A framework for the design of a medical tutoring system for the instruction of undergraduates in general practice.

One of the difficulties in teaching clinical medicine is the lack of opportunity a student has to acquire techniques for solving clinical problems. By using a computer to simulate a General Practice environment where patients with sets of symptoms are presented, a student can gain experience of diagnostic techniques and treatment management for any medical condition. Such an approach should enhance a student's development of properly structured clinical algorithms for interrogating a patient and arriving at an appropriate management plan. The intelligent tutoring system developed at the Department of Computer Science with the collaboration of the Department of General Practice aims not only to simulate this environment but also to formulate the basis for a general interactive learning environment for all subject domains with similar problem-solving model. In this system, a student may question, examine and provide treatment plans for a patient whilst constantly being monitored by the system:. Using Artificial Intelligence techniques, the tutor is able to assess the progress of a student throughout the tutorial session and produce tutoring interventions at appropriate stages, according to the student's ability. The system's knowledge base consists of disease profiles and population parameters which are created and updated by a separate system - the Medical Editor. The manipulation of this database allows tailoring of the system to simulate any clinical situation in Primary Care. This research considers in detail the current teaching/tutoring strategies adopted by all medical computer-assisted learning systems. It identifies the main areas of difficulty for using such systems in the Primary Care undergraduate course and discusses the consultation model used in this system with full comparison of the models used in Secondary Care. The research also discusses the main design issues which forms the framework for building learning environments based on intelligent tutoring systems