<![CDATA[This study examines students’ experiences of using DeepL AI for academic translation, particularly in relation to translating research proposals from Indonesian into English, with a focus on perceived benefits and challenges. A descriptive qualitative approach was employed, involving 22 students from the English Department, Faculty of Languages and Literature, Universitas Negeri Makassar, who had experience using DeepL for academic translation. Data were collected through an online questionnaire administered via Microsoft Forms, consisting of closed- and open-ended questions. Responses to the open-ended questions were analyzed using the interactive model of Miles, Huberman, and Saldaña through data condensation, data display, and conclusion drawing and verification. The findings show that DeepL helped students translate more efficiently, produce language they perceived as relatively natural, use academic vocabulary and style, understand English-language literature, and support academic writing and revision. However, students still encountered difficulties with specialized terminology and context-dependent meanings, requiring them to review and edit the translations. Technical limitations and the risk of overreliance on AI were also concerns. These findings highlight the need to develop students’ post-editing skills so that machine translation can be used critically and responsibly in academic work.]]>
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