用多智能体系统帮教师个性化生成中学数学题，提升教学适配性。
Mathematics Teachers Interactions with a Multi-Agent System for Personalized Problem Generation
- 教师输入题目基础内容和主题，四类智能体分别评估数学准确、真实感、可读性和现实性。
- 212道题在ASSISTments平台使用，师生普遍希望调整题中现实情境的细节。
- 智能体能有效发现现实性问题，但最终版本中此类问题较少，显示教师控制权重要。
大型语言模型正日益能够根据学习者特征调整教育任务。本研究考察了一种教师参与的多智能体系统，用于个性化生成中学数学题。教师输入基础题目和目标主题后，语言模型生成题目，随后四个专精于不同维度（数学准确性、真实性、可读性、现实性）的AI智能体进行评估。八名中学数学教师利用该系统在ASSISTments平台创建了212道题目并分配给学生。研究发现，教师和学生均希望修改题目中现实情境的细粒度元素，表明真实感与适配性仍存挑战。尽管智能体在生成阶段检测到大量现实性问题，但最终版本中此类问题极少被教师和学生指出。可读性和数学幻觉问题也较为罕见。研究为支持教师控制权的多智能体个性化系统提供了实践启示。
原文摘要 · Abstract (English)
Large language models can increasingly adapt educational tasks to learners characteristics. In the present study, we examine a multi-agent teacher-in-the-loop system for personalizing middle school math problems. The teacher enters a base problem and desired topic, the LLM generates the problem, and then four AI agents evaluate the problem using criteria that each specializes in (mathematical accuracy, authenticity, readability, and realism). Eight middle school mathematics teachers created 212 problems in ASSISTments using the system and assigned these problems to their students. We find that both teachers and students wanted to modify the fine-grained personalized elements of the real-world context of the problems, signaling issues with authenticity and fit. Although the agents detected many issues with realism as the problems were being written, there were few realism issues noted by teachers and students in the final versions. Issues with readability and mathematical hallucinations were also somewhat rare. Implications for multi-agent systems for personalization that support teacher control are given.
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