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This paper addresses some of the problems that arise when applying active learning to the context of human-robot interaction (HRI). Active learning is an attractive strategy for robot learners because it has the potential to improve the accuracy and the speed of learning, but it can cause issues from an interaction perspective. Here we present three interaction modes that enable a robot to use active learning queries. The three modes differ in when they make queries: the first makes a query every turn, the second makes a query only under certain conditions, and the third makes a query only when explicitly requested by the teacher. We conduct an experiment in which 24 human subjects teach concepts to our upper-torso humanoid robot, Simon, in each interaction mode, and we compare these modes against a baseline mode using only passive supervised learning. We report results from both a learning and an interaction perspective. The data show that the three modes using active learning are preferable to the mode using passive supervised learning both in terms of performance and human subject preference, but each mode has advantages and disadvantages. Based on our results, we lay out several guidelines that can inform the design of future robotic systems that use active learning in an HRI setting.