将大模型与教学理论结合，让对话系统更懂学习者反思。
Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions
- 用规则框架锚定教育理论，大模型动态生成深度提问。
- 对话中学习者对目标和活动的反思更深入，但存在重复和错位问题。
- 适合教育科技研究者、智能辅导系统开发者参考。
对话系统长期用于支持学习者反思，基于理论的规则系统能提供结构化引导，但难以应对参与度变化；而大语言模型（LLMs）虽可生成情境敏感回应，却缺乏对学习互动结构的学术研究支撑，存在与教学理论脱节的风险。本文提出一种混合对话系统，将LLM的响应能力嵌入到以自我调节学习理论为基础的规则框架中，用于文化响应式机器人夏令营中的学习者反思支持。规则结构确保对话符合教育理论，而LLM则根据对话上下文决定何时及如何触发更深层次的反思。通过对对话内容的主题分析，我们发现嵌入LLM的对话在促进学习者对目标与活动的反思方面表现更优，但也因提示重复和不匹配导致参与度下降。该系统在提升反思质量的同时，也揭示了实际应用中的挑战。
原文摘要 · Abstract (English)
Dialogue systems have long supported learner reflections, with theoretically grounded, rule-based designs offering structured scaffolding but often struggling to respond to shifts in engagement. Large Language Models (LLMs), in contrast, can generate context-sensitive responses but are not informed by decades of research on how learning interactions should be structured, raising questions about their alignment with pedagogical theories. This paper presents a hybrid dialogue system that embeds LLM responsiveness within a theory-aligned, rule-based framework to support learner reflections in a culturally responsive robotics summer camp. The rule-based structure grounds dialogue in self-regulated learning theory, while the LLM decides when and how to prompt deeper reflections, responding to evolving conversation context. We analyze themes across dialogues to explore how our hybrid system shaped learner reflections. Our findings indicate that LLM-embedded dialogues supported richer learner reflections on goals and activities, but also introduced challenges due to repetitiveness and misalignment in prompts, reducing engagement.
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