ClassMind: Scaling Classroom Observation and Instructional Feedback with Multimodal AI
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915506799247360 |
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| author | Qu, Ao Wen, Yuxi Zhang, Jiayi Wen, Yunge Zhao, Yibo Prakash, Alok Salazar-Gómez, Andrés F. Liang, Paul Pu Zhao, Jinhua |
| author_facet | Qu, Ao Wen, Yuxi Zhang, Jiayi Wen, Yunge Zhao, Yibo Prakash, Alok Salazar-Gómez, Andrés F. Liang, Paul Pu Zhao, Jinhua |
| contents | Classroom observation -- one of the most effective methods for teacher development -- remains limited due to high costs and a shortage of expert coaches. We present ClassMind, an AI-driven classroom observation system that integrates generative AI and multimodal learning to analyze classroom artifacts (e.g., class recordings) and deliver timely, personalized feedback aligned with pedagogical practices. At its core is AVA-Align, an agent framework that analyzes long classroom video recordings to generate temporally precise, best-practice-aligned feedback to support teacher reflection and improvement. Our three-phase study involved participatory co-design with educators, development of a full-stack system, and field testing with teachers at different stages of practice. Teachers highlighted the system's usefulness, ease of use, and novelty, while also raising concerns about privacy and the role of human judgment, motivating deeper exploration of future human--AI coaching partnerships. This work illustrates how multimodal AI can scale expert coaching and advance teacher development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ClassMind: Scaling Classroom Observation and Instructional Feedback with Multimodal AI Qu, Ao Wen, Yuxi Zhang, Jiayi Wen, Yunge Zhao, Yibo Prakash, Alok Salazar-Gómez, Andrés F. Liang, Paul Pu Zhao, Jinhua Human-Computer Interaction Classroom observation -- one of the most effective methods for teacher development -- remains limited due to high costs and a shortage of expert coaches. We present ClassMind, an AI-driven classroom observation system that integrates generative AI and multimodal learning to analyze classroom artifacts (e.g., class recordings) and deliver timely, personalized feedback aligned with pedagogical practices. At its core is AVA-Align, an agent framework that analyzes long classroom video recordings to generate temporally precise, best-practice-aligned feedback to support teacher reflection and improvement. Our three-phase study involved participatory co-design with educators, development of a full-stack system, and field testing with teachers at different stages of practice. Teachers highlighted the system's usefulness, ease of use, and novelty, while also raising concerns about privacy and the role of human judgment, motivating deeper exploration of future human--AI coaching partnerships. This work illustrates how multimodal AI can scale expert coaching and advance teacher development. |
| title | ClassMind: Scaling Classroom Observation and Instructional Feedback with Multimodal AI |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.18020 |