An intelligent tutoring system for visual classification problem solving

OBJECTIVE This manuscript describes the development of a general intelligent tutoring system for teaching visual classification problem solving. MATERIALS AND METHODS The approach is informed by cognitive theory, previous empirical work on expertise in diagnostic problem-solving, and our own prior work describing the development of expertise in pathology. The architecture incorporates aspects of cognitive tutoring system and knowledge-based system design within the framework of the unified problem-solving method description language component model. Based on the domain ontology, domain task ontology and case data, the abstract problem-solving methods of the expert model create a dynamic solution graph. Student interaction with the solution graph is filtered through an instructional layer, which is created by a second set of abstract problem-solving methods and pedagogic ontologies, in response to the current state of the student model. RESULTS In this paper, we outline the empirically derived requirements and design principles, describe the knowledge representation and dynamic solution graph, detail the functioning of the instructional layer, and demonstrate two implemented interfaces to the system. CONCLUSION Using the general visual classification tutor, we have created SlideTutor, a tutoring system for microscopic diagnosis of inflammatory diseases of skin.

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