Despite the prevalence of software applications that exploit user information to individualize the experience, personalized training systems are still relatively rare. This paper describes changes in technology and standards that may alter this trend. Utilizing these advances, we have developed a standards-based learner model that is updated dynamically during training and that controls content sequencing. We have established the impact of this technology on learning through training effectiveness research. With this core learner modeling capability established, we have subsequently started exploratory studies into ways it might be used to manage scenario-based, simulation training. Specifically, we describe two prototype systems that use this core modeling capability, but that use the information it provides in distinctly different ways. Because of the complexity of simulation training, the root cause of performance issues is seldom apparent. The first prototype addresses this issue by using the learner model to select follow-on scenarios that help to build skill while distinguishing among competing learning needs hypotheses. The second prototype addresses the issue of maximizing learning opportunities within a scenario. It uses the core learner model to modify a scenario during execution in order to provide additional opportunities to achieve specific learning objectives or to adjust the challenge of an exercise. Directions for future research for both efforts are described.