从EduVisBench到EduVisAgent：面向推理驱动的教学可视化的基准与多智能体框架
From EduVisBench to EduVisAgent: A Benchmark and Multi-Agent Framework for Reasoning-Driven Pedagogical Visualization
AI总结：
本文提出多领域多层次基准EduVisBench评估基础模型的教学视觉推理能力，并设计多智能体协作框架EduVisAgent，通过协调专门智能体进行推理分解与可视化设计，显著提升了生成教育一致性可视化的效果。
摘要：
尽管基础模型（FMs），如扩散模型和大型视觉语言模型（LVLMs），已广泛应用于教育场景，但它们生成具有教学有效性的视觉解释的能力仍然有限。大多数现有方法主要关注文本推理，忽略了结构化和可解释的可视化在支持概念理解中的关键作用。为了更好地评估FMs在教育场景中的视觉推理能力，我们引入了EduVisBench，这是一个多领域、多层次的基准。EduVisBench包含需要视觉支撑解决方案的多样化STEM问题集，以及基于教学理论的细粒度评估准则。我们的实证分析表明，现有模型经常难以应对分解复杂推理并将其转化为与人类认知过程对齐的视觉表征这一固有挑战。为了解决这些局限性，我们提出了EduVisAgent，这是一个多智能体协作框架，协调专门智能体进行教学规划、推理分解、元认知提示和可视化设计。实验结果表明，EduVisAgent显著优于所有基线，实现了40.2%的提升，并提供了更具教育一致性的可视化。
英文摘要：
While foundation models (FMs), such as diffusion models and large vision-language models (LVLMs), have been widely applied in educational contexts, their ability to generate pedagogically effective visual explanations remains limited. Most existing approaches focus primarily on textual reasoning, overlooking the critical role of structured and interpretable visualizations in supporting conceptual understanding. To better assess the visual reasoning capabilities of FMs in educational settings, we introduce EduVisBench, a multi-domain, multi-level benchmark. EduVisBench features diverse STEM problem sets requiring visually grounded solutions, along with a fine-grained evaluation rubric informed by pedagogical theory. Our empirical analysis reveals that existing models frequently struggle with the inherent challenge of decomposing complex reasoning and translating it into visual representations aligned with human cognitive processes. To address these limitations, we propose EduVisAgent, a multi-agent collaborative framework that coordinates specialized agents for instructional planning, reasoning decomposition, metacognitive prompting, and visualization design. Experimental results show that EduVisAgent substantially outperforms all baselines, achieving a 40.2% improvement and delivering more educationally aligned visualizations. EduVisBench and EduVisAgent are available at https://github.com/aiming-lab/EduVisBench and https://github.com/aiming-lab/EduVisAgent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
