Dialogue Act Patterns in GenAI-Mediated L2 Oral Practice: A Sequential Analysis of Learner-Chatbot Interactions

TL;DR

This study analyzes learner-chatbot interactions over 10 weeks, revealing how dialogue patterns relate to learner progress and informing better GenAI chatbot design for second language practice.

Contribution

It introduces a pedagogy-informed dialogue act coding scheme and identifies key interaction patterns linked to learner success in GenAI-mediated L2 practice.

Findings

High-progress sessions had more learner-initiated questions.

Low-progress sessions showed more clarification-seeking behavior.

Prompting-based corrective feedback was more frequent after learner responses.

Abstract

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.