AI代理通过四类技术范式重塑教育流程，提升作业评分一致性。
Evolution of AI in Education: Agentic Workflows
- 基于反思、规划、工具使用和多代理协作四类设计范式构建教育代理流程。
- 多代理作文评分框架初步显示比单一大模型更一致的评分表现。
- 适合关注AI教育应用与可解释性研究的研究者与教育技术开发者。
本研究旨在根据提出的四大技术范式——反思、规划、工具使用和多代理协作，分析教育领域中的智能体工作流。我们批判性地考察了人工智能代理在这些关键设计范式中的角色，探讨其优势、应用场景及挑战。其次，为展示智能体系统的实际潜力，我们提出一个概念验证应用：用于自动化作文评分的多代理框架。初步结果显示，该代理方法相比独立的大语言模型可能具备更高的评分一致性。研究结果突显了人工智能代理在教育场景中的变革潜力，同时强调了对其可解释性和可信度进一步研究的必要性。
原文摘要 · Abstract (English)
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.
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