AI教授能看见你：它边讲边写边画，专为你调整
现在的AI教学助手大多是“念PPT”，不会根据你的反应调整板书、手势或语速。这篇论文让AI像真人教授一样，在讲课的同时能做出“手写、高亮、下划线”等可见的教学动作，并且这些动作会随着你的学习进度和风格实时变化。核心是一个多智能体系统：一个“教授智能体”带领一群助手，先研究你的需求，再规划课程，最后在虚拟黑板上边讲边写，动作和台词严格对齐。实验覆盖高中到研究生课程，专家评估显示其教学质量和个性化程度明显优于现有方案。它不是你明天就能用的产品，但指明了AI教育从“放录音”走向“真互动”的方向。
📄 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.