MACHINE TRANSLATION'S IMPACT ON TRANSLATOR TRAINING: BALANCING EFFICIENCY AND CRITICAL THINKING

Dekabr, 2025-Yil 286 MACHINE TRANSLATION'S IMPACT ON TRANSLATOR TRAINING: BALANCING EFFICIENCY AND CRITICAL THINKING Sayyora Usarova Oriental University. https://doi.org/10.5281/zenodo.18022004 Abstract. The use of machine translation (MT) tools has drastically changed the learning procedures in translator education. While MT increases productivity and decreases minor lexical mistakes, depending too much on it may undermine analytical assessment and critical thinking. This study analyzes how MT modifies student translation work, highlighting its benefits, drawbacks, and beneficial teaching methods. Translation tasks with and without MT and reflective questionnaires were combined in a mixed-methods manner. The results show that guided MT use increases productivity while preserving analytical engagement, highlighting the significance of strategic integration in translation training. Keywords: machine translation, translator training, critical thinking, translation quality, student translators. Introduction Machine translation techniques such as DeepL and Google Translate are already commonplace in translation education. Learners are utilizing these tools extensively to accelerate their translating tasks, regularly translating with lexical accuracy and quicken ahead of a thorough comprehension of the real text (Bowker, 2015). Although MT can enhance the basic understanding of a topic, it may also decrease the chances for free problem-solving, cognitive skills, and self-reflection. With an emphasis on lexical correctness, emotional expression translation, and critical thinking abilities, this study investigates how MT impacts student translators' performance. Designing translation training programs that include technology without sacrificing analytical proficiency requires an understanding of these consequences. 2.1 Participants The study included 20 undergraduate translation students with intermediate to upper-level English ability. All participants had previously used MT technologies for homework assignments and in the classroom. 2.2 Research Instruments  Translation assignments: brief literary passages with culturally and emotionally charged language. Both MT and non-MT translations were completed by the students.  Checklist for lexical and cultural analysis: quantitative evaluation of mistakes, emotional content gain and loss.  Reflective questionnaire: Students evaluated their perceived critical thinking involvement, strategy choices and dependence on MT. 2.3 Data Analysis  Quantitative: Table 1 shows the frequency of lexical errors, semantic gain and loss, and strategy usa. Dekabr, 2025-Yil 287  Qualitative: Thematic analysis of the questionnaire answers revealed critical evaluation abilities and reasoning processes.  Visual representation of strategy utilization and critical thinking 3.1 Lexical Accuracy and Emotional Content Table 1. MT vs Non-MT Translation Performance Measure With MT Without MT Lexical errors (average) 7 14 Emotional content preserved (%) 70 55 Semantic loss (%) 10 25 Semantic gain (%) 5 3 Findings: MT helps maintain surface-level emotional content and minimizes basic linguistic errors. Learners who utilized it very often, however, showed less critical thinking in choosing the correct lexical words. 3.2 Translation Strategies and Critical Thinking  Graphical analysis showed a strong correlation between students' critical thinking skills and translation strategies. Learners who used contextual modulation had reached the highest degree of critical thinking, as shown in the bar chart. These pupils were aware of practical and sentimental nuances, actively analyzed lexical options, and changed meanings related to context.  The pupils who employed explicitation techniques demonstrated a moderate degree of critical thinking. They made an effort to elucidate latent meanings, but their choices were frequently impacted by machine-generated recommendations, leding to a lack of analytical depth.  "On the other hand, literal translation was mostly linked to critical thinking. In particular, when machine translation technologies were employed, participants who relied on literal techniques tended to accept straight lexical counterparts without evaluating emotional overtones or cultural distinctiveness.  "The comparative data also show that basic lexical problems were less common in translations generated with machine translation assistance. These translations, however, demonstrated a greater degree of semantic loss and a lower percentage of emotional content preservation. Although surface-level lexical errors were more common, translations completed without machine translation showed greater emotional correctness. Compared to students who used literal MT output, those who used contextual modulation and explicitation showed superior analytical reasoning skills. Lower critical thinking scores were associated with excessive dependence on automated recommendations. The results validate the MT's dual impact on translator education: 1. Acting efficiently means that there are fewer errors related to the language and quick results are gaining in translation. 2. Cognitive Risks: Problem-solving, critical thinking, mindfulness, introspection, and the ability to make clear decisions may be hindered by dependency. Students can gain efficiency while retaining analytical skills through the guided use of MT in conjunction with reflection exercises (O'Brien, 2012; Cronin, 2013). Dekabr, 2025-Yil 288 These findings imply the efficacy of educational techniques, including discussions of translation options and comparative MT/non-MT exercises. Conclusion Although it must be carefully incorporated, machine translation is a useful tool for translator education. When led, MT improves lexical efficiency and accuracy without sacrificing critical thinking skills. Instructors should teach instruction training and give cognitive tasks to engage students and assignments related to increasing their self-esteem, critical thinking, and problem-solving. Studies could demonstrate the real view and benefits or defects of machine translation and how it influences some basic parts in the future. References 1. Bowker, L. (2015). Computer-aided translation technology: A practical introduction. University of Ottawa Press. 2. Cronin, M. (2013). Translation in the Digital Age. Routledge. 3. O’Brien, S. (2012). Translation as human–computer interaction. Translation Spaces, 1(1), 101–122. 4. Pym, A. (2011). What technology does to translation. Translation and Interpreting, 3(1), 1–9. 5. Kiraly, D. (2014). A social constructivist approach to translator education. Routledge.