikeness of the robot. Politeness is a key strategy used in learning and teaching, and here an effect is also observed for perceived politeness. Interestingly, participants’ overall preference was for embodiment combinations that are deemed polite and more like a teacher, but are not necessarily the most lifelike. From these findings, we are able to inform the design of robotic tutors going forward

Effective Tutoring with Empathic Embodied Conversational Agents

This thesis examines the prospect of using empathy in an Embodied Tutoring System (ETS) that guides students through an online quiz (by providing feedback on student answers and responding to self-reported student emotion). The ETS seeks to imitate human behaviours successfully used in one-to-one human tutorial interactions. The main hypothesis is that the interaction with an empathic ETS results in greater learning gains than a neutral ETS, primarily by encouraging positive and reducing negative student emotions using empathic feedback. In a preparatory study we investigated different strategies for expressing emotion by the ETS. We established that a multimodal strategy achieves the best results regarding how accurately human participants can recognise the emotions. This approach was used in developing the feedback strategy for our empathic ETS. The preparatory study was followed by two studies in which we compared a neutral with an empathic ETS. The ETS in the second of these studies was developed using results from the first of these studies. In both studies, we found no statistically significant difference in learning gains between the neutral and empathic ETS. However, we did discover a number of interactions between the ETS system, learning gains and, in particular 1) student scores on an empathic tendency test and 2) student ability. We also analysed the subjective responses and the relation between self-reported emotions during the quiz and student learning gains. Based on our studies in a formal class room setting, we assess the prospects of using empathic agents in a classroom setting and describe a number of requirements for their effective use

Adapting Progress Feedback and Emotional Support to Learner Personality

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The State and Use of Virtual Tutors

Virtual tutoring is the process by which students and teachers participate in the learning experience in an online, virtual, or networked environment. This process can not only separate the participants from each other in a physical space, but it can also separate them by time. Virtual tutoring can take the form of the group of students coming together synchronously in an online setting and receiving lessons from a single tutor, or by asynchronous learning in which the teacher pre-plans lessons in advance that the students consume on their own time. The advent of online learning technologies and virtual learning environments are gaining significant attention, and are likely to become a key aspect of teaching and learning at all levels of education. With the recent advancements in technology and especially artificial intelligence, the state of the art of virtual tutoring is becoming more and more advanced as well. In this literature review, I will propose the question of \u27\u27What are the current uses and state of the art of virtual tutoring?\u27\u2

No Grice: Computers that Lie, Deceive and Conceal

In the future our daily life interactions with other people, with computers, robots and smart environments will be recorded and interpreted by computers or embedded intelligence in environments, furniture, robots, displays, and wearables. These sensors record our activities, our behavior, and our interactions. Fusion of such information and reasoning about such information makes it possible, using computational models of human behavior and activities, to provide context- and person-aware interpretations of human behavior and activities, including determination of attitudes, moods, and emotions. Sensors include cameras, microphones, eye trackers, position and proximity sensors, tactile or smell sensors, et cetera. Sensors can be embedded in an environment, but they can also move around, for example, if they are part of a mobile social robot or if they are part of devices we carry around or are embedded in our clothes or body. \ud \ud Our daily life behavior and daily life interactions are recorded and interpreted. How can we use such environments and how can such environments use us? Do we always want to cooperate with these environments; do these environments always want to cooperate with us? In this paper we argue that there are many reasons that users or rather human partners of these environments do want to keep information about their intentions and their emotions hidden from these smart environments. On the other hand, their artificial interaction partner may have similar reasons to not give away all information they have or to treat their human partner as an opponent rather than someone that has to be supported by smart technology.\ud \ud This will be elaborated in this paper. We will survey examples of human-computer interactions where there is not necessarily a goal to be explicit about intentions and feelings. In subsequent sections we will look at (1) the computer as a conversational partner, (2) the computer as a butler or diary companion, (3) the computer as a teacher or a trainer, acting in a virtual training environment (a serious game), (4) sports applications (that are not necessarily different from serious game or education environments), and games and entertainment applications

Social robots in educational contexts: developing an application in enactive didactics

Due to advancements in sensor and actuator technology robots are becoming more and more common in everyday life. Many of the areas in which they are introduced demand close physical and social contact. In the last ten years the use of robots has also increasingly spread to the field of didactics, starting with their use as tools in STEM education. With the advancement of social robotics, the use of robots in didactics has been extended also to tutoring situations in which these \u201csocially aware\u201d robots interact with mainly children in, for example, language learning classes. In this paper we will give a brief overview of how robots have been used in this kind of settings until now. As a result it will become transparent that the majority of applications are not grounded in didactic theory. Recognizing this shortcoming, we propose a theory driven approach to the use of educational robots, centred on the idea that the combination of enactive didactics and social robotics holds great promises for a variety of tutoring activities in educational contexts. After defining our \u201cEnactive Robot Assisted Didactics\u201d approach, we will give an outlook on how the use of humanoid robots can advance it. On this basis, at the end of the paper, we will describe a concrete, currently on-going implementation of this approach, which we are realizing with the use of Softbank Robotics\u2019 Pepper robot during university lectures

Towards Dialogue Dimensions for a Robotic Tutor in Collaborative Learning Scenarios

There has been some studies in applying robots to education and recent research on socially intelligent robots show robots as partners that collaborate with people. On the other hand, serious games and interaction technologies have also proved to be important pedagogical tools, enhancing collaboration and interest in the learning process. This paper relates to the collaborative scenario in EMOTE EU FP7 project and its main goal is to develop and present the dialogue dimensions for a robotic tutor in a collaborative learning scenario grounded in human studies. Overall, seven dialogue dimensions between the teacher and students interaction were identified from data collected over 10 sessions of a collaborative serious game. Preliminary results regarding the teachers perspective of the students interaction suggest that student collaboration led to learning during the game. Besides, students seem to have learned a number of concepts as they played the game. We also present the protocol that was followed for the purposes of future data collection in human-human and human-robot interaction in similar scenarios