超越怀疑：用自适应教学警觉框架评估大语言模型的教学意图推理
Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework
AI总结：
提出自适应教学警觉（APV）框架，通过贝叶斯教学意图推理引擎建模教学选择与学习者推理，显著提升LLM区分教学与暴露内容的能力，与人类判断高度相关。
摘要：
大语言模型（LLM）在教学交流中推理教学意图的能力仍未得到充分探索，尤其是在翻译教学等教育领域。为此，我们提出了\textbf{自适应教学警觉（APV）}框架，这是一种新颖的计算形式化方法，将交流警觉重新定义为通过意图推理优化学习的自适应机制。APV通过贝叶斯教学意图推理引擎（PIIE）形式化该问题，该引擎模拟教师如何选择内容以最大化教学效用，以及警觉的学习者应如何逆向推理潜在的教学配置——包括体裁、立场和动机。我们通过三层层级评估APV：区分教学体裁、推理结构化教学设置以及泛化到真实教育话语。在领先的LLM（如GPT-4o、Claude 3.5）上的实验表明，APV显著提高了模型的警觉性。它在教学内容和暴露内容之间实现了最强的区分，与人类判断高度相关（$r=0.958$），并在基线方法退化的自然数据上保持稳健性能。这项工作建立了一个统一的框架来评估和增强LLM对教学动机的理解，推动了更可靠的AI辅助学习系统的发展。
英文摘要：
The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly in educational domains such as translation pedagogy. To address this, we propose the \textbf{Adaptive Pedagogical Vigilance (APV)} framework, a novel computational formalism that reframes communicative vigilance as an adaptive mechanism for optimizing learning through intent inference. APV formalizes the problem via a Bayesian Pedagogical Intent Inference Engine (PIIE), which models how instructors select content to maximize pedagogical utility and how vigilant learners should inversely reason about latent instructional configurations -- encompassing genre, stance, and incentives. We evaluate APV through a three-tier hierarchy: distinguishing instructional genre, reasoning about structured pedagogical setups, and generalizing to authentic educational discourse. Experiments on leading LLMs (e.g., GPT-4o, Claude 3.5) show that APV substantially improves model vigilance. It achieves the strongest discrimination between pedagogical and exposure-based content, correlates highly with human judgments ($r=0.958$), and maintains robust performance on naturalistic data where baseline methods degrade. This work establishes a unified framework for assessing and enhancing LLMs' understanding of pedagogical motives, advancing the development of more reliable AI-assisted learning systems.
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