  Using robots in educational contexts has already shown to be beneficial for a
student's learning and social behaviour. For levitating them to the next level
of providing more effective and human-like tutoring, the ability to adapt to
the user and to express proactivity is fundamental. By acting proactively,
intelligent robotic tutors anticipate possible situations where problems for
the 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's
cognitive-affective states can be used by a robotic tutor for triggering
proactive tutoring dialogue. In doing so, it is aimed to improve the learning
experience. For this reason, a concept learning task scenario was observed
where a robotic assistant proactively helped when negative user states were
detected. In a learning task, the user's states of frustration and confusion
were deemed to have negative effects on the outcome of the task and were used
to trigger proactive behaviour. In an empirical user study with 40
undergraduate and doctoral students, we studied whether the initiation of
proactive behaviour after the detection of signs of confusion and frustration
improves the student's concentration and trust in the agent. Additionally, we
investigated which level of proactive dialogue is useful for promoting the
student's concentration and trust. The results show that high proactive
behaviour harms trust, especially when triggered during negative
cognitive-affective states but contributes to keeping the student focused on
the task when triggered in these states. Based on our study results, we further
discuss future steps for improving the proactive assistance of robotic tutoring
systems.
