Postgraduate EFL Writing with GenAI across Scientific Domains: A Qualitative Approach to Faculty and Doctoral Student Feedback

Abstract

To understand the impact of Generative Artificial Intelligence (GenAI) on the academic writing of English as a Foreign Language (EFL) students in higher education, more research is required across all academic levels, including postgraduate writing. Therefore, this qualitative study begins to fill this gap by examining a group of postgraduates at the University of Extremadura, Spain. Twenty-one participants, all with a B2 or higher English proficiency level, enrolled in a 10-hour hybrid course during October and November 2024. The course focused on using GenAI and Broad Data-Driven Learning (BDDL) resources, such as simple online corpora tools, to assist their academic writing. We collected participant feedback through qualitative means, including in-class discussions, annotated writing tasks, and a final survey. The overall findings show that participants responded positively to these tools and used them to improve their texts in key areas: linguistic analysis, lexical-grammatical refinement, and writing style. We also observed that participants in Social Sciences and Humanities appraised these resources and approaches distinctively more positively, coping with more linguistic nuances. Writers in Experimental Sciences and Engineering, in contrast, revealed a more lukewarm stance while acknowledging the importance of these tools for their academic writing. Despite the study’s small sample size, these preliminary findings suggest that postgraduate EFL writers can successfully combine linguistic and expert knowledge with GenAI tools to enhance their writing in their respective fields.