Rethinking AI in Arabic Translation Learning: The Role of DeepL and Teacher Mediation
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Abstract
This study aims to analyze the implementation of DeepL AI Translate in Arabic translation learning at MTs Alkhairaat Pusat Palu Indonesia, examine its impact on students’ learning processes, and identify the role of teachers in controlling the quality of AI-assisted translations. This research employs a qualitative descriptive approach within a naturalistic framework, with data collected through 15 days of classroom observations, in-depth interviews, and documentation. The findings reveal that DeepL is integrated into a structured learning process consisting of pre-translation, AI-assisted translation, post-translation, and evaluation stages. The use of DeepL enhances student participation, learning efficiency, confidence, and vocabulary comprehension. However, its use remains largely limited to lexical-level support and may lead to dependency if not pedagogically controlled. In this context, teachers play a crucial role as mediators, facilitators, and quality controllers of translation outcomes. This study highlights that the effectiveness of AI in language learning depends not only on technological capability but also on its pedagogical integration within the classroom.
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