【摘要】 大语言模型凭借强大的分析与推理能力正在变革教育研究范式，特别是其在智能体技术方面取得的显著进步，为系统性解决科研领域的复杂问题提供了有力支持。基于此，文章聚焦元综合这一典型的研究任务场景，探讨如何基于智能体的方法提供更加系统化的支持：首先，文章提出了多步骤规划、协同模式构建、提示赋能、工具集成的元综合智能体应用原则，设计了由6个智能体协同的应用模式，并基于该模式开发了元综合智能体工具。然后，文章通过案例研究将智能体工具应用于典型的元综合任务，发现与人类团队相比，智能体更能遵照元综合研究流程执行任务，生成结果更全面；人类团队在应用过程中对智能体的准确性和使用体验给予了积极评价。最后，文章基于研究发现提出了智能体在教育研究中的应用策略，以期为深入解决教育研究实践问题提供新的人机协同思路。更多还原

【Abstract】 With their robust analytical and inferential capabilities, large language model(LLM) are transforming educational research paradigms, particularly made significant advancements in agent technology, which provides strong support for systematically solving complex problems in the scientific research field. Based on this, the paper focused on the typical research task scenarios of meta-synthesis, and discussed how to provide more systematic support with an agent-based approach. Firstly, this paper introduced the application principles for meta-synthesis agent application, including multi-step planning, collaborative mode construction, prompt empowerment, and tool integration, designed an application mode involving the coordinated efforts of six agents, as well as developed a meta-synthesis agent tool based on this mode. Then,the agent tool was applied to typical meta-synthesis tasks through case studies. It was found that compared to human teams,the agent can perform the task in accordance with the meta-synthesis research process better and generate more comprehensive results. Meanwhile, human teams gave positive evaluation on the accuracy and user experience of the agent during the application process. Finally, based on the research findings, this paper put forward the application strategy of the agents in educational research, in order to offer a new insight of man-machine collaboration for solving the practical problems of educational research.更多还原