Curriculum tree: A knowledge-based architecture for intelligent tutoring systems

AbstractIntelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points. In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.

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Modeling Human-Like Robustness in an Intelligent Tutoring System

ECTI Transactions on Computer and Information Technology (ECTI-CIT) ◽

10.37936/ecti-cit.200731.54206 ◽

1970 ◽

Vol 3 (1) ◽

pp. 2-8

Author(s):

Hameedullah Kazi ◽

Peter Haddawy ◽

Siriwan Suebnukarn

Keyword(s):

Intelligent Tutoring Systems ◽

Intelligent Tutoring ◽

Intelligent Tutoring System ◽

Medical Problem ◽

Knowledge Based Systems ◽

Tutoring System ◽

Tutoring Systems ◽

Specific Solution ◽

Knowledge Based ◽

Narrow Scope

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few human-like attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.

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