Agentic AI-Enhanced Virtual Reality for Adaptive Immersive Learning Environments
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Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real time. Experiments are conducted using Unity3D integrated with Python-based RL agents and simulated student models. Results demonstrate a 35.7% improvement in learner retention and a 42.1% reduction in cognitive overload compared to traditional static VR systems. The agent successfully personalized 92.3% of scenarios without human intervention. Emotional adaptivity and dynamic pacing showed increased engagement and reduced frustration metrics among diverse learners. Agentic VR represents a paradigm shift in intelligent education systems, enabling autonomous, emotionally-aware, and responsive learning environments. The proposed framework outperforms conventional VR platforms by offering real-time adaptive learning without pre-scripted logic. Integrating reinforcement learning, neuro-symbolic reasoning, and affective feedback into a single VR space results in a novel contribution to adaptive educational technology. The reported metrics were obtained from an administrator-controlled user study of 10 participants (aged 18 - 25 years), all of whom participated in three VR-based learning experiences as described in a controlled evaluative protocol. This study provides a foundation for future work on autonomous agents in educational met verses, special education, and lifelong learning systems.
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