AI in teaching English writing: automatic scoring and feedback system

Artificial intelligence technology, with its excellent data processing and analyzing ability, has become a new method for scientifically evaluating the quality of students’ writing. In this paper, we construct an automatic scoring model for English writing using a hybrid neural network, then use a random forest algorithm to categorize the assignment utterances and design an automatic writing feedback system. The results of the teaching case show that the mean posttest writing score of the experimental class is 16.832, and the mean value of the posttest writing error rate is 7.278, both of which perform significantly better than the control class. Moreover, the assignments from the experimental class demonstrated a higher level of word performance. As the experiment progressed, the students’ perceptions of this paper’s system grew more positive. This underscores the significance of the research conducted in this paper.