Sun Yat-sen University has established an AI-powered classroom teaching evaluation platform to comprehensively enhance the digital literacy of educators. Supported by technologies like image recognition, speech analysis, and text recognition, this platform aggregates real-time data from multimedia lesson plans, recorded teacher-student interactions, and blackboard writings. It employs digital tools to provide immediate feedback on educational dynamics to educators and administrators, enabling them to promptly identify shortcomings in teaching and enhance oversight of classroom activities. The platform is characterized by its innovative approach to evaluating the overall educational process. Moving away from traditional random classroom inspections, it utilizes a data-driven evaluation model that focuses on classroom indicators and tracks anomalies in overall AI scores against established benchmarks. By incorporating this tracking process into supervisory tasks, the platform has significantly boosted the accuracy, diversity, and objectivity of assessments.

Furthermore, the platform archives extensive records of classroom activities, analyzing multimodal (e.g. affective, behavioral, and cognitive) data collected during educational interactions to monitor aspects like student attentiveness, engagement, and teaching style. This analysis provides educators with detailed feedback, fostering a deeper understanding of learning and teaching conditions and empowering them to adjust their strategies and enhance their skills. Consequently, the platform has established a data-driven positive feedback mechanism that encompasses comprehensive evaluation, rigorous supervision, diversified support, and collaborative improvement.