A Qualitative Approach to EFL Postgraduates’ GenAI-Assisted Research Writing Within Social Sciences

Abstract

Systematic and rigorous approaches are necessary to fully understand GenAI’s (Generative AI’s) impact on L2 English/EFL (English as a Foreign Language) academic writing in higher education. In this scope, postgraduate EFL writing has been explored little. The present qualitative study examines this topic within Social Sciences at the University of Extremadura, Spain, where seven participants with a B2 English level or higher enrolled in a 10-h hybrid course about GenAI for academic English writing in October and November of 2024, focusing on AI tools and Broad Data-Driven Learning (BDDL) resources (e.g., simple online corpora tools) to assist their writing. Participants’ feedback was collected by qualitative means (in-class discussions, task writing annotation, and final survey). Overall findings indicate notably positive responses and usage of these tools for the improvement of their texts (e.g., linguistic analysis, lexical-grammatical refinement, and text style improvement). Participants’ activities also showcase miscellaneous approaches and strategies in their management of GenAI. Despite the study’s small sample, these preliminary findings reveal that these postgraduate EFL writers can exploit expert and linguistic knowledge effectively using GenAI, demonstrating meta-linguistic awareness and digital literacy-related skills.