The Use of Executable Cognitive Models in Simulation-Based Intelligent Embedded Training

Abstract

This paper defines a new role for expert models in intelligent embedded training — guiding practice. The integration of problem-based practice with focused, automated instruction has long proven elusive in training systems for complex real-world domains. The training strategy of ‘guided practice’ offers a way to merge the approaches of traditional simulation-based practice and intelligent tutoring's knowledge tracing. The performance of the trainee is dynamically assessed against scenario-specific expectations and performance standards, which are generated during the simulation by embedded models of expert operators. This research developed an executable cognitive model capable of solving realistic simulation scenarios in an expert-level manner, identified and implemented modifications and extensions to this baseline model needed to generate dynamic and adaptive expectations of future trainee actions, and developed means of providing cognitive state information for use in (separate) diagnostic processes, without resorting to full-scale knowledge tracing methods.