发表机构
Academic Center for Computing and Media Studies, Kyoto University, Japan; College of Information Science and Engineering, Ritsumeikan University, Japan（京都大学计算与媒体研究室; 立命馆大学信息科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究借助转换网络分析，以日本外语学习者与写作聊天机器人“佩妮”的交互为对象，发现两个行为循环，揭示外语水平对交互的显著影响，表明人工智能辅助写作是对话过程，强调需差异化设计聊天机器人以促进学习者认知参与。
AI 中文摘要
生成式人工智能聊天机器人有望通过提供即时、个性化反馈来改变外语写作。但其教学价值取决于学习者如何与之互动，而这一过程常被视为“黑匣子”。本研究用转换网络分析为使用由大型语言模型驱动的写作聊天机器人“佩妮”的日本外语学习者的时间动态建模。对4500多个写作会话和21000次聊天机器人交互的分析揭示了两个主要行为循环：“修订循环”，反馈直接导致成功纠错；“聊天循环”，学习者在反馈后与聊天机器人进行持续对话。关键的是，外语水平显著影响交互：高水平学习者更多地与聊天机器人进行开放对话和协商，而低水平学习者更依赖重复的纠正反馈循环。研究结果表明，人工智能辅助写作是一个非线性的对话过程，并强调需要差异化的聊天机器人设计，以超越简单纠错，促进所有学习者更深层次的认知参与。
英文摘要
Generative AI chatbots promise to transform English as a Foreign Language (EFL) writing by providing immediate, personalised feedback. However, their pedagogical value depends on how learners engage with them - a process often treated as a "black box." This study uses Transition Network Analysis to model the temporal dynamics of Japanese EFL learners using "Penny," an LLM-powered writing chatbot. Analysis of over 4,500 writing sessions and 21,000 chatbot interactions reveals two dominant behavioural loops: a "Revision Loop," where feedback leads directly to successful error correction, and a "Chat Loop," where learners engage in sustained dialogue with the chatbot following feedback. Crucially, EFL proficiency significantly shapes interaction: high-proficiency learners engage more in open dialogue and negotiation with the chatbot, while low-proficiency learners rely more heavily on repetitive corrective feedback cycles. The findings demonstrate that AI-scaffolded writing is a non-linear, dialogic process and highlight the need for differentiated chatbot design to move beyond simple error correction and foster deeper cognitive engagement for all learners.
DOI：10.1007/978-3-032-34157-0_13
