An AI-powered framework for assessing teacher performance in classroom interactions: a deep learning approach

Introduction Teacher performance evaluation is essential for improving instructional quality and guiding professional development, yet traditional observation-based methods can be subjective, labor-intensive, and inconsistently reliable. This study proposes an AI-powered framework to objectively assess classroom interactions. Methods We developed and evaluated a computer-vision framework using three state-of-the-art object detectors—YOLOv8, Faster R-CNN, and RetinaNet—to identify eleven classroom interaction categories. A labeled dataset of 7,259 images collected from real classroom settings was annotated and used for training and evaluation. Performance was assessed using mean Average Precision (mAP). Results YOLOv8 achieved the best performance among the evaluated models, with an mAP of 85.8%, indicating strong accuracy in detecting diverse classroom interactions. Faster R-CNN and RetinaNet performed competitively but were outperformed by YOLOv8. Discussion/Conclusion The results demonstrate that modern deep learning–based detection can provide more objective and reliable insights into teacher–student interactions than traditional approaches. The proposed framework supports evidence-based evaluation and has the potential to enhance feedback and outcomes in educational practice.].