A Production-Deployed Human-in-the-Loop Sequential Statistical Process Control Framework for Adaptive Cognitive Skill Optimization Using the OXBRIDGE HIGH TABLE™ (OHT™) Engine

This paper presents a production-deployed stochastic control architecture for adaptive cog-
nitive skill optimization using classical Industrial Engineering principles. The learner is formally
modeled as a stochastic production unit whose internal state evolves under constrained Statisti-
cal Process Control (SPC) stability limits. The system integrates human instructors as semantic
anomaly detection sensors and cognitive control actuators within a closed-loop sequential deci-
sion framework. A population-specific bias tensor is introduced to model structured transition
distortions in cognitive state evolution. Instructional policy is optimized using a target-directed
reward function ensuring stable convergence toward mastery states while preserving system sta-
bility. The architecture is actively deployed in a live commercial educational environment and
has executed thousands of closed-loop instructional control cycles.