Current research has succeeded in despite much work attempting to do so, human-- exploring a large number of domains and has explored machine communication is not yet sensitive to dialogue some nontraditional pedagogical strategies, such as partnering, context and to what is known or knowable about the student's mentoring, and scaffolding.

Our descriptive vocabulary may still In this article I propose to describe an area of artificial contain proper names as modifiers but the explanatory intelligence (Al) research that I and several colleagues vocabulary now involves the impersonal qualities of an have been enaged in for a number of years.

Paper presented at the IPN Symposium 7: Formalized Theories of Thinking and Learning and Their Implications for Science Instruction.

This chapter briefly outlines the difference between traditional instructional programs and ICAI. It then illustrates how GUIDON makes contributions in areas important to medical CAl: interacting with the student in a mixed-initiative dialogue (including the problems of feedback and realism), teaching problem-solving strategies, and assembling a computerbased curriculum. In evaluating GUIDON's performance, one can see the value in the basic idea of formalizing teaching knowledge in procedures that are separate from the knowledge to be taught. However, the program is inherently limited by the MYCIN knowledge base. The rule set is poorly structured, does not contain pathophysiological knowledge for justifying the diagnostic associations, and does not explicitly state the strategies for gathering information and focusing on hypotheses.

Steps toward a theoretic foundation for complex knowledge-based CAI.

Here we consider the logical bases for rules: what kinds of arguments justify the rules, and what is their relation to a mechanistic model of the domain? We use the terms "explain" and "justify" synonymously, although the sense of "making clear what is not understood" (explain) is intended more than "vindicating, showing to be right or lawful" (justify).

The idea of directly teaching students "how to think" goes back at least to Polya (1957), if not to Socrates, but it reached a new stage of development in Papert's laboratory (Papert, 1970). In the LOGO lab, young students were taught AI concepts such as hierarchical decomposition, opening up a new dimension by which they could take apart a problem and reason about its solution. In part, Polya's heuristics have seemed vague and too general, too hard to follow in real problems (Newell, 1983). But progress in AI programming, particularly expert system design, has suggested a vocabulary of structural concepts that we now see must be conveyed along with the heuristics to make them intelligible (see Chapter 29). Developing in parallel with Papert's educational experiments and capitalizing even more directly on AI technology, programs called intelligent tutoring systems (ITS) were constructed in the 1970s.

The parser le following paragraphs describe both.

Automatic computer programming or automatic programming occurs whenever a machine aids in this process. The amount of automatic programming that is occurring is a variable quantity that depends on how much aid the human is given. There are a number of dimensions on which the level of help can be measured including the level of the language used by the human, the amount of informality allowed, the degree to which the system is told what to do rather than how to do it, and the efficiency of the resulting code. Thus we usually say that there is a higher degree of automatic programming whenever a higher level language is used, less precision is required of the human, the input instructions are more declarative and less procedural, and the quality of the object code is better. The technologies of automatic programming thus include the fields that help move the programming experience along any of these dimensions: algorithm synthesis, programming language research, compiler theory, human factors, and others. This paper will concentrate on only the first of these topics, formal methodologies for the automatic construction of algorithms from fragmentary information. The formal methodologiest have been separated into two categories, synthesis from formal specifications and synthesis from examples. In the former case, it is assumed a specification is given for the target program with adequate domain information so that the target program can be derived in a series of logical steps.

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.