Learn with surprize from a robot professor

Abstract

In the years to come social robots will be extensively used in the teaching field. Consequently, it is important to determine how these robots can optimally interact with students. This paper specifically looks at the performance of social robots in place of university professors, in the field of engineering, measuring three outcomes, enjoyment (questionnaire), the acquisition of knowledge (academic test after class and final exam grades) and level of surprise (facial expressions monitored by cameras). Inspired by previous cognitive studies which have evidenced that feelings of surprise and familiarity affect the learning process, the hypothesis of this research is that robot-tutors will increase student's enjoyment. Leading the research to question whether higher levels of enjoyment strongly correlate with a higher acquisition of knowledge through a series of three experiments. Using the same subject material, three variables were tested: 1. one robot-tutor lecture, 2. one human-tutor lecture, 3. two robot-tutor lectures. Initial findings showed that students lectured by a human-tutor once, acquired a higher level of knowledge compared to those lectured by a robot-tutor once. However, those in the robot-tutor lecture shared higher levels of enjoyment as well as increased expressions of surprise. When students were lectured by a robot-tutor for the second time, results showed both a higher level of acquired knowledge and enjoyment and a drop in expressed surprise, compared to both, one human-tutor lecture and one robot-tutor lecture or one robot-tutor lecture. We conclude that high levels of surprise distracted students initially but once overcome, acted as motivation to study.