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.

翻译：本研究探讨了K-12教育工作者在真实教学情境中如何使用生成式人工智能工具，以及大语言模型如何支持对这些交互进行可扩展的定性分析。基于一个开放平台超过13,000条非脚本化的教育工作者与AI对话记录，我们考察了教育工作者在课程规划、差异化教学、评估和教学反思方面对AI的应用。在方法论层面，我们提出了一种可复现的、LLM辅助的定性分析流程，该流程支持归纳式主题发现、编码本开发和大规模标注，同时保持研究者对概念综合的掌控。实证结果表明，教育工作者在其教学推理过程中提示、调整和评估AI生成建议的具体模式。这项工作证明了将LLM支持与定性严谨性相结合以规模化分析复杂教育工作者行为并指导AI赋能教育工具设计的可行性。