Impact of Deep Generative Models on College English Classroom Interaction

With the development of information technology, teaching reforms have been innovated, and the quality of university programs has become higher and higher. However, the emergence and widespread application of deep generative models have brought more challenges and opportunities to classroom teaching. With the continuous development of educational technology and the updating of teaching concepts, the depth generation model, as an emerging teaching mode, is gradually receiving attention from the educational community. In order to verify the effectiveness of the depth generation model, this study selected a certain number of college English classes as experimental subjects, and conducted in-depth data analysis and discussion by comparing the differences between the experimental group and the control group in terms of the frequency of interactions, the quality of interactions, the degree of student participation and learning effects. By comparing the classroom effects of using a deep generative modelling classroom with a traditional classroom, the study found that students’ overall grades in English classrooms using deep generative modelling improved by approximately 31 points, and that the average completion rate of assignments in deep generative modelling classrooms was 94.8%, which was much higher than that of traditional classrooms.