Meta-synthesis Research Supported by Large Language Model:An Agent-based Approac
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.
large language modelagentmeta-synthesiseducational research
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