Intelligent Case Selection and Presentation

Designers of AI educational systems have largely ignored the problem of choosing what information to present and how to present it because systems to date have not been faced with a large space of possible presentations. This paper explains how a student model can provide the knowledge needed to make presentation decisions. Systems that use a student model for this purpose can make problems more interesting , challenging, and hand-tailored to students. We describe DECIDER: a program that improves stu-dent's decision-making abilities by constructing an environment where they can confront problems and decide on a plan of action. DECIDER chooses from a database of decision-making problems and varies how it presents those problems based on a model of the student's knowledge of goals, plans, and priorities. A good teacher can take the same material and present it in entirely different ways to different audiences. The better the teacher's model of the audience, the more appropriate and instructive their presentation will be. Because most AI educational systems do no reasoning about how to present information, they have not had the need for a student model for this purpose. We outline a set of techniques that can select cases and make presentation choices based on a student model. The resulting system focuses the student on new problems while providing an environment where they can attempt those problems, see the outcome, and explain their failures [SCHA86]. We have chosen decision-making as our task area, but the system design and pedagogical techniques described in this paper are applicable to any tutorial system that must select and present multi-faceted cases from a large library. Our system, DECIDER, teaches decision-making by presenting background information, a problem, and several options. The student decides on a plan to remedy the problem and observes the outcome. We have chosen U.S. national-security decision-making as our application domain , but the knowledge base and tutorial component are separate, so any decision-making domain could be equally substituted. Domain With any AI educational application modelling a real-world domain, the choice of what to teach must be constrained by both an abstract domain model and pedagog-ical goals defined within that model. A. The Decision-Making Model Decision-making is a complex process involving the interaction between advisors and decision-makers, opponents and proponents, planners and executers [NEUS86]. We have made a number of approximations to actual time-pressured, multi-agent decision-making, aimed at discretiz-ing the actual continuous process of …