  The paper presents a novel model-based method for intelligent tutoring, with
particular emphasis on the problem of selecting teaching interventions in
interaction with humans. Whereas previous work has focused on either
personalization of teaching or optimization of teaching intervention sequences,
the proposed individualized model-based planning approach represents
convergence of these two lines of research. Model-based planning picks the best
interventions via interactive learning of a user memory model's parameters. The
approach is novel in its use of a cognitive model that can account for several
key individual- and material-specific characteristics related to
recall/forgetting, along with a planning technique that considers users'
practice schedules. Taking a rule-based approach as a baseline, the authors
evaluated the method's benefits in a controlled study of artificial teaching in
second-language vocabulary learning (N=53).
