用智能体工作流让教育更个性化、高效，自动生成试题效果接近真实考题。
Agentic Workflow for Education: Concepts and Applications
- 构建四要素智能体工作流：反思、调用工具、规划任务、多智能体协作。
- 自动生成数学试题与真实考题无显著差异，验证了模型有效性。
- 适合教育科技研发者和教师，可减轻负担、提升教学创新效率。
随着大语言模型和AI智能体的快速发展，智能体工作流在教育领域展现出变革潜力。本文提出教育智能体工作流（AWE），一个包含自我反思、工具调用、任务规划和多智能体协作的四组件模型。与传统线性提示-响应系统不同，AWE基于冯·诺依曼多智能体系统架构，实现从静态交互到动态非线性流程的范式转变，支持可扩展、个性化和协作式任务执行。研究识别出四大核心应用方向：集成学习环境、个性化AI辅助学习、基于模拟的实验探索以及数据驱动决策。一项自动数学试题生成的案例研究显示，AWE生成题目在统计上与真实考题无显著差异，验证了其有效性。AWE为降低教师负担、提升教学质量、推动教育创新提供了可行路径。
原文摘要 · Abstract (English)
With the rapid advancement of Large Language Models (LLMs) and Artificial Intelligence (AI) agents, agentic workflows are showing transformative potential in education. This study introduces the Agentic Workflow for Education (AWE), a four-component model comprising self-reflection, tool invocation, task planning, and multi-agent collaboration. We distinguish AWE from traditional LLM-based linear interactions and propose a theoretical framework grounded in the von Neumann Multi-Agent System (MAS) architecture. Through a paradigm shift from static prompt-response systems to dynamic, nonlinear workflows, AWE enables scalable, personalized, and collaborative task execution. We further identify four core application domains: integrated learning environments, personalized AI-assisted learning, simulation-based experimentation, and data-driven decision-making. A case study on automated math test generation shows that AWE-generated items are statistically comparable to real exam questions, validating the model's effectiveness. AWE offers a promising path toward reducing teacher workload, enhancing instructional quality, and enabling broader educational innovation.
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