LOST IN TRANSLATION OR ASSISTED BY AI: MACHINE TRANSLATION DEPENDENCY AND STUDENT TRANSLATION QUALITY - Yandri Susuek Ndun (International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET), 2026)
The increasing availability of artificial intelligence–based machine translation tools has significantly transformed the learning practices of English as a Foreign Language (EFL) students. While such technologies provide rapid and convenient translation assistance, concerns have emerged regarding students’ dependency on these tools and its potential impact on translation competence. This study investigates the relationship between machine translation dependency and the translation quality of fourth-semester students in the English Education Study Program at Universitas Persatuan Guru 1945 NTT. Employing a mixed-methods design, quantitative data were collected through a structured questionnaire measuring machine translation dependency and a translation performance test evaluated using standardized translation quality indicators. Qualitative insights were obtained through semi-structured interviews with selected participants to explore students’ perceptions of AI-assisted translation. Data were analyzed using Structural Equation Modelling (SEM) and thematic analysis. The findings reveal that moderate use of machine translation tools contributes positively to translation efficiency; however, excessive dependency significantly reduces lexical accuracy, syntactic awareness, and contextual equivalence in students’ translations. The study highlights the necessity of pedagogical strategies that balance technological assistance with the development of independent translation competence. These findings contribute to the ongoing discourse on AI integration in language education and offer implications for translation pedagogy in EFL contexts.