<![CDATA[The study investigates the accuracy of students’ translations of descriptive texts assisted by DeepL Translate and students’ perception toward the use of this translation tool in translation learning. The presence of machine translation tools in academic contexts has raised concerns regarding translation quality and students' overreliance on automated systems. Employing a descriptive qualitative design, the study involved 32 undergraduate students of English Education Study Program at Universitas Negeri Padang. Data were collected through translation tasks consisting of three descriptive texts and a perception questionnaire. Students’ translation accuracy was measured using Nababan translation quality assessment model, while students' perceptions were examined across four aspects: attractiveness, relevance, perceived usefulness, and perceived motivation. The findings show that students achieved the highest translation accuracy in texts with explicit meanings and simple sentence structures (mean score 2.75), while accuracy declined in texts containing figurative language, abstract expressions, and longer sentence constructions (mean scores 2.48 and 2.44). The overall mean score of translation accuracy was 2.55, categorized as accurate. The questionnaire results reveal that students generally hold positive perceptions toward the use of DeepL Translate, with an overall mean score of 3.02, particularly regarding its relevance (3.20) and attractiveness (3.17). However, positive perceptions were not consistently aligned with high translation accuracy. These findings suggest that although DeepL Translate can support translation learning, students’ still need strong linguistic competence and post-editing skill to ensure accurate translation outcomes.]]>
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