This study integrates foundational theories of artificial intelligence to develop a classroom interaction feedback model tailored for environments leveraging artificial intelligence technologies. The model is structured into three primary phases: initiation, reaction, and feedback. To enhance the detection and analysis of interactive behaviors within English classrooms, this research introduces an advanced face detection algorithm derived from YOLOv5s, coupled with the GVT expression recognition model. These tools aim to assist educators in refining both the curriculum and pedagogical approaches based on dynamic classroom interactions. The practical applicability of this interaction model was evaluated through an empirical analysis involving English majors from College Q. The study revealed significant enhancements post-implementation of the intelligent English classroom interaction feedback model. Specifically, there was an increase in the average individual head-up rate by 0.2 and the overall head-up rate in the classroom by 0.178. Additionally, mood scores rose by 0.1 to 0.3, indicating a state of heightened engagement and active participation in classroom activities. Furthermore, the average academic performance of students exhibited a notable improvement of 13.47 points, with a standard deviation of approximately 5.86, suggesting a modest dispersion in achievement levels. The findings demonstrate that the intelligent English classroom interaction feedback model significantly boosts student engagement and concentration, thereby enhancing academic performance and supporting the development of core English literacy skills. This model provides a valuable framework for leveraging artificial intelligence to optimize educational outcomes in higher education settings.