The primary goal of this study is to analyze agentic workflows in education according to the proposed four major technological paradigms: reflection, planning, tool use, and multi-agent collaboration. We critically examine the role of AI agents in education through these key design paradigms, exploring their advantages, applications, and challenges. Second, to illustrate the practical potential of agentic systems, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results suggest this agentic approach may offer improved consistency compared to stand-alone LLMs. Our findings highlight the transformative potential of AI agents in educational settings while underscoring the need for further research into their interpretability and trustworthiness.

翻译：本研究的主要目标是根据提出的四大技术范式——反思、规划、工具使用与多智能体协作——分析教育领域的智能体工作流。我们通过这四个关键设计范式，批判性地审视了AI智能体在教育中的作用，探讨了其优势、应用场景与挑战。其次，为阐明智能体系统的实际潜力，我们展示了一个概念验证应用：一个用于自动化作文评分的多智能体框架。初步结果表明，与独立的LLMs相比，这种智能体方法可能提供更高的一致性。我们的研究结果凸显了AI智能体在教育环境中的变革潜力，同时强调需要进一步研究其可解释性与可信度。