RoBERTa-Enhanced Actor–Critic Reinforcement Learning for Adaptive and Personalized ESL Instruction

Technology-Assisted Language Learning (TALL) has developed and has greatly transformed the way English as a Second Language (ESL) is taught. The current digital resources and smart solutions have enabled more interactive and accessible learning, providing learners with an opportunity to train their skills at any time and place. Nevertheless, most of the current systems remain based on strict rules or conventional supervised training approaches. These methods can demand large quantities of labelled data, are inflexible in the learning process, and have little in the way of individualized feedback. Consequently, students may remain inattentive, and the acquisition of all the necessary language skills, such as reading, writing, listening, and speaking, may be unequal. In order to address such shortcomings, this study presents T-RLNN (RoBERTa-based Reinforcement Learning Neural Network), which is a dynamic model of ESL teaching. T-RNN combines deep contextual language comprehension and reinforcement learning in order to customize teaching to every learner. The RoBERTa encoder can retrieve semantic and syntactic feedback on responses of learners, and an actor-critic reinforcement learning agent can modify teaching plans in real time. The agent takes into account the learner-specific factors, i.e., proficiency, response time, engagement, and interaction behavior, to give the best guidance. It was trained in Python using PyTorch and tested on a curated dataset of 5,000 responses of a learner in reading, writing, listening, and speaking tasks. T-RLNN performed better than conventional models, such as Support Vector Machines, random forests, and conventional deep neural networks, with a 94.8 % accuracy, 92.7 % F1 -score, and 71.5 % Adaptivity Index. These findings indicate that T-RLNN has the potential to provide insightful, interactive, and learner-oriented ESL training and open the way to smarter and more adaptable language learning systems.

@article{Aravind2026,
  title     = {RoBERTa-Enhanced Actor–Critic Reinforcement Learning for Adaptive and Personalized ESL Instruction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Angalakuduru Aravind and A. Swathi and Jillellamoodi Naga Madhuri and R. Aroul Canessane and K. Lalitha Vanisree and Elangovan Muniyandy and Rasha M. Abd El-Aziz},
  doi       = {10.14569/IJACSA.2026.0170156},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170156}
}