Adaptive learning system based on virtual reality and reinforcement learning

Aiming at the problem of insufficient real-time adjustment capability of adaptive learning system in the field of Virtual Reality (VR) education due to insufficient utilization of multimodal behavior data and lack of dynamic strategy, this paper proposes a framework integrating VR and Deep Reinforcement Learning (DRL). Based on the Deep Q-Network (DQN), a reinforcement learning (RL) model is constructed, and a high-dimensional state space is constructed based on the learner’s cognitive state characteristics. The dynamic adjustment action space of teaching content, difficulty, and feedback is defined, and a reward function for collaborative optimization of learning effect gain and time efficiency is established. This paper develops a multi-scenario virtual learning environment, integrates motion capture and eye tracking technology to collect multimodal behavior data in real time, and realizes dynamic decision-making and deployment of teaching actions through a closed-loop strategy optimization mechanism driven by multi-source data. The experimental results show that the system significantly improves the mastery of professional course knowledge, and the accuracy of the two core knowledge points of binary modulation comparison and common-emitter amplifier circuit analysis is increased to 83.6% and 85.6%, respectively. The average response time of eye movement selection in simple scenarios of content adjustment is 143 milliseconds. This framework verifies the synergistic effect of multimodal data and real-time strategy optimization, forming a reusable adaptive learning technology paradigm, and providing theoretical support and methodological reference for the construction of a dynamic decision-making closed loop in the education system.