AI-driven Personalized Feedback Systems for Improving Japanese Writing Proficiency in University Education

Japanese language learners have a difficult task of grammatical error correction (GEC) because of the complex morphology particle use and proficiency level variations. Non-adaptive rule-based systems and statistical models have in many cases the difficulty to explain or model contextual dependencies and learner specific error patterns. This paper suggests a combined fine-tuned Japanese BERT with Seq2Seq Transformer as proficiency classifier, error detector and with grammatical mender for improving Japanese writing proficiency in University. Data was gathered on the basis of the Japanese learner corpora including the sentences with proficiency annotation and corrected references basing on JFLEG-JP and institutional learner data. Using these inputs, the framework jointly learns proficiency estimation and grammatical correction; Cross-entropy loss is employed to optimize both proficiency classification and sequence generation tasks, enabling effective contextual learning and accurate correction performance. Fine-tuning of the last four layers of BERT helps to detect semantic and syntactic subtleties. The suggested model is assessed with the help of the BLEU, Precision, Recall, and F1-score measures. Experimental outcomes show better results of a BLEU score of 61.9, Precision of 0.81, Recall of 0.78 and an F1-score of 0.79 which beats the current baseline models. The proposed framework enables proficiency-aware contextual grammatical correction with adaptive pedagogical feedback for Japanese university writing support.