Graph-Enhanced Transformer Framework for Context-Sensitive English Skill Assessment

The integration of Artificial Intelligence (AI) into English Language Teaching (ELT) has enabled personalized and interactive learning, yet most existing systems rely on static, rule-based feedback models, which fail to capture learner history or adapt interventions based on skill interdependencies. These limitations result in generic management, reduced learner engagement, and fragmented skill development. To overcome these challenges, this study proposes a hybrid DeBERTa–GAT–PPO framework that combines transformer-based contextual embeddings, graph attention-based inter-skill modeling, and reinforcement learning for adaptive, history-aware feedback. The model is implemented in Python 3.10 using PyTorch 2.0 and processes the Kaggle Feedback Prize – English Language Learning dataset, containing over 6,600 annotated essays across cohesion, syntax, vocabulary, phraseology, grammar, and conventions. Learner essays are preprocessed, embedded via DeBERTa, and represented as a knowledge graph to capture skill interdependencies through GAT. The PPO agent then generates context-sensitive feedback optimized via policy gradients. Experimental results demonstrate that the proposed framework achieves an accuracy of 89.8% and an AUC of 0.96, representing an approximate 6 to 8% improvement over baseline models such as BERT and RoBERTa. Visualizations and ablation studies confirm effective learning of inter-skill dependencies and reinforcement-based feedback adaptation. Overall, the proposed model provides scalable, interpretable, and pedagogically effective feedback, bridging the gap between conventional AI tutors and fully adaptive, learner-centered systems, thus advancing the state-of-the-art in intelligent English language tutoring.

@article{Shalini2025,
  title     = {Graph-Enhanced Transformer Framework for Context-Sensitive English Skill Assessment},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Anna Shalini and Myagmarsuren Orosoo and W. Grace Shanthi and Prema S and S. Farhad and Elangovan Muniyandy and A. Chrispin Antonieta Dhivya},
  doi       = {10.14569/IJACSA.2025.0161162},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161162}
}