Joint Vision 2010 and Joint Vision 2020 suggest that future military success will largely be a function of the military's capability to respond to dynamic changes in the environment. The capability is not likely to happen by chance; it must be trained. One approach is to provide practice opportunities in large-scale simulations where the training audience is presented with situations that require adaptation based on changing environmental conditions. While this type of training places challenging demands on the training audience, it also places demands on the training system. That is, the training system itself will have to be adaptive in response to the training audience.

Advancements in modeling and simulation (M&S) have provided an ever increasing capability to conduct distributed, large-scale exercises in synthetic environments, however, there has not been a commensurate level of effort to develop technologies to support realtime modification of these exercises when changes are required. Currently, exercise modification is largely unaided and resource intensive. Exercise controllers must manually determine when changes are required during a scenario, assess the impact of those changes on the exercise, and then implement those changes. Without the capability to rapidly modify the scenario in response to these situations, important training opportunities may be missed, critical training objectives may not be achieved, and valuable training resources may be wasted. As pressures to reduce training resource requirements and maintain training effectiveness continue, the need for methods and technologies to support learning will become even more acute.

Fortunately, recent developments in learning methods and technologies have considerable potential for enhancing the effectiveness and efficiency of M&S exercises. This paper will describe an effort that is exploiting advances in emerging training methods and technology to support realtime modification of M&S exercises based on trainee performance. The project goals are to demonstrate: (1) enhanced achievement of training objectives, (2) improved effectiveness of training by tailoring to the training audience's needs, and (3) reduced human resources to support exercise execution. This paper will describe: (1) the operational need, (2) current training requirements and issues, (3) promising component methods and technologies, and (4) the advanced development research effort.