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arXiv · 2405.18623

I See You: Teacher Analytics with GPT-4 Vision-Powered Observational Assessment

Unggi Lee·Yeil Jeong·Junbo Koh·Gyuri Byun·Yunseo Lee·Hyunwoong Lee·Seunmin Eun·Jewoong Moon·Cheolil Lim·Hyeoncheol Kim
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Abstract

This preliminary study explores the integration of GPT-4 Vision (GPT-4V) technology into teacher analytics, focusing on its applicability in observational assessment to enhance reflective teaching practice. This research is grounded in developing a Video-based Automatic Assessment System (VidAAS) empowered by GPT-4V. Our approach aims to revolutionize teachers' assessment of students' practices by leveraging Generative Artificial Intelligence (GenAI) to offer detailed insights into classroom dynamics. Our research methodology encompasses a comprehensive literature review, prototype development of the VidAAS, and usability testing with in-service teachers. The study findings provide future research avenues for VidAAS design, implementation, and integration in teacher analytics, underscoring the potential of GPT-4V to provide real-time, scalable feedback and a deeper understanding of the classroom.

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Unggi Lee, Yeil Jeong, Junbo Koh, Gyuri Byun, Yunseo Lee, Hyunwoong Lee, Seunmin Eun, Jewoong Moon, Cheolil Lim, Hyeoncheol Kim. 2024-05-30. I See You: Teacher Analytics with GPT-4 Vision-Powered Observational Assessment. https://arxiv.org/abs/2405.18623

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