How Generative AI Reshapes Classroom Dialogue in Chinese Higher Education: A Mixed-Method Study of AI-Enhanced Dialogic Teaching
DOI:
https://doi.org/10.60072/ijeissah.2026.v4i04.001Abstract
This study examines how generative artificial intelligence (GenAI) changes classroom dialogue in Chinese higher education, using a mixed-method design informed by dialogic teaching theory. Whereas earlier research has concentrated on AI adoption and user perceptions, this study treats GenAI as a structural mediator of classroom dialogue, one that shapes instructional design, interaction processes, and student engagement. Data were gathered at two Chinese universities and include 12 undergraduate courses, 10 teacher interviews, 24 classroom observations, and 221 student questionnaires. The quantitative results show significant increases in classroom participation (+22.2%), contribution frequency (p < 0.001), and higher-order cognitive responses, although overall learning engagement stays relatively stable. The qualitative findings point to four interrelated mechanisms by which GenAI supports dialogic teaching: AI-assisted lesson design, AI-supported questioning sequences, AI-driven personalized learning support, and AI-facilitated collaboration. These mechanisms correspond to, and extend, Alexander’s principles of dialogic teaching, enhancing purposefulness, reciprocity, supportiveness, cumulativeness, and collectivity. The study proposes a GenAI-Enhanced Classroom Dialogue Framework, which shows that GenAI does not affect learning outcomes directly but reshapes them by restructuring classroom dialogue. The findings suggest that GenAI mainly strengthens teachers’ pedagogical design capacity and allows classroom interaction to become more structured, inclusive, and cognitively demanding.
Keywords:
Classroom Interaction, Dialogic Teaching, Generative AI, GenAI, Higher Education TeachingDownloads
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