Dr. Arwa Almubarak

Session Summary:

Teacher evaluation remains one of the most subjective, resource-intensive, and inconsistent processes in education—yet it's critical to instructional quality and school improvement. This session introduces a groundbreaking AI-powered framework that leverages deep learning and computer vision to automate teacher performance evaluation based on classroom interaction analysis. Explore the technical architecture, implementation considerations, ethical implications, and practical applications of AI-driven teacher evaluation and contribute to building intelligent educational assessment ecosystems across diverse school contexts. Join this session to: 

 

• Understand the technical framework and methodology of AI-powered teacher evaluation systems and quantify teaching behaviours across standardised interaction categories  

• Evaluate the potential benefits and limitations of automated teacher evaluation systems in educational settings 

• Assess practical implementation pathways and use cases for AI-driven teacher evaluation tools within school quality assurance frameworks for better stakeholder acceptance and trust-building 

Speakers

• Dr. Arwa Almubarak | Teacher and PhD Student, Faculty of Computing and Information Technology, Department of Computer Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

التصنيفات

• AI and EdTech