An AI-Driven VR Learning Framework Using RL-Optimized Transformer Models for Personalized English Proficiency Assessment

Effective English language learning demands adaptive, interactive, and flexible instructional support, which traditional e-learning systems and existing AI tutors struggle to provide due to limited immersion, static feedback mechanisms, isolated task structures, and the absence of robust reward-driven learning strategies. Although prior studies on VR-based learning environments and Natural Language Processing (NLP) have reported enhanced learner motivation and engagement, most existing solutions suffer from fixed task sequencing, limited real-time linguistic intelligence, and inadequate grammar and pronunciation correction capabilities. To address these challenges, this study proposes a Virtual Reality–based architecture named the Self-Evolving Neural Intelligence Tutor (SENIT), driven by Curriculum Reinforcement Learning and Hierarchical Adaptive Weighting. SENIT integrates a fine-tuned T5 transformer for grammar refinement and prosody-aware feedback, while a reinforcement learning agent dynamically adjusts task difficulty and lesson progression based on learner performance. Developed using Python and TensorFlow and deployed within a Unity3D VR environment, SENIT enables realistic conversational simulations and multimodal learner assessment. Experimental evaluation on a dedicated VR English Learning Dataset demonstrates grammar and pronunciation accuracy improvements of 90% and 81%, respectively, outperforming existing models by approximately 12 percentage points. Additionally, learners achieved notable fluency gains and high engagement scores, highlighting SENIT’s effectiveness in delivering personalized, immersive language learning experiences.