Redefining Translator Training Paradigm in Ukraine: AI Integration and Compliance with European Standards - Karaban , Viacheslav; Karaban, Anna (The Journal of V.N. Karazin Kharkiv National University. Series: Foreign Philology. Methods of Foreign Language Teaching, 2025)
This article examines critical challenges facing translator training in Ukraine, driven by rapid advances in artificial intelligence (AI) and neural machine translation (NMT), and highlights the need to align national education with international translation standards. Recent developments in AI and neural machine translation have significantly enhanced translation efficiency and accuracy, influencing translator roles worldwide and emphasizing the necessity of adapting educational approaches accordingly. The purpose of the research is to identify existing gaps in language proficiency, translation technology competencies, and skills resilient to automation within Ukrainian higher education curricula, proposing targeted reforms. The study employs a mixed-methods approach, analyzing quantitative data from university programs and qualitative assessments of curricular documentation. Key findings reveal substantial disparities in students’ initial language proficiency, insufficient integration of translation technologies, and inadequate preparation for machine translation post-editing tasks. The study concludes that significant improvements in language training are necessary to enable students to effectively post-edit AI-generated translations and undertake complex, specialized tasks beyond routine translation. Recommendations include implementing standardized entry-level proficiency testing, restructuring curricula in accordance with European Master’s in Translation (EMT) standards, and integrating comprehensive machine translation post-editing (MTPE) courses. Future research should empirically evaluate the effectiveness of these reforms and explore comparative international contexts.