多智能体协作生成数学题，精准控制难度与清晰度。
Multi-Agent Collaborative Framework For Math Problem Generation
- 多个智能体迭代优化题目与答案，动态调节复杂度。
- 在五项评估标准上表现更优，尤其在难度匹配上提升明显。
- 适合教育AI研发者和自适应学习系统开发者参考。
自动数学题生成（AQG）仍是智能辅导系统和教育工作者面临的挑战。尽管基于预训练Transformer的语言模型显著推动了自然语言生成，但往往难以精确控制题目复杂度和认知要求。本文提出一种协同多智能体框架，将推理时计算融入题生成过程。该方法通过多个智能体迭代优化生成的题-答对，更好地平衡复杂度与认知需求。我们在五个元评估标准（相关性、重要性、清晰度、难度匹配、可答性）上评估生成题目，以检验系统对题目复杂度与质量的控制能力。初步评估显示，该协同多智能体框架通过更精细地协调认知挑战与清晰度，显著提升了生成内容的质量。这些有前景的结果表明，集成协同多智能体工作流可生成更可控、更具教学价值的内容，有助于推进自动化教育内容生成与自适应学习环境发展。
原文摘要 · Abstract (English)
Automatic question generation (AQG) for mathematics education remains an elusive goal for Intelligent Tutoring Systems and educators. While pre-trained transformer-based language models have significantly advanced natural language generation, they often struggle to precisely control problem complexity and cognitive demands. In this paper, we introduce a collaborative multi-agent framework as a novel method of incorporating inference-time computation into AQG. This approach leverages multiple agents that iteratively refine generated question-answer pairs to better balance complexity and cognitive demand. We evaluate the generated questions on five meta-evaluation criteria: relevance, importance, clarity, difficulty matching, answerability, to assess the system's ability to control the required complexity and quality of the questions. Preliminary evaluations show that this collaborative multi-agent framework elevates the quality of generated educational content by fostering a more nuanced balance between cognitive challenge and clarity. These promising outcomes suggest that integrating collaborative multi-agent workflows can yield more controlled, pedagogically valuable content that can help advance automated educational content generation and adaptive learning environments.
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