This paper introduces a distributed reinforcement learning-based MAC protocol designed for high-density educational IoT environments. In smart campuses, the reliability of real-time data from student wearable sensors and classroom environmental monitors is often hampered by hidden-node interference as well as network collisions. This phenomenon disrupts the synchronicity required for effective Human-AI collaboration. Current research utilizes an adaptive slot-swapping strategy to ensure stable,