A central process in any learning experience is the incorporation of a new fact into an existing theory. Often the goal of that process is more specific, to learn some new fact about some concept. But what does it mean to claim that a sentence is new, and even more interesting, what qualifies as a novel fact about some concept? Despite the vast interest in learning and the abundance of related papers (cf.

This is a remarkab'y exciting time to be involved professionally in the field of medical informatics. The underlying scientific principles are beginning to be identified and defined, educators are increasingly acknowledging the importance of thc field for physicians of the present and future, and the tec mology itself is growing at rates that make the future of the field both unbounded and impossible to predict. One has the sense that what was once a field for pioneers is now reaching the stage of established settlements, with a history, traditions, and a feel of permanence. It is therefore appropriate that, at the beginning of ddiberations designed to achieve significant educational goals for the field, we might start by considering the discipline itself and the characteristics that hnve tended to separate it from other traditional academic and research medical specialties. I would like to begin by assuming that certain basic points are well accepted and need not be defended here: first that medical informatics holds both realized and potential importance for the science anc practice of medicine, and second, that there is a need for all medical practitioners to be familiar both with information handling technology and with the underlying principles that make the field relevant, regardless of whether computers are involved.

It has been argued by Hartley and Sleeman.

This paper reports the results obtained with a group of 24 14-year-old pupils when presented with sets of algebra tasks by the Leeds Modelling System.

To appear inProc. of the American Association for Medical Systems & Informatics, 1983 Reprinted by permission of the American Association for Medical Systems and Informatics (AAMSI). Hardware advances in the next decade promise to make poss:*ale new medical educational technologies. New media for expressing, collecting, and sharing knowledge will provide students with means for coping with the increasing amounts of information. Novel means of graphically modelling physical phenomena--providing motivating and intuitively pleasing means for explorative interaction--could complement and sometimes replace traditional text material. Intelligent programs may serve as assistants, serving roles ranging from calculator to librarian to tutor, embracing a full range of secretarial and problen solving aids.

I cis is the final report for Contract N-00014-79-C-03C2, covering the period of 15 March 1979 through 14 March 1982. The goal of the project was to develop methods for representing teaching and problem-solving knowledge in computer-based tutorial systems. One focus of the work was formulation of principles for managing a case method tutorial dialogue; the other major focus was investigation of the use of a production rule representation for the subject material of tutorial program. The main theme pursued by this research is that representing teaching and problemsolving knowledge separately and explicitly enhances the ability to build, modify and test complex tutorial programs. Two major corr Jter programs were constructed.

No. STAN-CS-82-909 Also numbered: HPP42-7 Department of Computer Science Stanford University Stanford, CA 94305 Abstract This paper describes the student modeler of the GUIDON2 tutor, which understands plan: by a dual search strategy. It first produces multiple predictions of student behavior by a model-driven simulation of the expert. Focused, data-driven searches then explain incongruities. By supplementing each other, these methods lead to an efficient and robust plan understander for a complex domain. Diagnostic problem-solving requires domain knowledge and a plan for applying that knowledge to the problem.

The Key idea is to represent explicitly and separately: a A. The limitations of MYCIN for application to teaching domain-independent diagnostic strategy in the form of meta-rules,

This paper briefly outlines the difference between Branching on the bas of response was the first step coward traditional instructional programs and MAI.

There are several different types of goals, and each has a corresponding set of parameters. For example, the goal (obtain (coefficient 6 x 2)) means to obtain an expression for the coefficient of x2 in g6, either to print it out or pass as argument to some MACSYMA command. Note that this can be done either by finding an already computed expression (stored, say, as the value of some variable) or by computing it anew. Either implementation is satisfactory so long as it computes the desired expression.