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

Arwa Almubarak1,2,3, Wadee Alhalabi1,4, Ibrahim Albidewi1,2

  • 1Department of Computer Sciences, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in Artificial Intelligence
|September 19, 2025
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Summary

This study introduces an AI framework using computer vision to objectively evaluate teacher performance by analyzing classroom interactions. YOLOv8 demonstrated superior accuracy in identifying interaction categories, offering a more reliable alternative to traditional methods.

Keywords:
computer visiondeep learningeducationimproving classroom teachingin-classroom interactionobject detectionteacher performanceteacher performance evaluation

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Educational Technology

Background:

  • Traditional teacher performance evaluations rely on subjective, labor-intensive observation methods.
  • Current methods often lack consistent reliability and objective data for professional development.
  • There is a need for objective and scalable solutions to assess classroom dynamics.

Purpose of the Study:

  • To develop and evaluate an AI-powered computer vision framework for objective assessment of classroom interactions.
  • To compare the performance of state-of-the-art object detection models in identifying teacher-student interactions.
  • To provide a foundation for evidence-based teacher evaluation and feedback.

Main Methods:

  • A computer-vision framework was developed utilizing YOLOv8, Faster R-CNN, and RetinaNet object detection models.
  • A dataset of 7,259 classroom images was annotated to identify eleven distinct interaction categories.
  • Model performance was quantitatively assessed using mean Average Precision (mAP).

Main Results:

  • YOLOv8 achieved the highest performance with a mean Average Precision (mAP) of 85.8%.
  • The AI framework demonstrated strong accuracy in detecting diverse classroom interactions.
  • Faster R-CNN and RetinaNet showed competitive but ultimately lower performance compared to YOLOv8.

Conclusions:

  • Deep learning-based computer vision offers a more objective and reliable method for evaluating teacher-student interactions.
  • The proposed AI framework supports evidence-based educational assessment.
  • This technology has the potential to significantly enhance teacher feedback and improve educational outcomes.