隨著生成式人工智慧（Generative Artificial Intelligence, Generative AI）與大型語言模型（Large Language Models, LLMs）技術的快速發展，人工智慧（Artificial Intelligence, AI）已逐漸被應用於教育情境中，為教師教學設計與學習者互動支持帶來新的發展可能性。在數位學習（Digital Learning）環境日益普及的背景下，如何運用人工智慧協助教師提升教學效率，並促進學習者主動參與與高層次思考（Higher-Order Thinking），已成為教育科技（Educational Technology）領域值得關注的重要議題。然而，在非同步線上討論（Asynchronous Online Discussion）活動中，教師需要閱讀大量學習者貼文，並依據不同學生的貼文內容提供適切回饋與引導，此過程容易造成教師回覆負擔，且難以持續掌握討論脈絡與提供高品質引導。若討論過程缺乏適當引導，學習者的回應可能停留於表層意見表達，較難進一步進行理由說明、證據檢視與反思延伸，進而影響討論深度、批判性思考態度（Critical Thinking Attitude），以及整體學習成效。因此，如何透過智慧型代理人（Intelligent Agent）輔助教師經營非同步討論板，協助教師產生具引導性與情境脈絡的回覆內容，已成為提升線上討論品質與降低教師教學負擔的重要思考方向。
本研究旨在發展一套「非同步討論板輔助經營智慧型代理人」，並探討其應用於 Moodle 課程管理平台（Moodle Course Management System）之非同步討論活動時，對於學習者討論學習成效、批判性思考態度，以及討論參與度之影響。本系統結合大型語言模型、檢索增強生成（Retrieval-Augmented Generation, RAG）技術與蘇格拉底式提問策略，以過去課程討論內容與教師上傳之相關學習資源作為生成回覆之知識基礎，協助教師依據學習者討論內容產生具引導性與情境脈絡之回覆草稿。藉此輔助教師進行非同步討論板經營，並引導學習者進一步釐清討論議題之核心概念，促成多元觀點、複雜度、探究，以及延伸思考。
本研究採準實驗研究法，以臺北市某高中一位教師及其所任教之兩班二年級學生為研究對象，並將兩個班級隨機分派為實驗組與控制組。其中，實驗組學習者為 24 人，控制組學習者為 24 人。實驗組教師採用「非同步討論板輔助經營智慧型代理人」輔助 Moodle 非同步討論板回應，控制組則使用一般 Moodle 討論板，並由教師依自身蘇格拉底式引導方式來進行回應。研究主題為「生成式 AI 下的科學素養與社會衝擊」之社會性科學議題（Socio-Scientific Issue, SSI）。
研究結果顯示，實驗組學習者在整體學習成效、複雜度，以及多元觀點面向上顯著優於控制組，而探究面向則未達顯著差異；實驗組學習者之批判性思考態度前後測分數達顯著提升，控制組則未達顯著差異；實驗組學習者在整體討論參與度、行為投入，以及認知投入上顯著高於控制組，而心理投入面向則未達顯著差異。此外，學習歷程分析結果顯示，實驗組在師生互動延續次數上顯著高於控制組。訪談結果進一步指出，實驗組學習者認為教師回覆能依據其原先回答進一步追問，使其重新檢視自身想法，並從不同角度補充與延伸觀點；教師也認為，智慧型代理人所產生之蘇格拉底式追問，有助於引導學生進行觀點延伸與持續討論。相較之下，控制組學習者雖亦能透過自我探究與閱讀同儕觀點來促進理解，但教師回饋多偏向一般性回應或延伸提問，對於引發深入且持續討論之效果較為有限。此外，教師持續使用意圖分析亦顯示，教師對於系統具有高度正向評價，認為系統有助於提升非同步討論經營效率、成效、減輕回覆負擔，並強化討論內容之深度。
綜合而言，本研究所發展之「非同步討論板輔助經營智慧型代理人」可作為教師經營非同步討論板之輔助工具，不僅有助於提升學習者的討論學習成效、批判性思考態度與討論參與度，亦能促進師生互動延續並降低教師討論經營上的引導負擔。在生成式人工智慧逐漸融入教育現場的時代背景下，本研究可作為未來線上討論教學設計、智慧型代理人系統開發，以及教師與人工智慧協作應用之參考。

With the rapid development of generative artificial intelligence (Generative AI) and large language models (LLMs), artificial intelligence (AI) has gradually been applied in educational contexts, bringing new possibilities for teachers’ instructional design and learner interaction support. As digital learning environments become increasingly widespread, using AI to help teachers improve instructional efficiency and promote learners’ active participation and higher-order thinking has become an important issue in the field of educational technology.
However, in asynchronous online discussions, teachers need to read a large number of student posts and provide appropriate feedback and guidance based on the content of different responses. This process may increase teachers’ response workload and make it difficult for them to continuously understand the discussion context and provide high-quality guidance. Without proper guidance, learners’ responses may remain at the level of surface-level opinion expression, making it difficult for them to further explain their reasoning, examine evidence, and extend their reflections. This may influence discussion depth, critical thinking attitudes, and overall learning effectiveness. Therefore, using intelligent agents to support teachers in managing asynchronous discussion boards and helping them generate guided and context-aware responses has become an important direction for improving the quality of online discussions and reducing teachers’ instructional burden.
Therefore, this study aimed to develop an “Intelligent Agent for Supporting Teachers in Managing Asynchronous Discussion Boards” and to investigate its effects on learners’ discussion-based learning effectiveness, critical thinking attitudes, and discussion engagement when applied to asynchronous discussion activities in the Moodle Course Management System. The system integrates large language models, Retrieval-Augmented Generation (RAG), and Socratic questioning strategies. By referring to previous course discussion content and learning resources uploaded by the teacher as the knowledge base for response generation, the system assists teachers in generating guided and context-aware response drafts based on learners’ discussion content. In this way, the system supports teachers in managing asynchronous discussion boards and guides learners to further clarify the core concepts of discussion topics, consider multiple perspectives, enhance the complexity of their responses, engage in inquiry, and extend their thinking.
This study adopted a quasi-experimental research method. The participants were one teacher and two classes of second-year students from a senior high school in Taipei City. The two classes were randomly assigned to an experimental group and a control group. The experimental group included 24 learners, and the control group included 24 learners. The experimental group teacher used the Intelligent Agent for Supporting Teachers in Managing Asynchronous Discussion Boards to assist with responses in the Moodle asynchronous discussion board, while the control group teacher used a general Moodle discussion board and responded to students based on the teacher’s own Socratic questioning guidance. The research topic was the socio-scientific issue (SSI) of “Scientific Literacy and Social Impacts under Generative AI.”
The results showed that, in terms of learning effectiveness, learners in the experimental group significantly outperformed those in the control group in overall learning effectiveness, complexity, and multiple perspectives, while no significant difference was found in the inquiry dimension. In terms of critical thinking attitudes, the experimental group showed a significant improvement between the pretest and posttest scores, whereas the control group did not show a significant difference. In terms of discussion engagement, the experimental group scored significantly higher than the control group in overall discussion engagement, behavioral engagement, and cognitive engagement, while no significant difference was found in psychological engagement. In addition, the learning process analysis showed that the experimental group had a significantly higher number of sustained teacher-student interaction episodes than the control group. The interview results further indicated that learners in the experimental group believed that the teacher’s responses could ask follow-up questions based on their original answers, helping them re-examine their own ideas and extend their viewpoints from different perspectives. The teacher also considered that the Socratic follow-up questions generated by the intelligent agent helped guide students to extend their viewpoints and sustain the discussion. In contrast, although learners in the control group could also promote their understanding through self-inquiry and reading peers’ viewpoints, the teacher’s feedback was mostly general responses or extended questions, which had a more limited effect on encouraging in-depth and sustained discussion. The analysis of the teacher’s continuance intention also showed that the teacher had a highly positive evaluation of the system, believing that it helped improve the efficiency and effectiveness of asynchronous discussion management, reduce response workload, and strengthen the depth of discussion content.
In conclusion, the Intelligent Agent for Supporting Teachers in Managing Asynchronous Discussion Boards developed in this study can serve as a supporting tool for teachers in managing asynchronous discussion boards. It not only helps improve learners’ discussion-based learning effectiveness, critical thinking attitudes, and discussion engagement, but also promotes sustained teacher-student interaction and reduces teachers’ burden in discussion guidance. In the context of generative AI being increasingly integrated into educational practice, the results of this study can serve as a reference for future online discussion instructional design, intelligent agent system development, and applications of teacher-AI collaboration.

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