用教育学理论指导AI对话，自动识别并生成教学策略。
PedagoSense: A Pedology Grounded LLM System for Pedagogical Strategy Detection and Contextual Response Generation in Learning Dialogues
- 分两阶段检测教学策略，先判断有无，再细分类别。
- 数据增强提升检测性能，生成响应与策略高度一致。
- 适合教育AI开发者，推动自适应学习系统落地。
本文针对对话式学习中互动质量提升的挑战，提出PedagoSense系统，通过检测并推荐有效的教学策略来优化师生对话。该系统结合两阶段策略分类器与大语言模型生成能力：首先使用二分类器判断是否存在教学策略，随后进行细粒度分类以识别具体策略类型；同时根据对话上下文推荐合适策略，并由LLM生成符合该策略的回应。在人工标注的师生对话数据集上评估，额外引入非教学性对话用于二分类任务的数据增强。结果表明，策略检测性能优异，数据增强带来稳定提升；分析显示细粒度类别仍存在挑战。整体上，PedagoSense实现了教育理论与基于LLM响应生成的融合，推动更自适应的教育技术发展。
原文摘要 · Abstract (English)
This paper addresses the challenge of improving interaction quality in dialogue based learning by detecting and recommending effective pedagogical strategies in tutor student conversations. We introduce PedagoSense, a pedology grounded system that combines a two stage strategy classifier with large language model generation. The system first detects whether a pedagogical strategy is present using a binary classifier, then performs fine grained classification to identify the specific strategy. In parallel, it recommends an appropriate strategy from the dialogue context and uses an LLM to generate a response aligned with that strategy. We evaluate on human annotated tutor student dialogues, augmented with additional non pedagogical conversations for the binary task. Results show high performance for pedagogical strategy detection and consistent gains when using data augmentation, while analysis highlights where fine grained classes remain challenging. Overall, PedagoSense bridges pedagogical theory and practical LLM based response generation for more adaptive educational technologies.
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