Lets look at some developments in the field of artifical intelligence as they apply to learning systems. Massimo Negrotti has stated that there is an urgency in defining theories in this field.

ÒThe need for a theory comes both from scientific and from practical interests. According to the former we have to understand the artificial in order to discriminate it from the purely technological activity and try to understand also their different anthropological roots and intellectual motivations. According to the latter, we have to understand the different requirements needed for the use of conventional machines as compared to the 'use' of artificial devices. The intensity of today's technology, both artificial and conventional, makes such theoretical work legitimate both on technical grounds and also because of its urgency socially and culturally.Ó (Negrotti, 1993).

If we are to design ÔtrueÕ units of artificial intelligence we must be certain to clearly understand what this means. If you access http://wombat.doc.ic.ac.uk/foldoc/index.html and type in Ôartificial intelligenceÕ you will be presented with this definition:

(AI) The subfield of computer science concerned with the concepts and methods of symbolic inference by computer and symbolic knowledge representation for use in making inferences. AI can be seen as an attempt to model aspects of human thought on computers. It is also sometimes defined as trying to solve by computer any problem that a human can solve faster. Examples of AI problems are computer vision (building a system that can understand images as well as a human) and natural language processing (building a system that can understand and speak a human language as well as a human). These may appear to be modular, but all attempts so far (1993) to solve them have foundered on the amount of context information and "intelligence" they seem to require.

This field is growing quickly and their are a number of projects we should be aware of. The ACT Tutors being developed at Carnegie Melon University (http://sands.psy.cmu.edu/ACT/tutor/tutoring.html) by the The Advanced Computer Tutoring Project group are mathematics and programming tutors able to accomplish the following: Based on a computational theory of thought, our tutors can generate and follow the multiple possible solutions a student might attempt on any given problem and dynamically tailor instruction to each individual student and problem. Like a personal human tutor or coach, cognitive tutors observe student performance, identify strengths and weaknesses, and provide individualized, just-in-time instruction while students learn by doing.

The group of researchers working on this project (http://sands.psy.cmu.edu/ACT/tutor/tutor-people.html) are making dramatic claims about its effectiveness but ongoing research seems to be proving the validity of their models. In fact these could easily be seen as early MC models. If you wish you can gain access to demonstrations of these programs by linking to the following URL - http://sands.psy.cmu.edu/ACT/tutor/tutoring.html.