用大模型分析1.3万条教师对话，揭示真实教学中如何用AI。
How K-12 Educators Use AI: LLM-Assisted Qualitative Analysis at Scale
- 用大模型辅助开展大规模质性分析，保留研究者对主题提炼的控制权。
- 发现教师在备课、分层教学、评估和反思中频繁使用AI并调整生成内容。
- 为设计更贴合教学实际的AI教育工具提供实证支持，适合教育技术开发者参考。
本研究基于一个开放平台收集的超过13,000条未预设的教师与AI互动对话，探讨K-12教师在真实教学情境中如何使用生成式AI工具，以及大语言模型（LLMs）如何支持对这些交互的大规模质性分析。研究聚焦教师在教案设计、差异化教学、评估及教学反思中的应用。方法上，提出一种可复现的LLM辅助质性分析流程，支持归纳主题发现、编码本构建与大规模标注，同时保持研究者对概念整合的主导权。实证结果显示，教师在提示、调整和评估AI生成建议时展现出具体的行为模式，体现了其教学推理过程。本研究证明了将大模型支持与质性严谨性结合，在大规模分析复杂教师行为方面的可行性，并为设计面向教育场景的AI工具提供了依据。
原文摘要 · Abstract (English)
This study investigates how K-12 educators use generative AI tools in real-world instructional contexts and how large language models (LLMs) can support scalable qualitative analysis of these interactions. Drawing on over 13,000 unscripted educator-AI conversations from an open-access platform, we examine educators' use of AI for lesson planning, differentiation, assessment, and pedagogical reflection. Methodologically, we introduce a replicable, LLM-assisted qualitative analysis pipeline that supports inductive theme discovery, codebook development, and large-scale annotation while preserving researcher control over conceptual synthesis. Empirically, the findings surface concrete patterns in how educators prompt, adapt, and evaluate AI-generated suggestions as part of their instructional reasoning. This work demonstrates the feasibility of combining LLM support with qualitative rigor to analyze complex educator behaviors at scale and inform the design of AI-powered educational tools.
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