Abstract: 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.

A. Sri Lakshmi, E. S. Sharmila Sigamany, Revati Ramrao Rautrao, K. Ezhilmathi, Dr. Bhuvaneswari Pagidipati, Elangovan Muniyandy and Dr. Adlin Sheeba. “An AI-Driven VR Learning Framework Using RL-Optimized Transformer Models for Personalized English Proficiency Assessment”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170120

@article{Lakshmi2026,
title = {An AI-Driven VR Learning Framework Using RL-Optimized Transformer Models for Personalized English Proficiency Assessment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170120},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170120},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {1},
author = {A. Sri Lakshmi and E. S. Sharmila Sigamany and Revati Ramrao Rautrao and K. Ezhilmathi and Dr. Bhuvaneswari Pagidipati and Elangovan Muniyandy and Dr. Adlin Sheeba}
}