Does It Affect You? Social and Learning Implications of Using Cognitive-Affective State Recognition for Proactive Human-Robot Tutoring

摘要

Robotic technology has proven to be advantageous for student learning and social development in educational settings. However, in order to enhance their effectiveness and provide a more human-like tutoring experience, robots must be capable of adapting to the user and exhibiting proactivity. By acting proactively, these intelligent robotic tutors can anticipate potential obstacles and take preventative measures to avoid negative outcomes. However, determining when and how to behave proactively remains an open question. This study investigates how a robotic tutor can utilize a student’s cognitive-affective states to trigger proactive tutoring dialogue and improve the learning experience. Specifically, we observed a concept learning task scenario where a robotic assistant proactively assisted the user when negative states, such as frustration and confusion, were detected. In an empirical study involving 40 undergraduate and doctoral students, we evaluated whether the initiation of proactive behavior after the detection of signs of confusion and frustration improves the student’s concentration and trust in the robot. We also examined which level of proactive dialogue is most effective for promoting concentration and trust. The results indicate that high levels of proactive behavior can harm trust, especially when triggered during negative cognitive-affective states. However, this behavior does contribute to keeping the student focused on the task when triggered during these states. Based on our findings, we discuss potential future steps for improving the proactive assistance of robotic tutoring systems.