2. Computer-Based Tutoring of Medical Procedural Knowledge Oleg Larichev & Yevgeny Naryzhny Copyright © 1999, All Rights Reserved Institute for Systems Analysis of Russian Academy of Sciences Moscow, Russia Presented on AI-ED99: 9 th International Conference on Artificial Intelligence in Education, 1999, Le Mans, France Larichev O., Naryzhny Y., Computer-based Tutoring of Medical Procedural Knowledge. In: Lajoie S., Vivet M. (Eds.) Artificial Intelligence In Education. Open Learning Environments: New Computational Technologies to Support Learning, Exploration and Collaboration. 1999, IOS Press, pp. 517-523.

3. Introduction Declarativ e Procedural (skills) Domain knowledg e

4. Changes in Thinking Expert Novice 10 years Little knowledge Little knowledge Poor structure Poor structure Backward reasoning Backward reasoning Many errors Many errors Rich knowledge Rich knowledge Good structure Good structure Forward reasoning Forward reasoning Little errors Little errors Unability to verbalize Unability to verbalize

5. Diagnostics as Classification Operation in anamnesis Pain in thorax Suddenly occured dyspnea Low arterial pressure... Operation in anamnesis Pain in thorax Suddenly occured dyspnea Normal arterial pressure... class ~C n class C n A set of attributes: P = {P 1, P 2, …, P M } Scales of possible values: P i = {p i 1, p i 2, …, p ik i } The problem space: A = P 1 x P 2 x …x P M a i  A a j  A

6. Problem Space Partial Order Class C n Attribute P i pi1pi1 pi1pi1 pi1pi1 pi2pi2 pi1pi1 pi3pi3 pi1pi1 pi4pi4 More typical values for C n Less typical values for C n The most typical object for class C n a i  C n  A A less typical object for class C n

7. Building the Classification if the expert classifies this case to class C n......all the more typical objects belong to class C n automatically ORCLASS Suite

8. Class Boundary class C n The boundary of class C n (p i k & … & p i k+l ) and at least t typical values of attributes {P r,...,P r+s } Discriminative attributes Additive attributes

9. Classification Complexity class C n class ~C n The most difficult objects for classification Less difficult objects for classification

10. Learning Principles n The n The decision rules are not shown n Implicit n Implicit learning via problem solving n Inductive n Inductive student model n Immediate n Immediate feedback n Explanations n Explanations as the expert’s hints n Gradually n Gradually increasing complexity n A n A big number of tasks

11. OSTELA Tutoring System Teaching the Art of Differential Diagnostics of the Pulmonary Artery Thromboembolism and the Acute Infarct of Myocardium

12. Basic Steps: Introduction

13. Background Knowledge Test The preliminary knowledge of typical signs and findings is a must

14. Pre-test of Diagnostical Skills A test case Possible diagnoses for selection Instructions for the learner

15. Main Learning Step A training case Current ECG Possible diagnoses for selection

16. Feedback on Errors Expert’s Hints The case The correct diagnosis

17. OSTELA Training Results Young physicians of the Botkin Clinical Hospital Day 1 (4hrs)Day 2 (4hrs) 40-60% correct diagnoses in the pre-test 90-100% correct diagnoses in the post-test Unability to verbalize decision rules About 500 cases solved