How K-12 Educators Use AI: LLM-Assisted Qualitative Analysis at Scale

TL;DR

This paper explores how K-12 educators utilize generative AI in teaching, introducing a scalable LLM-assisted analysis method that uncovers patterns in educator-AI interactions and informs AI tool design.

Contribution

It presents a novel, replicable pipeline for large-scale qualitative analysis of educator-AI interactions, combining LLM support with qualitative research rigor.

Findings

Educators use AI for lesson planning, assessment, and reflection.

Patterns in prompting and adapting AI suggestions reveal instructional reasoning.

The analysis supports scalable, rigorous study of complex educator behaviors.

Abstract

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.