I see you: teacher analytics with GPT-4 vision-powered observational assessment

Abstract This preliminary study explores how GPT-4 Vision (GPT-4V) technology can be integrated into teacher analytics through observational assessment, aiming to improve reflective teaching practice. Our study develops a Video-based Automatic Assessment System (VidAAS) powered by GPT-4V. This approach uses Generative Artificial Intelligence (GenAI) to provide detailed insights into classroom dynamics. Our study encompasses various methods with multiple steps: a comprehensive literature review, prototype development of the VidAAS, and usability testing with in-service teachers. The study findings reveal that VidAAS demonstrates high accuracy in evaluating skills in the behavioral (psychomotor) domain and offers comprehensive explanations for each assessment. While showing promise in these areas, the system also indicates potential for further enhancement in processing speed and refinement in assessing cognitive and affective domains. We discuss how VidAAS supports teachers’ reflection-in-action and reflection-on-action, emphasizing the need to balance AI-driven insights and human judgment. Our study findings also guide future research avenues for VidAAS design, implementation, and integration in teacher analytics, underscoring GPT-4V’s potential for real-time, scalable feedback and a deeper classroom understanding.