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The research presented herein addresses the topic of explainability in autonomous pedagogical agents. We will be investigating possible ways to explain the decision-making process of such pedagogical agents (which can be embodied as robots) with a focus on the effect of these explanations in concrete learning scenarios for children. The hypothesis is that the agents' explanations about their decision making will support mutual modeling and a better understanding of the learning tasks and how learners perceive them. The objective is to develop a computational model that will allow agents to express internal states and actions and adapt to the human expectations of cooperative behavior accordingly. In addition, we would like to provide a comprehensive taxonomy of both the desiderata and methods in the explainable AI research applied to children's learning scenarios.