Research on AI-driven Personalized English Writing Feedback Mechanism

Abstract

: This study focuses on the construction of personalized English writing feedback mechanism driven by artificial intelligence, which breaks through the limitation that existing automatic writing evaluation tools only focus on surface language form error correction, and proposes a four-layer system architecture (data layer, processing layer, strategy engine layer and interaction layer). In this study, a three-level differentiated feedback model based on students' level (L1 basic level, L2 development level and L3 proficiency level) is designed, which combines the wrong "fingerprint" strengthening strategy and metacognitive excitation mechanism to guide students to correct themselves instead of directly providing answers. At the same time, a three-stage teacher-AI collaborative process is constructed to retain the core value of teachers in complex content evaluation and emotional support. Through an 8-week quasi-experimental study of two parallel classes (60 students in total) in Grade Two of a middle school, the results show that the post-test writing performance of the experimental group is significantly higher than that of the control group (t=5.67, p<0.01), with an average increase of 4.34 points. The recurrence rate of high-frequency errors in the experimental group was significantly lower than that in the control group (P < 0.05). The feedback viewing rate of students reached 94.2%, and the revised adoption rate reached 76.8%, and the students with weak foundation (L1 layer) made the most obvious progress. The research shows that the AI-driven personalized feedback mechanism can effectively improve students' writing performance, reduce the error recurrence rate and enhance students' active revision behavior, which provides a feasible technical path and practical paradigm for the digital transformation of foreign language education.