Designing robot personalities for human-robot symbiotic interaction in an educational context

The Expressive Agents for Symbiotic Education and Learning project explores human-robot symbiotic interaction with the aim to understand the development of symbiosis over long-term tutoring interactions. The final EASEL system will be built upon the neurobiologically grounded architecture - Distributed Adaptive Control. In this paper, we present the design of an interaction scenario to support development of the DAC, in the context of a synthetic tutoring assistant. Our humanoid robot, capable of life-like simulated facial expressions, will interact with children in a public setting to teach them about exercise and energy. We discuss the range of measurements used to explore children’s responses during, and experiences of, interaction with a social, expressive robot

Children's age influences their use of biological and mechanical questions towards a humanoid

Complex autonomous interactions, biomimetic appearances, and responsive behaviours are increasingly seen in social robots. These features, by design or otherwise, may substantially influence young children’s beliefs of a robot’s animacy. Young children are believed to hold naive theories of animacy, and can miscategorise objects as living agents with intentions; however, this develops with age to a biological understanding. Prior research indicates that children frequently categorise a responsive humanoid as being a hybrid of person and machine; although, with age, children tend towards classifying the humanoid as being more machine-like. Our current research explores this phenomenon, using an unobtrusive method: recording childrens conversational interaction with the humanoid and classifying indications of animacy beliefs in childrens questions asked. Our results indicate that established findings are not an artefact of prior research methods: young children tend to converse with the humanoid as if it is more animate than older children do

Learning from unstructured child-robot interactions

This reflective piece highlights some unexpected outcomes observed during selected Child-Robot Interaction (CRI) studies. As these were peripheral to the investigations underway, they were not included in related publications, yet they have been instrumental in directing subsequent research. We advise new researchers of the value of an open interactive environment in CRI studies, and careful observation of interactions, even when adjacent to the research question

Towards a Virtual Collaborator in Online Collaboration from an Organizations’ Perspective

In this empiric study, we present the specifications of virtual collaboration in times of the Covid-19 pandemic in an organization that worked mostly co-located beforehand, and requirements for a virtual collaborator (VC) resulting from those specifications. Related work shows that a VCs can support virtual teams in achieving their goals and promote creative work. We extend this with insights from practice by observing creative and collaborative workshops in the automotive industry and conducting interviews with facilitators and participants of these workshops. Subsequently, we identify the challenges that participants face in virtual collaboration, and derive design guidelines for a VC to address them. Main problems arise due to the virtual interaction lacking nonverbal communication and in the preparation phase that requires more planning and effort. A VC could help by influencing group cohesion and build networks between the participants, influencing the virtual working environment as well as contributing to the contents

EXAIT: Educational eXplainable Artificial Intelligent Tools for personalized learning

As artificial intelligence systems increasingly make high-stakes recommendations and decisions automatically in many facets of our lives, the use of explainable artificial intelligence to inform stakeholders about the reasons behind such systems has been gaining much attention in a wide range of fields, including education. Also, in the field of education there has been a long history of research into self-explanation, where students explain the process of their answers. This has been recognized as a beneficial intervention to promote metacognitive skills, however, there is also unexplored potential to gain insight into the problems that learners experience due to inadequate prerequisite knowledge and skills that are required, or in the process of their application to the task at hand. While this aspect of self-explanation has been of interest to teachers, there is little research into the use of such information to inform educational AI systems. In this paper, we propose a system in which both students and the AI system explain to each other their reasons behind decisions that were made, such as: self-explanation of student cognition during the answering process, and explanation of recommendations based on internal mechanizes and other abstract representations of model algorithms

The effects of robot facial emotional expressions and gender on child-robot interaction in a field study

Emotions, and emotional expression, have a broad influence on social interactions and are thus a key factor to consider in developing social robots. This study examined the impact of life-like affective facial expressions, in the humanoid robot Zeno, on children’s behaviour and attitudes towards the robot. Results indicate that robot expressions have mixed effects depending on participant gender. Male participants interacting with a responsive facially expressive robot showed a positive affective response and indicated greater liking towards the robot, compared to those interacting with the same robot maintaining a neutral expression. Female participants showed no marked difference across the conditions. We discuss the broader implications of these findings in terms of gender differences in human–robot interaction, noting the importance of the gender appearance in robots (in this case, male) and in relation to advancing the understanding of how interactions with expressive robots could lead to task-appropriate symbiotic relationships

Human-Machine Communication: Complete Volume. Volume 2

This is the complete volume of HMC Volume 2