针对通用大语言模型在地理命题中存在的科学性不足、目标偏离等痛点，本文引入检索增强生成技术，构建“教师主导、AI辅助”的智能协同命题路径。本文以“珠江口风暴潮”为例的命题实践，表明该路径通过构建可控知识空间，可有效提升命题的科学性与适恰性。本文提炼基于RAG的人机协同命题双循环模型，为地理测评创新提供实践范式。

Addressing key challenges of general large language models in geography test item development—such as insufficient scientific rigor and misalignment with assessment objectives—the paper introduces Retrieval-Augmented Generation (RAG) technology to establish an intelligent, collaborative item-writing approach characterized by“teacherled, AI-assisted”synergy. A practical case focusing on “storm surges in the Pearl River Estuary” demonstrates that this approach, by constructing a controllable knowledge space, eff ectively enhances both the scientifi c validity and contextual appropriateness of test items. The paper further proposes a dual-cycle human-AI collaborative item development model based on RAG, off ering a practical paradigm for innovation in geographical assessment.