A Contextualized Learner-Profiling Transformer Architecture for Adaptive Grammar Error Diagnosis and Instruction

Grammatical accuracy is a critical component of English as a Second Language (ESL) learning; however, many learners continue to struggle with recurring errors despite the availability of automated grammar correction tools. Although recent transformer-based models such as BERT, GPT, and T5 have demonstrated strong benchmark performance, existing grammar error correction (GEC) systems remain largely correction-oriented and lack pedagogical flexibility, learner awareness, and explanation-based feedback. To address these limitations, this study proposes an Adaptive Multi-Task T5 (AMT-T5) framework that integrates grammatical error correction, error-type classification, and personalized feedback generation within a unified transformer architecture. The proposed method is designed to actively support learner development by maintaining dynamic learner error profiles and adaptively reweighting attention to provide targeted instructional guidance. AMT-T5 is implemented using Python, PyTorch, and the Hugging Face Transformers library, and trained on the Lang-8 Learner Corpus, which contains authentic ESL learner sentences with expert corrections. Experimental results demonstrate that the proposed model significantly outperforms existing transformer-based baselines, achieving 78.9 BLEU, 90.7 GLEU, 82.6% full-sentence accuracy, and an error reduction rate of 91.2%, representing an approximate 18–22% improvement in grammatical accuracy over prior models. The framework further incorporates Direct Preference Optimization to align corrections with pedagogical expectations and Knowledge Distillation to enable efficient real-time deployment. Overall, the proposed AMT-T5 framework transforms grammar correction from a passive editing task into an adaptive, learner-centered educational process, offering a scalable and effective solution for intelligent ESL grammar learning systems.

@article{Shobharani2026,
  title     = {A Contextualized Learner-Profiling Transformer Architecture for Adaptive Grammar Error Diagnosis and Instruction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Bukka Shobharani and M Vijaya Lakshmi and Kama Ramudu and Jasgurpreet Singh Chohan and S. Farhad and Elangovan Muniyandy and Gulnaz Fatma and Ahmed I. Taloba},
  doi       = {10.14569/IJACSA.2026.0170153},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170153}
}