Shijun | Searcharxiv

arXiv2026

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

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.

cs.CL↗

arXiv2025

Synergizing Self-Regulation and Artificial-Intelligence Literacy Towards Future Human-AI Integrative Learning

Self-regulated learning (SRL) and Artificial-Intelligence (AI) literacy are becoming key competencies for successful human-AI interactive learning, vital to future education. However, despite their importance, students face imbalanced and underdeveloped SRL and AI literacy capabilities, inhibiting effective using AI for learning. This study analyzed data from 1,704 Chinese undergraduates using clustering methods to uncover four learner groups reflecting developing process(Potential, Development, Master, and AI-Inclined) characterized by varying SRL and AI literacy differentiation. Results highlight obvious disparities in SRL and AI literacy synchronization, with the Master Group achieving balanced development and critical AI-using for SRL, while AI-Inclined Group demonstrate over-reliance on AI and poor SRL application. The Potential Group showed a close mutual promotion trend between SRL and AI literacy, while the Development Group showed a discrete correlation. Resources and instructional guidance support emerged as key factors affecting these differentiations. To translate students to master SRL-AI literacy level and progress within it, the study proposes differentiated support strategies and suggestions. Synergizing SRL and AI literacy growth is the core of development, ensuring equitable and advanced human-centered interactive learning models for future human-AI integrating.

cs.CY↗