Sabine Seufert

Due to advances in machine learning (ML) and artificial intelligence (AI), computer systems are becoming increasingly intelligent and capable of taking on new tasks (e.g., automatic translation of texts). In education, such AI-powered smart machines (e.g., chatbots, social robots) have the potential to support teachers in the classroom in order to improve the quality of teaching. However, from a teacher’s point of view, it may be unclear which subtasks could be best outsourced to the smart machine. Considering human augmentation, this paper presents a theoretical basis for the use of smart machines in education. It highlights the relative strengths of teachers and smart machines in the classroom and proposes a staged process for assigning classroom tasks. The derived task allocation process can be characterized by its three main steps of 1) break-down of task sequence and rethinking the existing task structure, 2) invariable task assignment (normative and technical considerations), and 3) variable task assignment (efficiency considerations). Based on the comparative strengths of both parties, the derived process ensures that subtasks are assigned as efficiently as possible (variable task assignment), while always granting priority to subtasks of normative importance (invariable task assignment). In this way, the derived task allocation process can serve as a guideline for the design and the implementation of smart machine projects in education.

Social robots are increasingly being used in education. They can take over various roles including teaching assistant, tutor, and novice. This chapter aims to provide a conceptual overview of the phenomenon. A classification of social robots is outlined; the criteria are: visual appearance, social capabilities, and autonomy and intelligence. The majority of robots used in education are humanoid; Nao from SoftBank Robotics is a quasi-standard type. An important social capability is empathy; a model illustrating how a robot can show empathy is discussed. A taxonomy is presented in order to capture the various degrees of robot autonomy. To achieve autonomy, artificial intelligence is necessary. This chapter advocates for a symbiotic design approach where tasks are collaboratively carried out by the teacher and the social robot, utilizing the complementary strength of both parties. This may be in line with the concept of hybrid intelligence. The ethical aspects of social robot use are explored, including privacy, control, responsibility, and the role of teachers. Moreover, the acceptance of social robots is discussed. Overall, attitudes towards social robots seem to be positive; however, there are also contrary findings. Finally, results are presented from a technology acceptance study with a sample of N = 462 university students from the social sciences. The chapter closes with suggestions for further research.