隨著大型語言模型與自主代理人技術的快速發展，人工智慧的應用已逐漸由單一模型的預測，發展為具備情境理解、推理與決策支持能力的代理人系統。於學習場域中，智慧學習代理人亦被期待扮演智慧導師的角色，依據學習者的個別狀態提供即時且適性化的學習支持。然而，現有智慧學習代理人在掌握學習者於學習歷程中的動態狀態，以及依據學生個別情況進行自主推理與適性化輔導等方面，仍有發展空間。多數系統仍偏重於學習結果的評量，較缺乏對學習歷程中學習狀態的持續感知與即時回應；既有學習狀態辨識方法多依賴靜態特徵，較難掌握學習狀態隨時間演變的動態歷程；此外，多數代理人的輔導仍以固定規則或預設提示為主，缺乏依據學習情境進行推理與自主決策的能力。
為回應上述問題，本研究提出一具備時序狀態感知與自主推理能力之智慧導師代理人，以提供持續性的學習觀察與即時適性化輔導。於學習狀態感知方面，本研究改造 Time-LLM 架構，融合學生學習歷程中的時序情緒特徵，以及人格特質與先備能力等靜態特徵，以辨識學習情意、學習態度及步驟性學習成效，並分析各項特徵對不同學習狀態之影響方向與程度。於自主推理與輔導決策方面，本研究整合輔導策略知識圖譜、檢索增強生成（Retrieval-Augmented Generation, RAG）技術及大型語言模型，使代理人能依據辨識結果推理學生狀態、檢索候選策略、形成個別化輔導決策，並生成適性化回饋。此外，本研究設計全域策略權重與個人策略權重機制，使代理人能依據學生接受輔導後的狀態變化，持續調整策略選擇，以提升輔導決策的適應性。
本研究將所提出之智慧導師代理人整合至跨領域閱讀學習平台，應用於國小自然科學閱讀課程。研究首先比較所提出之時序狀態感知機制與多種基準方法於學習狀態辨識之表現；其次，透過系統運作紀錄與推理案例分析，檢視代理人自主推理與輔導決策之運作流程；最後，以49名國小學生進行準實驗，其中實驗組25人使用整合智慧導師代理人之學習平台，對照組24人採原有教學方式，以評估其對學習狀態、自然科學學習表現及系統接受度之影響。
研究結果顯示，本研究提出之時序狀態感知機制於學習情意、學習態度及步驟性學習成效三項辨識任務之表現，皆優於線性迴歸、支援向量迴歸、XGBoost及隨機森林等基準方法。系統運作紀錄與推理案例分析亦顯示，代理人能依據學生學習狀態辨識結果，結合知識圖譜完成學生狀態推理、候選策略檢索、策略選擇及個別化回饋生成。準實驗結果顯示，實驗組學生之學習情意與步驟性學習成效均呈現顯著提升，且在控制先前學習表現後，其自然科學學習表現顯著優於對照組。此外，學生對系統之知覺有用性與知覺易用性亦皆給予高度正向評價。
綜合而言，本研究所提出之智慧導師代理人具備持續感知學生學習狀態、自主推理及個別化輔導決策之能力，可於數位跨領域閱讀學習情境中提供適性化學習支持，初步驗證時序狀態感知與自主推理機制應用於智慧導師代理人之可行性，以及其於智慧教育之應用潛力。

This study developed an intelligent tutoring agent with temporal state awareness and autonomous reasoning for online cross-disciplinary reading in elementary science. The agent continuously recognized learning affect, learning attitude, and step-level learning performance; detected states requiring attention; retrieved tutoring strategies from a knowledge graph; and generated context-sensitive feedback. Its recognition performance, reasoning process, effects in an authentic learning setting, and student perceptions were evaluated. Results showed that the proposed mechanism outperformed four traditional baseline methods across the three recognition tasks. System records supported the operational feasibility and traceability of the reasoning process. Significant positive changes were found in learning affect and step-level learning performance after tutoring interventions, and students generally perceived the agent-integrated platform as useful and easy to use.

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