Personalized Learning Path Optimization Using Reinforcement Learning

Develop a reinforcement learning framework for dynamically generating personalized student learning paths. Create a multi-armed bandit algorithm that adapts curriculum recommendations based on individual student performance, learning style, and engagement metrics. Implement a reward function that balances academic progression, student motivation, and skill acquisition. Design an ethical decision-making framework that prevents algorithmic bias and ensures transparent recommendation mechanisms.