Demonstration-based Solution Authoring for Skill Assessment

Abstract

The high cost of developing content has been a major impediment to the widespread deployment of intelligent training systems. To enable automated skill assessment, traditional approaches have required significant time investment by highly trained individuals to encode first-principles domain models for the training task. In contrast, approaches grounded in example-based methods have been shown to significantly reduce authoring time. This paper reports on an approach to creating solution models for automated skill assessment using an example-based methodology, specifically targeting domains for which solution models must support robustness to learner mistakes. With this approach, a content author creates a baseline solution model by demonstrating a solution instance and then specifies a set of annotations to generalize from that instance to a comprehensive solution model. Results from a user study show that domain experts are comfortable with the approach and capable of applying it to generate quality solution models. Keywords: Artificial Intelligence, Artificial Intelligence Center, AIC, intelligent tutoring, assessment, model authoring