LectūraAgents：一种多智能体框架，旨在实现自适应化的个性化AI辅助学习和基于实体的教学体验。
LectūraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching
摘要
有效的、基于个性化算法的AI辅助学习系统需要具备以下能力：能够生成针对特定学习者的精准教育材料，同时还能根据学习者的不同需求动态调整教学策略。不过，现有的教育系统中，大多数都只专注于课程内容的自动化处理或模拟演练，这些方式往往难以模拟出适合个体学习者的多样化教学方法。为此，我们提出了LectūraAgents这一多智能体框架——它通过端到端的自适应教学机制，实现个性化的学习体验。该框架的核心原理类似于教授与学生的关系：由ProfessorAgent带领一组专业智能体共同负责课程的研发、规划、审核以及教学内容的呈现，这些内容都会根据学生的学习需求进行调整。该框架主要有三个创新点：(1) 采用分层多智能体架构，实现端到端的个性化学习；(2) 具备自适应教学机制，ProfessorAgent可以在教学环境中执行各种教学行为，如手写注释、高亮显示等；(3) 引入了Teaching Action-Speech Alignment算法，该算法利用显著性启发式方法和时间语义分割技术，生成与学习者特征相匹配的教学行为序列。我们在高中、本科和研究生阶段的不同课程上对该框架进行了测试，并通过针对具体样本的评估标准对教学效果进行了验证。实验结果表明，与现有方法相比，LectūraAgents在课程内容质量、教学效果以及个性化程度方面都有显著提升，因此该框架无疑是一种具有良好教育效果的个性化学习解决方案。
English Abstract
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.