TRANSLATION STRATEGIES IN EFL/ESL AND MTGEN/AI POST-EDITING

This study investigates the interplay of translation techniques, machine translation (MT), and generative artificial intelligence (GenAI) within English as a Foreign Language (EFL) and English as a Second Language (ESL) settings. Utilizing a Systematic Literature Review (SLR) of 23 peer-reviewed studies from 2021–2025, it delineates prevailing methodologies in literary, business, and cultural texts, investigates determinants affecting strategy selection, and contrasts human translation with MT/GenAI post editing regarding accuracy, fluency, and cultural subtleties. Following PRISMA 2020 criteria, the analysis used descriptive statistics and thematic categorization based on both traditional and postcolonial frameworks. The results show that more and more people want a hybrid approach to translating education and practice, where MT/GenAI makes drafts and human post editing improves the quality of language and culture. The "draft by machine, craft by human" paradigm improves translation skills by connecting tactics like explicitation, compensation, and idiomatic adaptation to better readability and coherence. The study suggests combining MT/GenAI with rubric based post editing to improve translators' tech and strategic skills in modern translation teaching.