用大模型生成高质量数学练习题，提升教学效果。
From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models
- 基于示例和课程内容提示，提升生成题目的质量。
- 无需复杂指令，低投入即可生成多层级数学题目。
- 适合教育工作者快速构建个性化练习内容。
教育者开始利用生成式AI（GenAI）辅助创建课程内容，但如何有效使用仍不明确。本研究探索了在高级数学教学中优化内容生成的初步步骤，重点考察了公开可用的GenAI生成高质量练习题的能力。通过两项研究：(1) 分析当前GenAI版本的生成能力；(2) 针对发现的局限性提出改进框架。结果表明，GenAI可在极少支持下生成不同质量的数学题目，但提供示例和相关课程内容可显著提升输出质量。该研究为教育者合理采用GenAI、提升学生学习体验提供了实用指导。
原文摘要 · Abstract (English)
Educators have started to turn to Generative AI (GenAI) to help create new course content, but little is known about how they should do so. In this project, we investigated the first steps for optimizing content creation for advanced math. In particular, we looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content. We conducted two studies to: (1) explore the capabilities of current versions of publicly available GenAI and (2) develop an improved framework to address the limitations we found. Our results showed that GenAI can create math problems at various levels of quality with minimal support, but that providing examples and relevant content results in better quality outputs. This research can help educators decide the ideal way to adopt GenAI in their workflows, to create more effective educational experiences for students.
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