GIFT

An emphasis on self-regulated learning in the military community (U.S. Army Training & Doctrine Command, 2011) has highlighted a need for point-of-need training in environments where human tutors are either unavailable or impractical. Computer-Based Tutoring Systems (CBTS) have been shown to be as effective as expert human tutors (VanLehn, 2011) in one-to-one tutoring in well-defined domains (e.g., mathematics or physics) and significantly better than traditional classroom training environments. CBTS have demonstrated significant promise, but fifty years of research have been unsuccessful in making CBTS ubiquitous in military training or the tool of choice in our educational system. Why?
The availability and use of CBTS have been constrained by their high development costs, their limited reuse, a lack of standards, and their inadequate adaptability to the needs of learners (Picard, 2006). Their application to military domains is further hampered by the complex and often ill-defined environments in which our military operates today. CBTS are often built as domain-specific, unique, one-of-a-kind, largely domain-dependent solutions focused on a single pedagogical strategy (e.g., model tracing or constraint-based approaches) when complex learning domains may require novel or hybrid approaches. The authors posit that a modular CBTS framework and standards could enhance reuse, support authoring and optimization of CBTS strategies for learning, and lower the cost and skillset needed for users to adopt CBTS solutions for military training and education. This paper considers the design and development of a modular CBTS framework called the Generalized Intelligent Framework for Tutoring (GIFT).