Enriching Solution Space for Robustness in an Intelligent Tutoring System

Authors:
Hameedullah Kazi;Peter Haddawy;Siriwan Suebnukarn
Affiliations:
Computer Science & Information Management Program, Asian Institute of Technology, Thailand, {hameedullah.kazi, haddawy}@ait.ac.th;Computer Science & Information Management Program, Asian Institute of Technology, Thailand, {hameedullah.kazi, haddawy}@ait.ac.th;School of Dentistry, Thammasat University, Thailand, ssiriwan@tu.ac.th
Venue:
Proceedings of the 2007 conference on Supporting Learning Flow through Integrative Technologies
Year:
2007

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Abstract

Intelligent tutoring systems assist medical faculty in training and equipping students with the required clinical reasoning skills. Plausible student solutions to a given problem are rejected by tutoring systems as being incorrect, if they do not match a specific solution accepted by the tutoring system. This leads to brittleness in evaluating student solutions. In this paper we describe a combination of knowledge base expansion and exploitation of existing knowledge structure to enhance robustness in an intelligent tutoring system for medical problem-based learning using UMLS. We present a tutoring system that enriches the solution space by collating different plausible solutions and exploiting the knowledge structure in UMLS to offer students a broader scope of reasoning.