  While speech-enabled teachable agents have some advantages over typing-based
ones, they are vulnerable to errors stemming from misrecognition by automatic
speech recognition (ASR). These errors may propagate, resulting in unexpected
changes in the flow of conversation. We analyzed how such changes are linked
with learning gains and learners' rapport with the agents. Our results show
they are not related to learning gains or rapport, regardless of the types of
responses the agents should have returned given the correct input from learners
without ASR errors. We also discuss the implications for optimal error-recovery
policies for teachable agents that can be drawn from these findings.
