An Operational Adaptive Training System (OATS) using operational intelligence sources can give First Responders, from bomb disposal teams to firemen and law enforcement officers, the ability to maintain their operational edge in an era of constantly changing threats. The traditional approach of infrequent attendance at residential training and lagging responses to rapidly changing threat warnings is not sufficient to keep up with the evolving threats. Most agencies cannot afford to send their officers to more frequent residential courses to catch up, even though the gap means more risk in terms of preparedness and First-Responder safety. An OATS uses distributed learning technology to provide up-to-date training to First Responders anywhere they have access to a computer and can a secure link to the Internet.

The Internet provides key infrastructure for an OATS. It allows schools to access streams of operational intelligence data from around the world. Similarly, it allows those schools to analyze that data and distribute up-to-date training materials to any agency using Learning Management Systems. The weak link is the transformation of the analyzed intelligence data into training materials, which is a costly, labor-intensive process that requires centralized peak-load staffing to maintain responsiveness.

This paper describes research on emerging Semantic Web technologies to identify and isolate changes in training materials based on the analysis of incoming intelligence information. An ontology is used as a requirements traceability model to capture generic training requirements and link them to training assets. The incoming data is processed to determine which requirements are affected and to identify the assets to be modified. Critical tasks and performance measures are updated, as necessary, to meet the new intelligence, and simulation-based initial conditions and assessment methods are generated for these measures.