We describe two tutorial dialogue systems that adapt techniques from task-oriented dialogue systems to tutorial dialogue. Both systems employ the same reusable deep natural language understanding and generation components to interpret students' written utterances and to automatically generate adaptive tutorial responses, with separate domain reasoners to provide the necessary knowledge about the correctness of student answers and hinting strategies. We focus on integrating the domain-independent language processing components with domain-specific reasoning and tutorial components in order to improve the dialogue interaction, and present a preliminary analysis of BeeDiff's evaluation.