Arabic Language Education Students' Experience in Using DeepL for Indonesian to Arabic Text Translation - Kania Dewi; Diny Rismayanti; Alissya Putri Wahyuni; Nazril Irham (Al-Kilmah: Jurnal Pendidikan Bahasa Arab dan Humaniora, 2026)
The development of information technology in the form of Artificial Intelligence based on Neural Machine Translation (NMT) has brought an influence in the world of education, including in Arabic translation activities. One of the technologies that is often used in the translation process is DeepL. This study aims to find out the experience of Arabic language education students in using DeepL to translate texts from Indonesian to Arabic. This study uses a descriptive qualitative method with in-depth interview data collection techniques. The research subjects consisted of four Arabic Language Education students of the class of 2024 who were selected using purposive sampling techniques with the criteria of having used DeepL in translation tasks. The results of the study show that DeepL helps students translate texts and sentences faster, increase vocabulary, and make it easier to compose Arabic sentences. However, there are also several obstacles, such as the absence of harakat, inconsistency of rules, and lack of context of meaning in the translation. In addition to providing benefits, DeepL users also have the potential to cause dependence. Therefore, it is necessary to have a critical attitude and evaluative ability in using DeepL as a tool in learning Arabic.