LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks. However, LLMs have not yet been used to characterize synergistic learning in students' collaborative discourse. In this exploratory work, we take a first step towards adopting a human-in-the-loop prompt engineering approach with GPT-4-Turbo to summarize and categorize students' synergistic learning during collaborative discourse. Our preliminary findings suggest GPT-4-Turbo may be able to characterize students' synergistic learning in a manner comparable to humans and that our approach warrants further investigation.

翻译：大语言模型（LLM）已展现出利用人类输入对输出进行情境化的能力，在多项任务中已接近或超越人类水平表现。然而，当前尚未有研究将LLM应用于表征学生协作话语中的协同学习特征。本探索性研究首次采用基于人机协同的提示工程方法，利用GPT-4-Turbo对学生协作话语中的协同学习行为进行总结与分类。初步结果表明，GPT-4-Turbo能够以与人类相当的方式表征学生的协同学习特征，该方法值得进一步深入研究。