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.].