Knowledge-based tutoring: the GUIDON program
Knowledge-based tutoring: the GUIDON program
Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
The temporal logic of reactive and concurrent systems
The temporal logic of reactive and concurrent systems
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Artificial Intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Tutoring Diagnostic Problem Solving
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
A collaborative intelligent tutoring system for medical problem-based learning
Proceedings of the 9th international conference on Intelligent user interfaces
Learning Bayesian Networks
An intelligent tutoring system for visual classification problem solving
Artificial Intelligence in Medicine
Clinical reasoning learning with simulated patients
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
A Human-Machine Cooperative Approach for Time Series Data Interpretation
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
Guest editorial: Intelligent medical training systems
Artificial Intelligence in Medicine
Persuasive dialogues in an intelligent tutoring system for medical diagnosis
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Delivering tutoring feedback using persuasive dialogues
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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Objective: This paper describes an approach for developing intelligent tutoring systems (ITS) for teaching clinical reasoning. Materials and methods: 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. Results: This approach is implemented into TeachMed, a patient simulator we are developing to support clinical reasoning learning for a problem-based learning medical curriculum at our institution; we demonstrate some scenarios of tutoring feedback generated using this approach. Conclusion: Each of the knowledge representation formalisms that we use has already been proven successful in different applications of artificial intelligence and software engineering, but their integration into a coherent pedagogic model as we propose is unique. The examples we discuss illustrate the effectiveness of this approach, making it promising for the development of complex ITS, not only for clinical reasoning learning, but potentially for other domains as well.