INTEGRATING MACHINE TRANSLATION INTO TRANSLATOR TRAINING: A TRANSFORMATIVE APPROACH IN UZBEK HIGHER EDUCATION (International conference on multidisciplinary science, 2025)
The global translation industry is rapidly evolving due to advances in machine translation (MT), artificial intelligence, and neural network technologies. In this changing context, translator education must adapt to equip graduates with both linguistic and technological competencies. This article explores the integration of MT and post-editing into translator training programs in Uzbekistan through a transformative learning lens. Drawing on the theoretical legacy of Professor G‘aybulla Salomov and recent research by Uzbek scholars such as A. Nurmonov, N. Mahmudov, and B. Allaberdiev, the study discusses how higher education can foster hybrid translator competence combining linguistic expertise, cultural mediation, and AI literacy. The paper concludes that transformative pedagogical models—emphasizing reflection, collaboration, and technology use—can bridge traditional translation theory with contemporary digital practice in Uzbekistan’s universities.