AI教授学会看人下菜碟：边讲边写边比划
现在的AI教学系统大多只会念稿子，或者生成一堆文字材料，根本不会像真人老师那样边讲边写、高亮、画线。这篇论文搞了个多智能体框架，让一个「教授智能体」带着一群助手，先研究学生情况，再规划讲课内容，最后在虚拟黑板上一边讲一边做手势——比如写板书、圈重点、画下划线，而且这些动作和说话内容是同步对齐的。实验请了真老师打分，说它比现有系统更像真人教学。它不是你明天就能用上的，但方向很明确：AI教学的下一个战场，不是内容多准确，而是教得有多像人。
📄 原文摘要(英文)
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.