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arXiv AI By Y. Fong, J. Xiang, T. Y. D. Chan, K. Lee, E. Y. H. Lau

I code or AI code: A comparative evaluation of AI-rated scores in classroom observations

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The study evaluated whether a large language model (GPT‑5) could score teacher‑child interactions in early childhood classrooms using the Classroom Assessment Scoring System (CLASS) framework, comparing its results to human raters. Across 87 video‑recorded observations from 38 classrooms in Hong Kong, AI scores converged most closely with human ratings in the Emotional Support domain, especially the Quality of Feedback dimension, while diverging more in procedural or context‑dependent areas such as Classroom Organization and Instructional Support. The findings suggest that transcript‑based AI scoring can serve as a preliminary screening tool to aid teacher reflection, but it is not yet reliable enough to replace trained observers for full CLASS evaluations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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