When working with review materials in a self-test mode, student involvement is maximized when the problems presented variably fall within, and occasionally slightly beyond, a student's current level of ability or training. Because of the many difficulties associated with developing generic, fully adaptive systems with rule-based expert system technology, we have focused on using neural network technology as a practical, domain-independent means of optimizing the presentation of multimedia educational programs. The pattern classification capabilities of a neural network-based tutorial supervisor, developed as a series of external commands, have been used to successfully mediate the presentation of image-intensive courseware in cardiac pathophysiology. Research issues include identifying how to extend this approach to dynamically-generated courseware content, e.g., graphic simulations, and determining the educational effectiveness of various control algorithms used to assign students to problem sets of different levels of difficulty.