Computer-Based Tutoring Systems (CBTS) are effective learning tools with a high degree of customizability. However, their application in the training community is limited due to high development costs, limited reuse, and a lack of standards (Sottilare, et al. 2012). To remedy this issue, the U.S. Army Research Laboratory is developing an open-source modular program called the Generalized Intelligent Framework for Tutoring (GIFT). GIFT provides a set of tools to author, deliver, and evaluate intelligent tutoring applications. An essential component of GIFT is a domain-independent pedagogical module that manages instruction based on a learner's unique information. The purpose of this pedagogical module is to tailor and induce intervention via empirically-based generic instructional strategies. The goal of this research is to create an algorithm in the form of a decision tree within the pedagogical module, which will inform adaptation based on generalized characteristics associated with the learner and domain being trained.

The authors previously presented a list of learner characteristics (e.g., learner motivation, working memory capacity, prior knowledge, etc.) that form the basis of this pedagogical model development (Goldberg et al., 2012). For each identified variable, validated psychometric instruments were selected and threshold levels established (i.e., score designates high/low groupings). Based on this information, the authors developed an extensive database of empirically validated instructional strategies. Each strategy was mapped to the four categories of Merrill's (1994) Component Display Theory (CDT): Expository generality (general rules), Expository instance (specific examples), Inquisitory generality (recall knowledge), and Inquisitory instance (apply knowledge). This development resulted in a pedagogical model that provides recommended generalized strategies for incorporation in the CBTS authoring process. The authors will present work associated with the model development, highlighting a detailed use-case of its implementation within a specific training instance. In addition, the authors will also present the results from initial model validation.