Reconstructing the Grammar-Translation Method with Generative AI in EFL Teaching

Abstract

The Grammar-Translation Method (GTM) has traditionally been associated with rule-based instruction, vocabulary memorization, and sentence translation. (Lu, 2023) Although frequently criticized for lacking communicative practice, GTM continues to influence teaching in many EFL classrooms where grammatical accuracy and reading ability remain central goals. The recent emergence of generative artificial intelligence (AI) tools provides new possibilities for revisiting this long-standing method. This paper explores how generative AI can be practically integrated into GTM to modernize classroom procedures, increase learner engagement, and improve form-focused learning outcomes. Based on realistic classroom practices, the study proposes an instructional framework that combines explicit grammar teaching, translation activities, and AI-assisted feedback. Several teaching examples are presented to illustrate how this approach can be implemented in daily lessons. The paper argues that generative AI does not replace the teacher but enhances the effectiveness of GTM by providing abundant contextualized input, differentiated exercises, and immediate feedback. This reconstructed approach offers a feasible and efficient model for EFL teachers working in accuracy-oriented learning contexts.