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Using robots in educational contexts has already shown to be beneficial for astudent's learning and social behaviour. For levitating them to the next levelof providing more effective and human-like tutoring, the ability to adapt tothe user and to express proactivity is fundamental. By acting proactively,intelligent robotic tutors anticipate possible situations where problems forthe student may arise and act in advance for preventing negative outcomes.Still, the decisions of when and how to behave proactively are open questions.Therefore, this paper deals with the investigation of how the student'scognitive-affective states can be used by a robotic tutor for triggeringproactive tutoring dialogue. In doing so, it is aimed to improve the learningexperience. For this reason, a concept learning task scenario was observedwhere a robotic assistant proactively helped when negative user states weredetected. In a learning task, the user's states of frustration and confusionwere deemed to have negative effects on the outcome of the task and were usedto trigger proactive behaviour. In an empirical user study with 40undergraduate and doctoral students, we studied whether the initiation ofproactive behaviour after the detection of signs of confusion and frustrationimproves the student's concentration and trust in the agent. Additionally, weinvestigated which level of proactive dialogue is useful for promoting thestudent's concentration and trust. The results show that high proactivebehaviour harms trust, especially when triggered during negativecognitive-affective states but contributes to keeping the student focused onthe task when triggered in these states. Based on our study results, we furtherdiscuss future steps for improving the proactive assistance of robotic tutoringsystems.

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