Exploring Postgraduate Students' Generative AI Use in English Academic Writing: Practices, Challenges and Pedagogical Implications
Ge Zhang *
School of Foreign Languages and Cultures, Nanjing Normal University, No. 122 Ninghai Road, Gulou District, Nanjing 210024, China.
Liping Chen
School of Foreign Languages and Cultures, Nanjing Normal University, No. 122 Ninghai Road, Gulou District, Nanjing 210024, China.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study investigated the use of generative artificial intelligence (GAI) by postgraduate students in English as a foreign language (EFL) thesis introduction writing, focusing on their interaction practices, rhetorical and functional writing difficulties, and relationships between these difficulties and observable GAI use.
Study Design: An exploratory, classroom-embedded study combining qualitative analysis with descriptive statistics.
Place and Duration of Study: A postgraduate thesis writing course at a Chinese university, September–December 2025, with data drawn primarily from the first five weeks.
Methodology: The study analysed 35 students' thesis introduction drafts and corresponding GAI interaction records, supplemented by stimulated-recall interviews with three participants. GAI-use practices were examined in terms of interaction patterns, task focus, and text-generation responsibility. The drafts were analysed with reference to the Create a Research Space model and functional adequacy, followed by a cross-source analysis.
Results: Students used GAI for diverse writing tasks and differed in their allocation of text-generation responsibility, but sustained interaction around individual writing problems was limited. Although most drafts contained recognisable rhetorical moves, difficulties persisted in literature synthesis, research-gap construction, and argumentative coherence. Cross-source analysis further revealed difficulties in identifying genre-specific writing problems, formulating targeted GAI requests, and evaluating generated content against relevant evidence and rhetorical expectations.
Conclusion: Meaningful GAI-assisted academic writing requires more than frequent interaction or control over text generation. Genre-specific diagnostic criteria and process-oriented support are needed to promote purposeful GAI use and evidence-based authorial judgement.
Keywords: Generative artificial intelligence, English for academic purposes, thesis introduction writing, human–AI collaboration, genre analysis, functional adequacy, authorial responsibility