混合LLM嵌入对话代理用于学习者反思：设计响应性且理论驱动的互动
Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions
AI总结：
本文提出一种混合对话系统，结合LLM的响应能力与理论驱动的规则框架，以支持学习者反思，但发现其在提高反思深度的同时也带来了一些挑战。
摘要：
对话系统长期以来支持学习者的反思，具有理论基础的规则基于设计提供了结构化的支架，但往往难以应对参与度的变化。相比之下，大型语言模型（LLMs）可以生成上下文敏感的响应，但并未受到数十年来关于学习互动应如何结构化的研究的影响，这引发了关于其与教学理论的一致性问题。本文提出了一种混合对话系统，该系统将LLM的响应能力嵌入到理论对齐的规则基础上的框架中，以支持文化敏感的机器人暑期营中的学习者反思。规则基础的结构使对话扎根于自我调节学习理论，而LLM则决定何时以及如何提示更深入的反思，以响应不断演变的对话上下文。我们分析了对话中的主题，以探讨我们的混合系统如何塑造学习者的反思。我们的发现表明，嵌入LLM的对话支持了更丰富的学习者对目标和活动的反思，但也引入了由于重复性和提示不一致而减少参与度的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
