JAIED v7n2

The three papers comprising this special issue of the Journal of Artificial Intelligence in Education are based, on edited transcripts of invited talks given at the AI-ED 1995 Conference in Washington, DC. It seemed to us that the invited presentations at conferences are often the most influential in affecting the directions of a research field, and yet they often do not exist in a referential form. For example, Kurt Van Lehn's talk at AI-ED 1993 is often quoted via the short paper in the proceedings, which in fact, differed substantially from the talk.

For these papers, we asked the speakers-cum-authors to resist the temptation to embark on a wholesale revision of the transcripts. We wished to retain something of the style, the informality, and the spontaneity of a talk, as opposed to a finely honed paper. We have deliberately left in various comments and asides which will remind readers that it is indeed a transcript, not a normal paper, they peruse. We ask readers to read the paper in this spirit (and not to quote any slight infelicity of expression).

I would like to thank the three authors-Tak-Wai Chan, Pierre Dillenbourg, and Johanna Moore-for allowing their talks to be published in this way. It is sometimes not easy to face up to a transcript of what one has really said especially, perhaps, for those such as Tak-Wai and Pierre for whom English is a second language. Of course, these written versions lose something of the vitality of the presentations, especially without the visual demonstrations. Nonetheless, we are sure this issue provides a record of events which have been and will be influential in the AI-ED field.

The Editor

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Learning Companion Systems, Social Learning Systems, and the Global Social Learning Club

Tak-Wai Chan

Department of Computer Science and Information

Engineering

National Central University, Chung-Li

Taiwan, 32054 R.O.C

This talk reviews our work in the past and presents a vision of future research based on our current approach. I take a rather personal perspective in describing how the idea of the learning companions emerged. Then I discuss how, after the development of a series of experimental systems over some years, the idea of the learning companions was extended to a class of systems which we call social learning systems. Failures or successes of some of these systems are discussed. After that, I describe the recent readjustment of our approach and take a leap to aim at a global social learning club. It is becoming clear that such a virtual learning community will be typical or prevalent in the next century.

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Some Technical Implications of Distributed Cognition on the

Design on Interactive Learning Environments

Pierre Dillenbourg

TECFA, Faculte de Psychologie et des Sciences de l'Education

University of Geneva, 1227

Carouge, Switzerland

Edited transcript of "Diagnosis is mutual: A distributed cognition approach," an invited talk at the World Conference on Artificial Intelligence in Education, Washington, DC, August, 1995.

This talk prompted two arguments. First, I contend that the three main functions in an interactive learning environment are intrinsically collaborative. The functions are: (a) Learning modeling is a collaborative process. This is not a new idea, but rather the natural evolution of research on learning modeling; (b) explanation and even (c) tutoring are described also as collaborative processes. My second claim is that current knowledge-based techniques are not appropriate to develop collaborative agents. I suggest that collaborative engines should merge dialogue operators with reasoning operators. For instance, dialogue operators could be used during the instanciation of rules in order to enable agents to negotiate the scope of variables.

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Making Computer Tutors More Like Humans

Johanna D. Moore

Learning Research and Development Center

University of Pittsburgh

Pittsburgh, PA, 15260, USA

If computer-based instructional systems are to reap the benefits of natural language interaction, they must be endowed with the properties that make human natural language interaction so effective. In this paper, I describe several features that distinguish human tutorial explanations from those produced by today's computer-based tutors. I argue that in order to build systems capable of structuring explanations naturally and appropriately interpreting and generating context-sensitive utterances, it is necessary to model and reason about the plans the speakers in a discourse are attempting to carry out. I then present a plan-based approach to natural language generation, and provide examples from several implemented systems that illustrate how the approach enables systems to participate effectively in natural language dialogues with their users.

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