Generative AI in University EFL Writing: Tensions among Efficiency, Language Proficiency, and Learner Agency
Abstract
The rapid diffusion of generative artificial intelligence (GenAI) is reshaping university-level English writing by lowering the linguistic and cognitive costs of text production. Large language models can generate and revise prose, suggest vocabulary, explain grammatical problems, reorganize paragraphs, and provide immediate feedback. These affordances can improve writing efficiency and the surface quality of students' texts, particularly when learners face linguistic constraints or limited access to individualized feedback. Yet the educational value of GenAI cannot be reduced to technological empowerment. When AI performs ideation, language production, organization, and revision on behalf of learners, opportunities for language output and cognitive engagement may be reduced. This paper examines the resulting tension among writing efficiency, language proficiency, and learner agency through the lenses of second language acquisition, cognitive load, constructivist learning, and self-regulated learning. It argues that the central issue is not whether students use AI, but how responsibility for thinking and decision-making is distributed between students and AI. The paper proposes a conceptual model in which the depth of AI involvement interacts with learner agency to shape learning outcomes. The most productive condition is a collaborative development zone in which students retain ownership of goals, arguments, and final judgments while using AI as a linguistic and cognitive scaffold. The paper further argues for process-based assessment, critical AI literacy, staged AI access, and a redefinition of EFL writing competence. In an AI-mediated environment, effective writing competence extends beyond language generation to include evaluation, strategic collaboration, and meaning reconstruction.
