Asynchronous, text-based discourse-such as students' posts in discussion forums-is widely used to support collaborative learning. However, the distributed and evolving nature of such discourse often makes it difficult to see how ideas connect, develop, and build on one another over time. As a result, learners may struggle to recognize relationships among ideas-a process that is critical for idea advancement in productive collaborative discourse. To address this challenge, we explore how large language models (LLMs) can provide representational guidance by modeling student discourse as a Knowledge Synthesis Graph (KSG). The KSG identifies ideas from student discourse and visualizes their epistemic relationships, externalizing the current state of collaborative knowledge in a form that can support further inquiry and idea advancement. In this study, we present the design of the KSG and evaluate the LLM-based approach for constructing KSGs from authentic student discourse data. Through multi-round human-expert coding and prompt iteration, our results demonstrate the feasibility of using our approach to construct reliable KSGs across different models. This work provides a technical foundation for modeling collaborative discourse with LLMs and offers pedagogical implications for augmenting complex knowledge work in collaborative learning environments.

翻译：异步、文本化的话语——例如学生在讨论论坛中的发帖——被广泛用于支持协作学习。然而，此类话语的分布式和动态演进特性，常常使得我们难以看清观点如何随时间推移相互连接、发展和构建。因此，学习者可能难以识别观点之间的关系——这一过程对于富有成效的协作话语中的观点推进至关重要。为应对这一挑战，我们探索了大型语言模型如何通过将学生话语建模为知识合成图来提供表征性指导。KSG从学生话语中识别观点，并将其认知关系可视化，从而以能够支持进一步探究和观点推进的形式，外化协作知识的当前状态。在本研究中，我们介绍了KSG的设计，并评估了基于LLM的方法从真实学生话语数据构建KSG的效果。通过多轮人类专家编码和提示迭代，我们的结果证明了使用我们的方法在不同模型间构建可靠KSG的可行性。这项工作为使用LLM建模协作话语提供了技术基础，并为增强协作学习环境中的复杂知识工作提供了教学启示。