I See You: Teacher Analytics with GPT-4 Vision-Powered Observational Assessment
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909213116071936 |
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| author | Lee, Unggi Jeong, Yeil Koh, Junbo Byun, Gyuri Lee, Yunseo Lee, Hyunwoong Eun, Seunmin Moon, Jewoong Lim, Cheolil Kim, Hyeoncheol |
| author_facet | Lee, Unggi Jeong, Yeil Koh, Junbo Byun, Gyuri Lee, Yunseo Lee, Hyunwoong Eun, Seunmin Moon, Jewoong Lim, Cheolil Kim, Hyeoncheol |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18623 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | I See You: Teacher Analytics with GPT-4 Vision-Powered Observational Assessment Lee, Unggi Jeong, Yeil Koh, Junbo Byun, Gyuri Lee, Yunseo Lee, Hyunwoong Eun, Seunmin Moon, Jewoong Lim, Cheolil Kim, Hyeoncheol Human-Computer Interaction 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. |
| title | I See You: Teacher Analytics with GPT-4 Vision-Powered Observational Assessment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.18623 |