If computer-based instructional systems are to reap the benefits of natural language interaction, they must be endowed with the properties that make human natural language interaction so effective. To identify these properties, we replaced the natural language component of an existing Intelligent Tutoring System (ITS) with a human tutor, and gathered protocols of students interacting with the human tutor. W e then compared the human tutor's responses to those that would have been produced by the ITS. In this paper, I describe two critical features that distinguish human tutorial explanations from those of their computational counterparts.

We argue that discourse plans must capture the intended causal and decompositional relations between communicative actions. W e present a planning algorithm, DPOCL , that builds plan structures that property capture these relations, and show how these structures are used to solve the problems that plagued previous discourse planners, and allow a system to participate effectively and flexibly in an ongoing dialogue.