Intelligent Tutoring System for Medical Statistics

Intelligent Tutoring Systems (ITS) promises improved quality of education at reduced cost; theycould provide the benefits of one-on-one tutoring without the personnel cost. However, this potentialhas not yet been realized. We will create a new paradigm for intelligent tutoring systems, thataddresses a fundamental flaw in traditional approaches. The outcome of any teaching intervention isuncertain, and the state of the student's knowledge is uncertain. Our approach explicitly models thisuncertainty, providing a more accurate model of the student and enabling the tutoring system to learnfrom experience. We will apply this paradigm to medical statistics education. Medical practitioners needa good grasp of statistical concepts to read and critique medical literature, plan and carry out projectsinvolving data collection, understand and practice scientific method in research, and make decisions inthe face of uncertainty. However, there is widespread concern that statistics is not being taughteffectively to health care professionals. Biostatistics is difficult to teach in a traditional classroom settingand is widely disliked. An ITS, providing one-on-one tutoring, could transform biostatistics education.