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By Etienne Wenger

Contributor note: ahead by means of John Seely Brown & James Greeno
Publish yr note: First released in 1987

Artificial Intelligence and Tutoring Systems, the 1st complete reference textual content during this dynamic zone, surveys study because the early Nineteen Seventies and assesses the cutting-edge. Adopting the point of view of the verbal exchange of information, the writer addresses useful concerns concerned with designing tutorial platforms in addition to theoretical questions raised via investigating computational tools of information conversation.

Weaving jointly the objectives, contributions, and interesting demanding situations of clever tutoring process improvement, this well timed booklet turns out to be useful as a textual content in classes on clever tutoring platforms or computer-aided guideline, an advent for beginners to the sector, or as a reference for researchers and practitioners.

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Extra info for Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge

Sample text

Indeed, a theme that will surface again and again is that the goal of communicating knowledge with explicit representations in instructional systems has led researchers to undertake very fundamental investigations into the nature of knowledge and into the characteristics that make it communicable. These investigations have in turn given rise to important insights into issues of knowledge representation, reasoning, and learning central to artificial intelligence. In fact, some of these investigations have been among AI's most significant developments; they establish intelligent knowledge communication as a fundamental research direction in artifi­ cial intelligence, when it has usually been considered as an application.

29 30 Tutorial dialogues: from semantic nets to mental models and an investigation of principles for representing the knowledge to be conveyed. On the one hand, communication strategies must be expressed in terms of representational abstractions. On the other hand, the requirements of communication direct research efforts to the nature of domain knowledge and of reasoning mechanisms that can support communication processes. After these themes interact in the context of systems such as SCHOLAR and WHY, the investigation of mental representations even predominates.

Thus, along with the idea of explicitly repre­ senting the knowledge to be conveyed came the idea of doing the same with the student, in the form of a student model Ideally, this model should include all the aspects of the student's behavior and knowledge that have repercussions for his performance and learning. However, the task of constructing such a student model is obviously not a simple one for computer-based systems. In fact, it is even difficult for people, who have much more in common with their fellow interlocutors than do silicon machines.

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