Building an Intelligent Tutoring System: Some Guidelines from a Study of Human Tutors

One reason that intelligent tutoring systems (ITSs) are rarely found outside of the research lab has to do with the guidelines available to developers of these systems. First, some of these guidelines are stated as general, abstract goals such as “adapt to the student.” What ITS developers need, however, are specific strategies and techniques which can be implemented in an ITS to accomplish those goals. Second, not all of the guidelines have an empirical basis. One solution to both of these problems is to study human tutors. This paper demonstrates this approach, and discusses an empirical study of human tutors which was conducted to address these issues. Specifically, it discusses 1) the knowledge acquisition method which we designed to capture the appropriate empirical data, 2) how we used this method to study human tutors and students in the medical problem-solving domain of immunohematology (blood banking), 3) several guidelines which appeared to drive the tutors' behavior (e.g., “limit the number of interrupts to the student”), and 4) specific tutoring strategies which can be incorporated into an ITS to make its behavior follow these guidelines.