Intelligent Tutoring Gets Physical: Coaching the Physical Learner by Modeling the Physical World

Extending the application of intelligent tutoring beyond the desktop and into the physical world is a sought after capability. If implemented correctly, Artificial Intelligence (AI) tools and methods can be applied to support personalized and adaptive on-the-job training experiences as well as assist in the development of knowledge, skills and abilities (KSAs) across athletics and psychomotor domain spaces. While intelligent tutoring in a physical world is not a traditional application of such technologies, it still operates in much the same fashion as all Intelligent Tutoring Systems (ITS) in existence. It takes raw system interaction data and applies modeling techniques to infer performance and competency while a learner executes tasks within a scenario or defined problem set. While a traditional ITS observes learner interaction and performance to infer cognitive understanding of a concept and procedure, a physical ITS will observe interaction and performance to infer additional components of behavioral understanding and technique. A question the authors address in this paper is how physical interactions can be captured in an ITS friendly format and what technologies currently exist to monitor learner physiological signals and free-form behaviors? Answering the question involves a breakdown of the current state-of-the-art across technologies spanning wearable sensors, computer vision, and motion tracking that can be applied to model physical world components. The breakdown will include the pros and cons of each technology, an example of a domain model the data provided can inform, and the implications the derived models have on pedagogical decisions for coaching and reflection.