The application of discourse analysis technology based on artificial intelligence in high school English teaching

In response to the problems existing in the application of AI (Artificial Intelligence) discourse analysis technology in high school English teaching, such as insufficient context adaptability of the automatic feedback system and weak teacher-student-AI collaboration mechanisms, this paper proposes an innovative framework of dynamic context modeling and multimodal collaborative drive. By integrating multimodal data streams of voice, text, facial expressions, and body movements in classroom scenes, a cross-modal feature dynamic fusion model is constructed to capture the semantic associations and emotional states of teacher-student interactions in real time. Based on deep reinforcement learning, a feedback algorithm with adaptive state perception capabilities is designed to periodically update the classification parameters of teaching scenes. The teacher's experience rules and AI analysis results are simultaneously embedded in the system decision-making layer through knowledge distillation technology to form a closed-loop mechanism for human-computer collaborative optimization. The experimental results show that the proposed model performs best in multiple indicators. The teacher response delay is the shortest, only 2.19 seconds; the student interaction density is the highest, reaching 14 times per minute; the student emotion scores are concentrated in a high and stable range of 3.4 to 4.3; the average score is 80.83 points. Class participation and satisfaction are also the highest, reaching 69.53% and 3.51 points, respectively, proving the advantages of the model in improving teaching effectiveness.