Teacher performance evaluation is essential to a teacher’s primary responsibilities. This study investigates the process of evaluating teachers’ performance to provide a basis for planning an ongoing professional development program. AI-based systems present an opportunity for objective and reliable evaluations, facilitating the identification of areas requiring improvement and contributing to the overall enhancement of educational quality. Consequently, incorporating AI into evaluating teachers has become a research hotspot. This research proposes an analytical comprehensive AI-based system for teacher evaluation. Moreover, we introduce a novel analytical index system derived from the current adopted Key Performance Indicators (KPI) in the Ministry of Education in Saudi Arabia and an overall algorithm. This study is part of an integrated project to evaluate teachers’ performance using AI. To build our model, we are accurately determining the evaluative weights through 13 workshops held with 283 field experts. The main hypothesis is that the weights were evenly distributed. After studying them, we found they were relatively different by 0.1 to 0.3%. By analyzing T-KPI, we believe that our proposed index system could overcome the limitations of human-based assessments as it assigned appropriate AI techniques for each, thus ensuring accurate, reliable, and objective evaluation. This innovation would also economically impact rationalizing spending and reducing costs.

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.].