Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem's core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks such as numeric calculation, geometry validation, and visualization, our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms basic LLMs in terms of text coherence, consistency, relevance and similarity, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.

翻译：生成准确且一致的视觉辅助材料是数学教育中的关键挑战，其中几何图形和函数等视觉表征在提升学生理解能力方面发挥着关键作用。本文提出一种新颖的多智能体框架，利用大语言模型（LLMs）实现复杂数学可视化与连贯问题文本的自动化生成。我们的方法不仅简化了精确视觉辅助材料的生成过程，还确保这些辅助材料与问题的核心数学概念保持一致，从而同时改进问题创建与评估环节。通过集成多个各司其职的智能体（分别负责数值计算、几何验证和可视化等任务），本系统能够生成具有数学精确性和情境相关性的视觉辅助问题。在几何与函数两类问题上的评估表明，该方法在文本连贯性、一致性、相关性和相似性方面显著优于基础LLMs，同时保持了原始问题本质的几何与函数完整性。尽管在确保视觉输出一致性方面仍存在挑战，但本框架充分展现了大语言模型在革新教育工作者生成与运用数学教育视觉辅助材料方式上的巨大潜力。