From Assistance to Dependency: A Qualitative Case Study of AI-Mediated Academic Writing Among Postgraduate EFL Students
DOI:
https://doi.org/10.35445/alishlah.v18i3.9922Keywords:
Assessment, distributive fairness, non-formal education, procedural fairnessAbstract
Artificial Intelligence (AI) is increasingly used in academic writing, particularly among EFL learners, due to its ability to support linguistic accuracy and writing efficiency. However, existing studies tend to emphasize the benefits of AI tools, while the potential shift from linguistic assistance to language dependency remains underexplored, especially in postgraduate EFL contexts. This study aims to investigate how AI mediates academic writing among postgraduate EFL students, focusing on the continuum between assistance and dependency. A qualitative case study design was employed, involving 20 postgraduate students. Data were collected through semi-structured interviews and document analysis of students’ writing samples, and analyzed using thematic analysis supported by NVivo. The findings reveal four key themes: (1) AI enhances linguistic accuracy, efficiency, and confidence; (2) AI functions as a cognitive mediator in idea development and text structuring; (3) AI contributes to emerging dependency, reflected in reduced critical engagement and learner autonomy; and (4) AI use operates along a dynamic continuum from assistance to dependency, characterized by tension between awareness and actual practice. These findings suggest that AI is not inherently beneficial or harmful, but its impact depends on how it is used. Practically, the study highlights the need for pedagogical strategies that guide students to use AI critically and reflectively, such as integrating AI-supported tasks with independent writing, promoting metacognitive awareness, and establishing clear boundaries for AI use in academic work.
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