分析聊天机器人与日语学习者互动，发现高阶学习者更爱对话式交流。
Penny: Transition Network Analysis of Learner-Chatbot Interactions in Scaffolded EFL Writing
- 用动态网络分析追踪4500次写作会话中的交互模式
- 发现纠错循环与持续对话是两大核心行为路径
- 低水平学习者依赖重复纠错，高水平则倾向深度对话
生成式AI聊天机器人有望革新英语作为外语（EFL）写作教学，提供即时个性化反馈。然而其教学价值取决于学习者如何与之互动——这一过程常被视为“黑箱”。本研究采用过渡网络分析（Transition Network Analysis），建模日本EFL学习者使用“Penny”（一个基于大语言模型的写作聊天机器人）的交互时序动态。分析超过4,500次写作会话和21,000次聊天机器人交互后发现，存在两种主导行为循环：一种是“修订循环”，反馈直接导致错误修正；另一种是“对话循环”，学习者在收到反馈后持续与聊天机器人展开对话。关键发现是：学习者英语水平显著影响互动方式——高水平学习者更倾向于开放性对话与协商，而低水平学习者则更依赖重复性的纠正反馈循环。研究揭示了人工智能支持的写作是一个非线性、对话式的复杂过程，强调需根据不同水平设计差异化聊天机器人，超越简单纠错，促进所有学习者的深层认知参与。
原文摘要 · Abstract (English)
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.
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