This paper presents a multi-task transformer ensemble that unifies Grammatical Error Correction, Tense Prediction, and CEFR-level Proficiency Grading within a single explainable framework for English language learning. The system integrates fine-tuned transformer models—Flan-T5, BERT, RoBERTa, and DistilBERT—through a confidence-weighted ensemble to generate grammatical corrections, verify tense consistency, and predict learner proficiency levels. The framework was trained and validated on large-scale linguistic datasets, including BEA-2019, C4, PTB, BNC, EFCAMDAT, and CLC, to improve robustness across writing domains. Experimental results show that the Flan-T5 GEC module achieved BLEU = 32.4 with a 36.8% reduction in training time, while the RoBERTa tense classifier reached F1 = 0.99. For CEFR grading, the model achieved κ = 0.90 , indicating strong aggregate agreement with human ratings, although performance at the most advanced proficiency level remained more challenging. Compared with non-integrated module outputs, the ensemble further improved overall linguistic accuracy and interpretability, with statistically significant gains in key aggregate metrics (p 0.05). These findings suggest a hierarchical dependency among grammar, tense, and proficiency, while provid- ing an interpretable AI mechanism for delivering personalized and pedagogically meaningful feedback.
Dũng et al. (Fri,) studied this question.