Abstract
This paper presents an approach to intelligent tutoring for diagnostic problem solving that uses knowledge about causal relationships between symptoms and disease states to conduct a pedagogically useful dialogue with the student. An animated pedagogical agent, Adele, uses the causal knowledge, represented as a Bayesian network, to dynamically generate a diagnostic process that is consistent with the best practice approach to medical diagnosis. Using a combination of hints and other interactions based on multiple choice questions, Adele guides the student through a reasoning process that exposes her to the underlying knowledge, i.e., the patho-physiological processes, while being sensitive to the problem solving state and the student’s current level of knowledge. Although the main focus of this paper is on tutoring medical diagnosis, the methods described here are applicable to tutoring diagnostic skills in any domain with uncertain knowledge.
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Ganeshan, R., Johnson, W.L., Shaw, E., Wood, B.P. (2000). Tutoring Diagnostic Problem Solving. In: Gauthier, G., Frasson, C., VanLehn, K. (eds) Intelligent Tutoring Systems. ITS 2000. Lecture Notes in Computer Science, vol 1839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45108-0_7
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DOI: https://doi.org/10.1007/3-540-45108-0_7
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