We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.