EducationQ：通过多智能体对话框架评估大语言模型的教学能力
EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework
AI总结：
本研究提出多智能体对话框架EducationQ以评估LLM教学能力，测试14款模型后发现教学效果与模型规模无线性相关，验证了混合评估方法的有效性，指出教育AI需针对性优化教学能力。
摘要：
大语言模型（LLM）正越来越多地被用作教育工具，但由于师生互动具有资源密集、依赖语境且方法学复杂的特性，评估其教学能力仍颇具挑战。我们提出了EducationQ，这是一个多智能体对话框架，通过模拟动态教育场景高效评估教学能力，框架内设有专门负责教学、学习和评估的智能体。我们对来自主流AI机构（OpenAI、Meta、Google、Anthropic等）的14个LLM进行了测试，测试涵盖13个学科、10个难度等级的1498道题目。结果显示，教学效果与模型规模或通用推理能力并非线性相关——部分规模较小的开源模型在教学场景中的表现优于规模更大的商业模型。这一发现凸显了当前评估体系的一个关键缺陷：现有评估更侧重知识回忆，而非互动式教学法。我们采用混合方法评估，结合量化指标、定性分析与专家案例研究，识别出了表现最优的模型所具备的独特教学优势（例如精妙的提问策略、自适应反馈机制）。人类专家评估与我们针对有效教学行为的自动定性分析的一致性达78%，验证了该方法的有效性。EducationQ表明，作为教师的LLM需要超越简单规模扩张的专项优化，这提示下一代教育AI应优先针对性提升特定教学效能。
英文摘要：
Large language models (LLMs) increasingly serve as educational tools, yet evaluating their teaching capabilities remains challenging due to the resource-intensive, context-dependent, and methodologically complex nature of teacher-student interactions. We introduce EducationQ, a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios, featuring specialized agents for teaching, learning, and evaluation. Testing 14 LLMs across major AI Organizations (OpenAI, Meta, Google, Anthropic, and others) on 1,498 questions spanning 13 disciplines and 10 difficulty levels reveals that teaching effectiveness does not correlate linearly with model scale or general reasoning capabilities - with some smaller open-source models outperforming larger commercial counterparts in teaching contexts. This finding highlights a critical gap in current evaluations that prioritize knowledge recall over interactive pedagogy. Our mixed-methods evaluation, combining quantitative metrics with qualitative analysis and expert case studies, identifies distinct pedagogical strengths employed by top-performing models (e.g., sophisticated questioning strategies, adaptive feedback mechanisms). Human expert evaluations show 78% agreement with our automated qualitative analysis of effective teaching behaviors, validating our methodology. EducationQ demonstrates that LLMs-as-teachers require specialized optimization beyond simple scaling, suggesting next-generation educational AI prioritize targeted enhancement of specific pedagogical effectiveness.
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