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 accomplished reference textual content during this dynamic region, surveys learn because the early Seventies and assesses the state-of-the-art. Adopting the point of view of the conversation of information, the writer addresses functional matters thinking about designing tutorial structures in addition to theoretical questions raised through investigating computational tools of data conversation.

Weaving jointly the objectives, contributions, and interesting demanding situations of clever tutoring method improvement, this well timed booklet comes in handy as a textual content in classes on clever tutoring structures or computer-aided guide, an creation for rookies to the sector, or as a reference for researchers and practitioners.

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

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This allows restricted versions of natural-language processors to be used. In this regard, Burton and Brown (1979a) note that the interface must not only be robust and efficient so as to become unobtrusive, but also be able to present a clear picture of the system's capabilities. Unless the student precisely perceives the extent to which the system can respond to his input, his interaction will be hampered either by too difficult queries or by suboptimal use of the facilities. Although this problem is common to all types of interactive software, systems that seem to manifest some intelligence tend to draw unrealistic expectations from naive users.

More recently, "ICAI" has often been replaced by the acronym "ITS," for "Intelligent Tutoring Systems" (Sleeman and Brown, 1982). In this book, I prefer to use this latter acronym to distinguish instructional systems involving artificial intelligence from more traditional approaches, which will simply be referred to as CAI. This preference is motivated by a claim that, in many ways, the significance of the shift in research methodology goes beyond the addition of an "I" to CAI. 1 Knowledge communication systems Implicit versus explicit encoding of knowledge The purpose of this chapter is to give the reader a sense of the field's identity by outlining its goals, its methodology, and its relation to other fields.

It is in sharp contrast with the behaviorist views characteristic of early notions of programmed teaching (Skinner, 1968). In fact, we will see in Part II that the lessons learned in the field so far keep suggesting a need to place epistemological, cognitive, and pedagogical issues before the mass production of instructional artifacts or the creation of authoring 8 Knowledge communication systems languages. Note that this does not mean that the design of systems has to be postponed; in fact, design is an intrinsic part of the field's methodology, and has been a critical catalyst in the articulation of processes that has led to the field's main contributions.

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