Adaptive instructional systems (AIS) will play a central role in the next generation of training systems for the military. A key feature of AISs is the capacity to automatically tailor instruction to fit the needs and skills of individual learners. Leveraging recent advances in artificial intelligence and machine learning, it is possible to tailor training and educational experiences based on the goals, learning needs, and preferences of individual learners and teams of learners. Tutorial planning, a critical component of adaptive training, controls how scaffolding and instructional interventions are structured and delivered to learners to create personalized learning experiences. Devising computational models that effectively scaffold learning experiences is a critical challenge for the field. For example, AISs need to determine when to scaffold, what type of scaffolding to deliver, and how scaffolding should be realized, all in real time. In this paper, we describe our work using the Generalized Intelligent Framework for Tutoring (GIFT), an open source framework for creating, deploying, and evaluating adaptive training systems, to create a web-based adaptive short course for teaching fundamental principles associated with counterinsurgency (COIN). The course presents students with a series of videos, integrated assessments, and remediation materials about doctrinal COIN concepts. The course’s adaptive remediation features are based on the ICAP active learning framework to deliver constructive, active, and passive forms of remedial feedback to students. We report the results of a recent study in which 500 participants completed the adaptive training course along with pre- and post- training knowledge tests. The paper provides an analysis of learning gains and factors that moderated these gains and concludes with a discussion of future research as it pertains to the goals of the broader research program investigating applications of machine learning, and reinforcement learning in particular, to automatically generate policies for instructional remediation in AISs.