Adaptivity in learner-teacher interaction

In social interactions between human learners and teachers, it is of central importance that the teacher develops a model of the learner that refers to knowledge, motivation, and emotion of the learner - such a model allows to match tasks and the provision of learning support to the learner. In turn, the learner needs to develop a model of the teacher that informs about the knowledge, teaching style and individual differences such as trustworthiness or patience of the teacher. The development of such a mutual understanding is a core component of successful social interactions in learning contexts. However, it is empirically not well researched in instructional psychology how learners and teachers build such mutual models of each other–and thus, how tasks, materials, learning content and social interactions can be adapted to the characteristics of the learner or the teacher. In robotics, on the other hand, the characteristics of synthetic learners and teachers can be changed, therefore the question is more, how the characteristics of learners and teachers can optimally be adapted to the requirements of the task, the current context, and specific characteristics of their interaction partners. In this project, we aim to investigate whether and how principles of adaptive social interaction among humans can be transferred to social interactions between humans and artificial agents (HRI), and between artificial agents (RRI).

The subproject focuses on adaptive learning strategies in Human-Robot Interaction and in Robot-Robot Interaction. One aim is to develop adaptive learning strategies in robots that can guide the learning process depending on the context, task difficulty, current knowledge of the robot and specific characteristics of the teacher. We will investigate learning and exploration strategies such as intrinsic motivation systems and evaluate them based on learning success and insights from learning theories in humans.