LectūraAgents：一個自適應個性化AI輔助學習與具身教學的多智能體框架

摘要

有效的個性化AI輔助學習需要系統不僅能生成精確的學習者專屬教材，還能針對不同學習者動態調整教學方式。然而，現有的教育代理主要側重於講座內容自動化與模擬，往往難以建模針對個別學習者量身定制的多模態與具身化教學方法。為此，我們提出LectūraAgents——一個透過端到端自適應具身化教學實現個性化學習的多代理框架。其核心在於模擬教授與學生的關係，由教授代理（ProfessorAgent）領導一個由專門下屬代理組成的協作團隊，透過研究、規劃、審查與具身化遞送適應學習者需求的講座內容。該框架包含三項主要貢獻：（1）用於端到端個性化學習的層級式多代理架構；（2）自適應具身化教學機制，其中教授代理在教學環境中對內容執行可見且具有教學動機的教學動作（例如手寫、標記重點、劃底線等）；（3）教學動作-語音對齊（TASA）演算法，該演算法採用凸顯性啟發式與時間語義分割，生成與學習者檔案一致且連貫的教學動作序列。我們在高中、大學本科與研究生層級的多樣課程上，使用樣本特定評分量規分析評估LectūraAgents；生成的講座教材與教學動作由教育專家評估與驗證。實驗結果顯示，相較於現有方法，該框架在講座內容品質、具身化教學品質、評量與個性化方面均取得一致提升，使LectūraAgents成為一個在教學法上具備堅實基礎、可規模化實現個性化學習的框架。

English

Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but also dynamically adapt their instruction to diverse learners. However, existing educational agents have primarily focused on lecture content automation and simulations, which often fall short of modelling multimodal and embodied instructional methods tailored for the individual learner. To this end, we propose LectūraAgents - a multi-agent framework that enables personalized learning through end-to-end adaptive embodied teaching. At its core, LectūraAgents mirrors a professor-student relationship, in which a ProfessorAgent leads a collaborative team of specialized subordinate agents through research, planning, review, and embodied delivery of lecture contents that adapt to a learner's needs. The framework offers three main contributions: (1) a hierarchical multi-agent architecture for end-to-end personalized learning; (2) an adaptive embodied teaching mechanism, wherein the ProfessorAgent executes visible and pedagogically motivated teaching actions (e.g., handwrite, highlight, underline, etc.) over contents in a teaching environment; and (3) a Teaching Action-Speech Alignment (TASA) algorithm that employs salience-based heuristics and temporal semantic segmentation to generate coherent teaching action sequences aligned with learner profiles. We evaluate LectūraAgents on diverse courses at high school, undergraduate, and graduate levels using sample-specific rubric-based analysis; with generated lecture materials and teaching actions assessed and validated by expert educators. Experimental results show consistent gains in lecture content quality, embodied teaching quality, assessment, and personalization over existing approaches, positioning LectūraAgents as a pedagogically well-grounded framework for personalized learning at scale.