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Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction

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arxiv 2306.03090 v1 pith:7QZHMSQ7 submitted 2023-06-05 cs.CL cs.AI

classification cs.CLcs.AI
keywords teacherclassroomteacherschatgptcoachcoachingexpertfeedback
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Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript segments based on classroom observation instruments, (B) identifying highlights and missed opportunities for good instructional strategies, and (C) providing actionable suggestions for eliciting more student reasoning. We recruit expert math teachers to evaluate the zero-shot performance of ChatGPT on each of these tasks for elementary math classroom transcripts. Our results reveal that ChatGPT generates responses that are relevant to improving instruction, but they are often not novel or insightful. For example, 82% of the model's suggestions point to places in the transcript where the teacher is already implementing that suggestion. Our work highlights the challenges of producing insightful, novel and truthful feedback for teachers while paving the way for future research to address these obstacles and improve the capacity of generative AI to coach teachers.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. A Benchmark for Math Misconceptions: Bridging Gaps in Middle School Algebra with AI-Supported Instruction

    cs.HC 2024-12 conditional novelty 5.0 of 10

    A new benchmark of 55 algebra misconceptions and 220 examples shows GPT-4-turbo diagnoses around 53% of misconceptions overall, 75% when topic-constrained, and 83.9% when educator feedback is included.

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