| 摘要: | 現行多數教育聊天機器人僅具應答功能，缺乏對學生學習歷程的整合與呈現，教師若欲掌握學生的理解狀態，需額外閱讀對話紀錄並進行分析，致使對話數據難以有效作為教學決策依據。為協助教師即時掌握學生於課程中的知識狀態，本研究提出一套結合對話知識追蹤與教材結構分析的AI輔助系統，能自動化從學生對話中推估知識掌握程度，並以結構化教材圖譜進行視覺化呈現。
本研究運用LightRAG分析教師所提供之教材內容，自動生成具知識點脈絡的知識圖譜，並結合大型語言模型（LLM）建構具階層與主題結構的心智圖，形塑作為知識追蹤基礎的教材架構。創新地將知識追蹤機制整合於教育聊天機器人的對話過程中，系統能根據教材節點自動標註學生對各知識點的掌握情況，並提供即時追蹤報告協助聊天機器人進行個人化引導，以雷達圖等多元視覺化方式，協助教師快速比較學生在各主題下的學習成效。
本研究以大學國文課程之實際教學場域為實驗對象，蒐集學生與AI助教對話資料，並將系統自動評分結果與教師人工評分進行比較。實驗結果顯示，系統於特定情境下能有效反映學生知識掌握狀態，且其評分結果與教師判斷具顯著相關性。進一步透過虛擬學生實驗驗證，即時知識追蹤機制能有效提升教育聊天機器人的引導效果，特別對被動學習風格學生的改善最為顯著。透過調整追蹤報告內容，系統成功實現「鞏固已知」與「擴展新知」兩種教學策略的應用，證實了個人化教學引導的可行性。綜合而言，本研究不僅提出一套整合教材分析、對話理解與學習歷程可視化的流程，更創新地將知識追蹤技術應用於教育聊天機器人的即時對話中，為對話式AI在智慧教育領域的應用提供具體參考。;Most educational chatbots focus solely on responding to students and lack tools for integrating and visualizing learning trajectories, leaving teachers to manually analyze conversation records to understand students′ comprehension states. This makes dialogue data difficult to effectively utilize as a basis for instructional decision-making. To help teachers promptly grasp students′ knowledge states during courses, this study proposes an AI-assisted system that combines dialogue-based knowledge tracing with instructional content analysis, capable of automatically inferring students′ knowledge mastery from dialogue interactions and presenting results through structured material graphs.
This research employs LightRAG to analyze teacher-provided instructional materials, automatically generating knowledge graphs with conceptual contexts, and combines large language models (LLMs) to construct hierarchical and thematic mind maps, forming the foundation for knowledge tracing. Innovatively integrating knowledge tracing mechanisms into the dialogue process of educational chatbots, the system can automatically annotate students′ mastery of knowledge points based on material nodes and provide real-time tracking reports to assist chatbots in personalized guidance, while using diverse visualization methods such as radar charts to help teachers quickly compare students′ learning effectiveness across different topics.
This study uses a university Chinese literature course as the experimental setting, collecting dialogue data between students and AI teaching assistants, and comparing automated system scoring with manual teacher evaluations. Experimental results show that the system effectively reflects students′ knowledge mastery states in specific contexts, with scoring results significantly correlating with teacher judgments. Further validation through virtual student experiments demonstrates that real-time knowledge tracing mechanisms can effectively enhance the guidance effectiveness of educational chatbots, with the most significant improvements observed for passive learning style students. By adjusting tracking report content, the system successfully implements two teaching strategies: "consolidating known knowledge" and "expanding new knowledge," confirming the feasibility of personalized instructional guidance. In summary, this research not only proposes an integrated workflow for material analysis, dialogue understanding, and learning trajectory visualization, but also innovatively applies knowledge tracing technology to real-time dialogue in educational chatbots, providing concrete references for conversational AI applications in smart education. |
