Simulation-based systems are increasingly being used for training "soft" skills such as providing cultural understanding, conducting interrogations and interviewing, and assessing adaptive thinking and leadership. Simulation-based training systems can be conceived as having three major components. First, an environment model drives actions and responses of simulated entities (objects, machines, terrain, avatars) in the virtual environment. Second, a student model maintains the system's understanding of the state of the student's knowledge and skills. Third, an instructional model selects and sequences the learning experiences of the student and provides feedback to the student based on inputs from the environment model and the student model. The latter two components partly define intelligent tutoring to guide simulation flow to promote learning.

This paper describes lessons learned in evolving simulation-based training systems for procedural skills into trainers for soft skills, particularly changes required in the student and instructional models. These simulations are being developed for intelligence analysis training. The remediation methods of the instructional model developed for procedural training were revised for soft skills since the soft skill performance criteria are less well defined in terms of student actions and simulation events. This revision required a more robust student model that can infer student bias and other imperfect conceptual models. The sequencing of instructional events was modified to take advantage of parameterized initial values and introduce a "sting" meant to entice students to make decisions consistent with imperfect conceptual models. The selection of enticements requires more interactions between the student model and the instructional model than was present in the procedural training simulations. Scenario-based training supports practice and assessment on multiple learning objectives at the same time. The configuration and sequencing of instructional events provides variable reinforcement of multiple learning objectives.