分析聊天机器人对话模式，发现高效学习靠主动提问和及时反馈
Dialogue Act Patterns in GenAI-Mediated L2 Oral Practice: A Sequential Analysis of Learner-Chatbot Interactions
- 用教育导向框架标注学生与AI聊天的对话行为
- 进步快的学生更常主动提问，反馈时机更关键
- 适合想优化AI语言学习工具的研究者和开发者
生成式AI语音聊天机器人为第二语言（L2）口语练习提供了可扩展的机会，但学习者成效相关的互动过程仍缺乏研究。本研究通过10周干预，分析了12名九年级中国英语学习者与GenAI语音聊天机器人之间的70次会话，共标注6,957个对话行为（DA）。采用教育导向编码方案，对比高进展与低进展会话中的对话行为分布与序列模式。结果显示：高进展会话中学习者主动提问更多，而低进展会话中澄清请求频率更高，表明理解难度更大；在序列层面，高进展会话中以提示为基础的纠正性反馈出现更频繁，且稳定出现在学习者回应之后，凸显反馈类型与时机对有效互动的关键作用。研究强调对话视角对生成式AI聊天机器人设计的价值，提出一个教育导向的对话行为编码框架，并为自适应语言学习AI系统的设计提供依据。
原文摘要 · Abstract (English)
While generative AI (GenAI) voice chatbots offer scalable opportunities for second language (L2) oral practice, the interactional processes related to learners' gains remain underexplored. This study investigates dialogue act (DA) patterns in interactions between Grade 9 Chinese English as a foreign language (EFL) learners and a GenAI voice chatbot over a 10-week intervention. Seventy sessions from 12 students were annotated by human coders using a pedagogy-informed coding scheme, yielding 6,957 coded DAs. DA distributions and sequential patterns were compared between high- and low-progress sessions. At the DA level, high-progress sessions showed more learner-initiated questions, whereas low-progress sessions exhibited higher rates of clarification-seeking, indicating greater comprehension difficulty. At the sequential level, high-progress sessions were characterised by more frequent prompting-based corrective feedback sequences, consistently positioned after learner responses, highlighting the role of feedback type and timing in effective interaction. Overall, these findings underscore the value of a dialogic lens in GenAI chatbot design, contribute a pedagogy-informed DA coding framework, and inform the design of adaptive GenAI chatbots for L2 education.
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