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ERIC Number: ED242311
Record Type: RIE
Publication Date: 1983-Nov
Pages: 17
Abstractor: N/A
Reference Count: 0
GUIDON. Technical Report #9.
Clancey, William J.
GUIDON is an intelligent computer-aided instruction (ICAI) program for teaching diagnosis, which has been tested using the infectious disease diagnosis rules of the MYCIN consultation system developed at the Stanford University School of Medicine. GUIDON engages a student in a dialogue about a patient suspected of having an infection and thus teaches the student about relevant clinical and laboratory data and diagnosis of the causative organism. Without reprogramming, the program can discuss any diagnostic problem that it can solve on its own. Moreover, by substituting problem solving knowledge from other domains, the program can immediately discuss problems in those domains. This power derives from the use of artificial intelligence methods for representing independently both subject material and general knowledge about how to teach. There are teaching rules and procedures for: determining what the student knows, responding to his/her partial solution, providing hints, and opportunistically interrupting to test his/her understanding. Experience with GUIDON reveals the importance of differentiating between causal and strategic knowledge in order to explain diagnostic rules and to teach a reasoning approach. These lessons are now guiding the development of new representations for teaching. A 13-item bibliography and a list of names and addresses of government and private sector research/information centers and personnel concerned with computer-aided instruction are provided. (Author/ESR)
Publication Type: Reports - Research
Education Level: N/A
Audience: Researchers
Language: English
Sponsor: Office of Naval Research, Arlington, VA. Personnel and Training Research Programs Office.
Authoring Institution: Stanford Univ., CA. Dept. of Computer Science.
Identifiers: Diagnostic Consultation Systems; GUIDON Program; Intelligent CAI Systems
Note: For related documents, see IR 011 066 and IR 011 079. Revised article reprinted with permission from the Handbook of Artificial Intelligence, Vol. II, A. Barr and E. A. Feigenbaum (Eds.). William Kaufman, Inc., 1982.