Accession Number : ADA345718


Title :   Generation of Tutorial Dialogues: Discourse Strategies for Active Learning


Descriptive Note : Final rept. 1 Jun 95-31 May 98


Corporate Author : ILLINOIS INST OF TECH CHICAGO


Personal Author(s) : Evans, Martha


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a345718.pdf


Report Date : 29 MAY 1998


Pagination or Media Count : 13


Abstract : With the support of the Cognitive Science Program of ONR, we are developing the capability to generate complex natural language tutorial dialogues for an intelligent tutoring system designed to help medical students understand the functioning of the negative feedback system that regulates blood pressure in the human body. We are convinced that a real natural language interface is vital to a tutoring system trying to help students learn complex concepts like negative feedback. The text generator for a tutoring system must be able to ask questions and provide hints in addition to generating definitions, descriptions, and explanations of the functioning of the physiological system and the underlying anatomy. The language understanding component must be ready to accept student responses and student initiatives, which are full of ellipses, novel abbreviations, wild spellings, and wilder grammar. Our work is embodied in a system called Circsim Tutor. Briefly described, Circsim Tutor presents the student with a set of clinical problems each of which results in a perturbation of blood pressure, and asks the student to explain step by step how the blood pressure is perturbed and how the perturbation is physiologically compensated for. The system conducts a tutorial dialogue in English, as the student does this. with the session organized around the students's errors in making these predictions.


Descriptors :   *COMPUTER AIDED INSTRUCTION , *MEDICAL COMPUTER APPLICATIONS , *TRANSFER OF TRAINING , COMPUTERIZED SIMULATION , ARTIFICIAL INTELLIGENCE , MAN COMPUTER INTERFACE , NATURAL LANGUAGE , INDIVIDUALIZED TRAINING , BLOOD PRESSURE , TEACHING MACHINES , HABITUATION LEARNING.


Subject Categories : MEDICINE AND MEDICAL RESEARCH
      COMPUTER SYSTEMS


Distribution Statement : APPROVED FOR PUBLIC RELEASE