Application of an NLP-Based AI Classroom Interaction Analysis System in Teaching Practice

With AI and natural language processing integrated into education, classroom interaction has gone beyond basic questions and responses. A natural language processing–based analysis system using real-time audio, text, and behavioral logs was developed to enable multidimensional capture, dynamic feedback, and visualization. An experiment on 12 university classes (356 students) revealed that AI intervention did not uniformly boost interaction frequency; average response time was reduced by 1.6 s, but extreme value convergence varied. The system's closed-loop feedback allowed instant tracking of behavioral trajectories. Teachers praised its flexible dashboard, while some students reported increased pressure. Local anomalies alongside overall group improvement provide new insights for classroom management and personalized teaching. This system advances interaction evaluation technologies and offers a new sample for big data–driven teaching research, with future work focusing on cross-disciplinary and cross-platform validation.