CLAiTS is an intelligent competency development framework that integrates with existing simulation platforms to transform training from a series of isolated events into a continuous progression-based learning cycle.

 

CLAiTS uses artificial intelligence, machine learning, and large language models to ingest authoritative doctrine, training standards, TTPs, instructor guidance, and human performance data generated during simulation events. This technology enables the system to interpret training requirements, analyze participant performance against established standards, identify training priorities, and generate targeted follow-on training scenarios.

 

Rather than relying solely on manual curriculum updates, instructor interpretation, or static scenario libraries, CLAiTS establishes a closed-loop process that continuously connects doctrine, training execution, performance analysis, instructor oversight, and adaptive scenario generation. Performance observations during the AAR are no longer treated as the end of a training event, but as inputs that directly influence future training scenarios.

 

CLAiTS does not replace instructors; it standardizes the process through which performance data is translated into follow-on scenarios, ensuring that training remains progressive, consistent, and doctrine-aligned

 

This closed-loop process enables three core outcomes:

 

1. Performance Analysis: Measure participant performance against doctrine, standards, TTPs, and organizational requirements to identify strengths and deficiencies.

2. Adaptive Regeneration: Generates targeted follow-on scenarios designed to reinforce strengths, remediate deficiencies, and maintain progression within the participants optimal performance zone.

3. Operational Training Sync: Rapidly synchronize updated authoritative doctrine with training simulators, reducing the time between operational changes and training implementation.