Intelligent feedback polarity and timing selection in the Shufti Intelligent Tutoring System

Abstract

It is well known that the training of medical students is a long and arduous process. Students master many areas of knowledge in a relatively short amount of time in order to become experts in their chosen field. The Socratic Method used in the latter stages of medical education, where a physician directly monitors a group of students, is inherently restrictive due to the limited number of cases and length of the students’ rotations. Innovative Intelligent Tutoring techniques offer a solution to this problem. This paper outlines the overall structure and design of Shufti, an Intelligent Tutoring System (ITS) focused on mammography and medical imaging. Shufti's aim is to provide medical students with an improved learning environment, exposing them to a broad range of examples supported by customized feedback and hints driven by an adaptive Reinforcement Learning system and Clustering Techniques.