Adaptive Learning Difficulty Calibration System

Develop a sophisticated Python-based system that dynamically adjusts learning content difficulty based on individual student performance. Use Bayesian inference techniques to create a probabilistic model that can predict optimal learning difficulty, implement adaptive testing algorithms, and generate personalized learning recommendations. Include comprehensive logging and model performance tracking mechanisms.