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This paper describes an approach for developing intelligent tutoring systems (ITS) for teaching clinical reasoning.

Our approach to ITS for clinical reasoning uses a novel hybrid knowledge representation for the pedagogic model, combining finite state machines to model different phases in the diagnostic process, production rules to model triggering conditions for feedback in different phases, temporal logic to express triggering conditions based upon past states of the student's problem solving trace, and finite state machines to model feedback dialogues between the student and TeachMed. The expert model is represented by an influence diagram capturing the relationship between evidence and hypotheses related to a clinical case.

This approach is implemented into TeachMed, a patient simulator we are developing to support clinical reasoning learning for a …