Improving Artificial Teachers by Considering How People Learn and Forget

TL;DR

This paper introduces a model-based intelligent tutoring approach that personalizes teaching interventions by incorporating cognitive models of learning and forgetting, optimizing practice schedules for language learning.

Contribution

It combines personalization and intervention optimization through a cognitive model and planning, advancing intelligent tutoring systems.

Findings

Improved vocabulary retention in controlled study

Effective personalization of teaching interventions

Demonstrated benefits over rule-based methods

Abstract

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).