Robots for autistic children - Understanding and facilitating predictability for engagement in learning
Due to the COVID-19 crisis the PhD defence of Bob Schadenberg will take place online (until further notice).
The PhD defence can be followed by a live stream.
Bob Schadenberg is a PhD student in the research group Human Media Interaction (HMI). His supervisor is prof.dr. V. Evers from the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS).
Autism Spectrum Condition (hereafter “autism”) is a lifelong neurodevelopmental condition that affects the way an individual interacts with others and experiences the world around them. Current diagnostic criteria for autism include two core features, namely (a) difficulties in social interaction and communication, and (b) the presence of rigid and repetitive patterns of behaviours and limited personal. As a result of the autism features, autistic individuals often favour more predictable environments, as they generally have difficulty dealing with change. In the context of social skill learning, experiencing discomfort due to dealing with unpredictability is problematic as it prevents children from being in a state where they are ready to learn. Incorporating a robot in social skill learning might be helpful in that it can provide a highly predictable manner of learning social skills, as we can systematically control the predictability of the robot's behaviour. Indeed, the predictability of a robot is a commonly used argument for why robots may be promising tools for autism professionals working with autistic children.
The effectiveness of robot-assisted interventions designed for social skill learning presumably depends --- in part --- on the robot’s predictability. Given its importance, the concept of predictability is currently not well understood. Moreover, while early studies on robots for autistic children have found that the robot can pique the children's interest and improve engagement in interventions, designing robots to sustain long-term engagement that leads to learning is difficult. The children are very different from each other in how autism affects the development of their cognitive, language, and intellectual ability, which needs to be taken into account for the child-robot interaction.
In my dissertation, I investigate how we can design robots in such a way that they can facilitate engagement. We specifically looked at how the individual differences between autistic children influence the way they interact with a robot. Another major topic in my dissertation is that of the concept of predictability. We provided a novel conceptualization and studied how a robot’s predictability influences our social perception, as well as how it influences the engagement of autistic children.