Challenges in Moving Adaptive Training & Education from State-of-Art to State-of-Practice

Adaptive training and education (ATE) systems are the convergence of intelligent tutoring system (ITS) technologies and external training and education capabilities (e.g., serious games, virtual humans and simulations). Like ITSs, ATEs provide instructional experiences that are tailored to the learner and may be more effective than the training or educational systems alone. ATEs also leverage existing environments, content and domain knowledge to reduce the authoring workload. The Generalized Intelligent Framework for Tutoring (GIFT) is an open-source ATE architecture with the primary goal to support easy authoring, automated instructional management during ATE experiences, and a testbed to evaluate the effect of ATE tools and methods. While this paper addresses challenges and goals in bringing ATE solutions from state-of-art to state-of-practice within GIFT, it also highlights generalized challenges in making ITS technologies ubiquitous and practical on a large scale across a broader variety of domains.