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.

翻译：有效的个性化AI辅助学习要求系统不仅能生成针对学习者的精准教育材料，还能动态调整教学方式以适应不同学习者。然而，现有教育智能体主要聚焦于讲座内容自动化与模拟，往往缺乏对多模态和具身化教学方法的建模能力，难以实现针对个体学习者的定制化教学。为此，我们提出LectūraAgents——一种通过端到端自适应具身教学实现个性化学习的多智能体框架。该框架的核心机制模拟教授-学生关系，其中教授智能体(ProfessorAgent)领导由专业子智能体组成的协作团队，通过研究、规划、审查和具身化授课等环节，生成适应学习者需求的讲座内容。本框架的三大贡献在于：(1)面向端到端个性化学习的层级化多智能体架构；(2)自适应具身教学机制，教授智能体在教学环境中对内容执行可视化且具教学动机的动作（如手写、高亮、下划线等）；(3)教学动作-语音对齐算法(TASA)，通过基于显著性的启发式策略与时间语义分割，生成与学习者画像一致的教学动作序列。我们通过基于样本特定评分标准的多维度分析，在高中、本科及研究生三个层级的多样化课程上评估LectūraAgents，并由教育专家对其生成的讲座材料与教学动作进行验证。实验结果表明，与现有方法相比，本框架在讲座内容质量、具身教学质量、教学评估效果和个性化水平方面均取得持续提升，为大规模个性化学习提供了坚实的教学理论基础。