墨迹与算法：探索人机协同写作中的时间动态
Ink and Algorithm: Exploring Temporal Dynamics in Human-AI Collaborative Writing
AI总结：
本研究针对人机协同写作中交互动态与学习支持机制不明的问题，采用动态时间规整聚类和认知网络分析，识别出四种AI使用时间模式并揭示其与写作认知行为的关联，为教育场景下的GAI学习支持设计提供依据。
摘要：
生成式人工智能（GAI）的出现彻底变革了写作领域，推动教育场景下的写作向人机协同写作模式转变。然而，目前学界对协同写作过程中人机交互的动态机制尚缺乏充分理解，因此如何借助这类前沿GAI技术有效支持人类学习仍在很大程度上未被探明。\n本研究旨在填补这一空白，通过探究人类在协同写作中如何使用GAI，考察GAI使用模式与人类写作行为之间的相互作用。考虑到人们对GAI的依赖程度可能存在差异，我们提出采用动态时间规整（Dynamic Time Warping）时间序列聚类方法，识别并分析人机协同写作过程中AI使用的常见时间模式。此外，我们引入认知网络分析（Epistemic Network Analysis），揭示GAI使用与反映认知过程的人类写作行为（即知识讲述、知识转化和认知存在）之间的关联，以期为开发更优的方法与工具提供洞见，支持人类通过这类人机协同写作活动实现有效学习。\n研究结果揭示了四种主要的差异化AI使用时间模式，并凸显了这些模式与人类写作行为之间的显著相关性。这些发现对在教育写作任务中借助GAI有效支持人类学习具有重要启示意义。
英文摘要：
The advent of Generative Artificial Intelligence (GAI) has revolutionized the field of writing, marking a shift towards human-AI collaborative writing in education. However, the dynamics of human-AI interaction in the collaborative writing process are not well understood, and thus it remains largely unknown how human learning can be effectively supported with such cutting-edge GAI technologies. In this study, we aim to bridge this gap by investigating how humans employ GAI in collaborative writing and examining the interplay between the patterns of GAI usage and human writing behaviors. Considering the potential varying degrees to which people rely on GAI usage, we proposed to use Dynamic Time Warping time-series clustering for the identification and analysis of common temporal patterns in AI usage during the human-AI collaborative writing processes. Additionally, we incorporated Epistemic Network Analysis to reveal the correlation between GAI usage and human writing behaviors that reflect cognitive processes (i.e., knowledge telling, knowledge transformation, and cognitive presence), aiming to offer insights for developing better approaches and tools to support human to learn effectively via such human-AI collaborative writing activities. Our findings reveal four major distinct temporal patterns in AI utilization and highlight significant correlations between these patterns and human writing behaviors. These findings have significant implications for effectively supporting human learning with GAI in educational writing tasks.
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。
