AI在教育研究領域的應用系列（十六）：人工智慧代理的教育應用與ChatGPT Agent實測分析

Applications of AI in Educational Research Series (XVI): Educational Applications of Agentic Artificial Intelligence: An Empirical Analysis of ChatGPT Agent

隨著生成式人工智慧快速發展，人工智慧系統已逐步由單純的即時回應工具，轉向具備任務規劃、行動執行與反思能力之代理式人工智慧，在此趨勢下，ChatGPT Agent結合大型語言模型、外部工具介接與持續記憶機制的人工智慧代理架構，為教育研究應用帶來新的可能性。本研究針對ChatGPT Agent之系統架構、核心功能與操作流程進行實測，釐清其與傳統生成式人工智慧在設計理念與功能層級上的差異。其次，以教育研究議題「生成式人工智慧在教育上的應用」為案例，進行ChatGPT Agent之實務操作與教育應用實測分析，檢視代理式人工智慧在學習支持與研究輔助層面的潛在優勢與限制。研究結果顯示，相較於傳統對話式人工智慧，ChatGPT Agent更能展現任務連續性、策略調整與情境回應能力，對於教學與學習活動引導與教育研究流程具有高度應用潛力。最後，本研究指出其實測成效仍高度仰賴人機分工界定，以及研究者在研究歷程中的專業判斷與引導，相關研究結果可作為後續人工智慧代理於教育相關研究與實務發展之參考。

As generative artificial intelligence has become increasingly adopted, AI systems have gradually extended beyond simple response-based functions to include mechanisms for task coordination, iterative execution, and reflective processing, commonly discussed under the concept of artificial intelligence agent (AI Agent ). In this context, the ChatGPT Agent represents an implementation that combines a large language model with external tool access and memory-related functions, suggesting potential applications within educational research. This study empirically examines the system architecture, core functions, and operational procedures of the ChatGPT Agent, with the aim of clarifying how it differs from conventional generative AI systems in terms of design orientation and functional scope.

Using the educational research topic of “applications of generative artificial intelligence in education” as a case, the study further conducts a practice-based analysis of the ChatGPT Agent in an educational research context. The analysis focuses on how agent-based AI may support learning-related tasks and research activities, as well as the constraints encountered during implementation. The results indicate that, compared with traditional conversational AI, the ChatGPT Agent is better suited to handling tasks that require continuity across steps, adaptive responses, and context maintenance. Nevertheless, the effectiveness of such applications depends substantially on clear human–AI role allocation and the researcher’s ongoing judgment and instructional guidance. The findings are intended to inform subsequent studies and practical discussions regarding the use of AI agents in educational research and practice.