Exploring elicitation frequency of learning-sensitive information by a robotic tutor for interactive personalization

We formalize and analyze a robotic tutor's elicitation of learning-sensitive information to be leveraged by interactive machine learning methods for personalized education. The user study presented in this paper is an initial exploration of elicitation frequency of learning sensitive information, and the social and computational implications thereof. Our results, evaluated using a variety of subjective measures, demonstrate that a humans-in-the-loop approach positively benefits the human-robotic tutor interaction, while minimizing the computational complexity of personalization.