Pedagogically Guided Integration of Generative AI in Mandarin Chinese Academic Writing: Lecturers’ Perspectives from Higher Education
DOI:
https://doi.org/10.53797/anp.jssh.v7i1.1.2026Keywords:
AI, Generative AI, Writing Model, Mandarin, PedagogicalAbstract
This study explores lecturers’ perspectives on integrating Generative AI Writing Models (GAIWM) into Mandarin Chinese academic writing instruction, with particular attention to pedagogical strategies, instructional design, assessment practices, and implementation challenges. Adopting a qualitative exploratory design, the study draws on semi-structured interviews with expert lecturers at Guangxi Art University. Data were analyzed using thematic analysis to identify recurring patterns related to GAIWM integration in academic writing pedagogy. The findings reveal four interrelated themes: (1) pedagogically guided integration of GAIWM as a supportive instructional scaffold, (2) alignment of AI tools with Mandarin Chinese academic writing conventions, (3) structured instructional design and process-oriented assessment of AI-based activities, and (4) challenges related to ethics, technological limitations, and professional development needs. Lecturers emphasized teacher mediation, contextual relevance, and controlled AI use as essential for meaningful learning outcomes. The study contributes a context-sensitive thematic model that conceptualizes effective GAIWM integration as an interaction among pedagogy, linguistic context, instructional design, and institutional capacity. The findings offer practical implications for writing instruction, curriculum design, and professional development in AI-enhanced higher education. By foregrounding lecturers’ perspectives in Mandarin Chinese academic writing instruction, this study addresses a critical gap in the AI-in-education literature and advances understanding of responsible, pedagogically grounded generative AI integration beyond English-dominant contexts.
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Copyright (c) 2026 Syaza Hazwani Zaini, Hui Guo Zaini
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