用多智能体自动适配课件，让教师专注教学创意。
Addressing Situated Teaching Needs: A Multi-Agent Framework for Automated Slide Adaptation
- 设计多智能体框架，根据教师指令自动调整课件内容。
- 在8门真实课程中测试，关键指标F1达0.89，效果接近人工。
- 适合希望节省备课时间的教育工作者和AI辅助教学研究者。
将教学幻灯片适配到教师的具体教学需求（如教学风格、学生背景）是教育者面临的重要但耗时的任务。通过访谈多位教师，我们系统识别并分类了阻碍这一过程的关键痛点。基于这些发现，提出一种新型多智能体框架，可根据教师的高层次指令自动完成课件调整。在8门真实课程中对16项修改请求进行评估，结果表明该框架在意图契合度、内容连贯性和事实准确性方面表现优异，视觉清晰度与基线方法相当，且在及时性和专家一致性上表现良好，达成0.89的F1分数。本工作标志着一种新范式：由AI代理承担教学设计的琐碎任务，使教师能专注于教学创新与战略规划。
原文摘要 · Abstract (English)
The adaptation of teaching slides to instructors' situated teaching needs, including pedagogical styles and their students' context, is a critical yet time-consuming task for educators. Through a series of educator interviews, we first identify and systematically categorize the key friction points that impede this adaptation process. Grounded in these findings, we introduce a novel multi-agent framework designed to automate slide adaptation based on high-level instructor specifications. An evaluation involving 16 modification requests across 8 real-world courses validates our approach. The framework's output consistently achieved high scores in intent alignment, content coherence and factual accuracy, and performed on par with baseline methods regarding visual clarity, while also demonstrating appropriate timeliness and a high operational agreement with human experts, achieving an F1 score of 0.89. This work heralds a new paradigm where AI agents handle the logistical burdens of instructional design, liberating educators to focus on the creative and strategic aspects of teaching.
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