Proceedings of the Annual Meeting of the Cognitive Science Society

To communicate effectively, intelligent tutoring systems should be able to generate clear explanations of phenomena in their domain. To explain complex phenomena in scientific domains, they must be able to produce extensive multiparagraph discourse. Traditionally, discourse planners have taken a monoiomc approach to generation: once they make a decision, that decision is never revoked. Because these approaches make no provision for evaluating and modifying a plan after it has been constructed, their flexibility is limited. Tiiis inflexibility is particularly acute when attempting to generate multi-paragraph discourse. W e propose a revision-based model of discourse planning that constructs instructional multiparagraph discourse plans, evaluates them, and restructures them. This is accomplished by delaying organizational commitments as long as possible and interleaving the planner's content determination and organization activities. This model accords well with research on writing. It has been implemented in an experimental system, K N I G H T , a discourse generator for intelligent tutoring systems. K N I G H T generates multiparagraph explanations in the domain of biology. A domain expert hais analyzed KNIGHT's explanations and found them to be clear and accurate.