The Use of DeepL as a Translation Tool in English Language Learning: Insights from Indonesian EFL University Students

Silvianida Qurrota A’yun, Sayit Abdul Karim, Gordan Matas, R.Yohanes Radjaban, Winarna Winarna

Abstract


The rapid advancement of digital technologies has transformed language learning practices, for instance, the use of DeepL translation tools for machine translation. It plays a growing role in facilitating comprehension, vocabulary development, and academic literacy among EFL learners. This study provides empirical evidence of how DeepL shapes students’ learning efficiency and translation strategies. This mixed-methods study explores the perceptions and challenges of 27 fourth-year English Language Education Study Program (ELESP) students at Universitas Teknologi Yogyakarta regarding DeepL as a translation tool in their language learning. Employing questionnaires and semi-structured interviews for data collection, the research gathered both quantitative and qualitative data. The questionnaire explored students' overall perceptions, while interviews delved into the specific challenges encountered by six selected students, using a purposive sampling technique across varying proficiency levels: two students with high proficiency, two students with medium proficiency, and two students with low proficiency, as indicated by the analyzed questionnaire scores. Furthermore, the researchers analyzed the questionnaire results through data checking, coding categories, and compared them across the highest, middle, and lowest categories, analyzing the results descriptively. Meanwhile, for the semi-structured interview, the results were analyzed using an interactive model, comprising data collection, data reduction, data display, and drawing conclusions. Findings indicate positive student perceptions. Quantitative analysis of questionnaire data revealed overwhelmingly positive perceptions, with 81.5% and 66.7% of students showing high agreement on key positive statements about DeepL's utility. However, the qualitative data reveal that all interviewed students face challenges, particularly the time-consuming process of double-checking translations due to the uncertainty of meaning. This study highlights DeepL's significant role in facilitating language learning while also underscoring the need for students to develop critical evaluation skills.

Keywords


Challenges; EFL learners; DeepL; Perception; Translation tool

Full Text:

PDF

References


Arnold, D., Balkan, L., Meijer, S., Humphreys, R. L., & Sadler, L. (2001). Machine translation: An introductory guide. BlackWell Publisher. papers2://publication/uuid/712F9001-24C3-4637-B25F-0B87CF03BF5A

Asmara, D. S. M., & Kembaren, F. R. B. (2024). Students’ perception towards the use of DeepL translator in writing thesis or journal for English education students. IJLECR - International Journal of Language Education and Culture Review, 10(1), 117–126. https://doi.org/https://doi.org/10.21009/ijlecr.v10i1.47937

Birdsell, B. (2022). Student writings with DeepL: Teacher evaluations and implications for teaching. JALT Postconference Publication, 2021(1), 117. https://doi.org/https://doi.org/10.37546/jaltpcp2021-14

Cotelli Kureth, S., Delormes Benites, A., Haller, M., Noghrechi, H., & Steele, E. (2023). “I looked it up in DeepL”: Machine translation and digital tools in the language classroom. Human Translation and Natural Language Processing Towards a New Consensus, 81–96. https://doi.org/https://doi.org/10.30687/978-88-6969-762-3/006

Deng, X., & Yu, Z. (2022). A systematic review of machine-translation-assisted language learning for sustainable education. Sustainability (Switzerland), 14(13). https://doi.org/https://doi.org/10.3390/su14137598

Doherty, S. (2016). The impact of translation technologies on the process and product of translation. International Journal of Communication, 10, 947–969. https://ijoc.org/index.php/ijoc/article/view/3499/1573

Fatkhurozi, A., & Hidayat, R. (2024). EFL students’ perception of Google Translate as a translation tool. Journal of Linguistics and Social Studies, 1(2), 75–83. https://doi.org/https://doi.org/10.52620/jls.v1i2.49

Han, C., & Lu, X. (2021). Interpreting quality assessment re-imagined: The synergy between human and machine scoring. Interpreting and Society, 1(1), 70–90. https://doi.org/https://doi.org/10.1177/27523810211033670

Jamal, A., Barges, B., Ali, S., Mubarak, J., Siraj, A., & Fahad, A. F. (2025). Exploring the perceptions of EFL learners on using artificial intelligence tools – A survey based study. British Journal of English Language Linguistics, 13(1), 17–48. https://doi.org/https://doi.org/10.37745/bjel.2013/vol13n11748

Kamaluddin, M. I., Rasyid, M. W. K., Abqoriyyah, F. H., & Saehu, A. (2024). Accuracy analysis of DeepL: Breakthroughs in machine translation technology. Journal of English Education Forum (JEEF), 4(2), 122–126. https://doi.org/https://doi.org/10.29303/jeef.v4i2.681

Kembaren, F. R. W. br., Pardamean, Hamdany, S., & Alqawwiy, T. A. (2024). Students’ perception of using translate tools in their assignment. Jurnal Pendidikan Tambusai, 8(2), 27375–27383. https://jptam.org/index.php/jptam/article/view/16838

Khairyah, P. N., Anastasya, M., & Natsir, R. Y. (2024). A phenomenological study of English education students’ perceptions of using DeepL Pro for text translation. IJM : Indonesian Journal of Multidisciplinary, 2, 610–619. https://doi.org/https://doi.org/10.26877/kghjpe40

Khotimah, K., Wahyudin, W., & Rohbiah, T. S. (2021). Students’ perception of Google Translate in online English learning. Jelts, 4(2), 78–85. https://doi.org/http://dx.doi.org/10.48181/jelts.v4i2.12016.

Kim, H. J. (2024). Machine translation use in presentation scripts: Learners’ reflections and implications for English education. Korean Journal of English Language and Linguistics, 24, 425–440. https://doi.org/https://doi.org/10.15738/kjell.24..202404.425

Kirana, A., Kembaren, F. R. W., & Hz, B. I. R. (2024). The influence of DeepL translator on EFL students’ writing. Jurnal Ilmu Sosial, Humaniora Dan Seni, 3(1), 746–753. https://doi.org/https://doi.org/10.62379/jishs.v3i1.2059

Laksana, K. N., Komara, & Cahya. (2024). Indonesian EFL students’ perceptions of DeepL machine translation tool: Utilization, advantages, and disadvantages. Journal of Language and Literature Studies, 4(2), 256–276. https://doi.org/https://doi.org/10.36312/jolls.v4i2.1931

Mahmud, N. I., & Saud, I. W. (2024). Students’ perception on the use of AI paraphrasing tools in writing research proposal. Journal of English Language Teaching, Linguistics, and Literature Studies, 4(2), 138. https://doi.org/https://doi.org/10.30984/jeltis.v4i2.3260

Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognitive Science, 5(4), 333–369. https://doi.org/https://doi.org/10.1016/S0364-0213(81)80017-1

Miles, M. B., & Huberman, M. (2014). Qualitative data analysis (SAGE (ed.)). https://opac.perpusnas.go.id/DetailOpac.aspx?id=498252

Munawwarah, M., & Martriwati. (2024). Students’ perception of using Deepl as machine translation in English learning. ELLTER Journal, 5(2), 284–295. https://doi.org/https://doi.org/10.22236/ellter.v5i2.15813

Ngoc, N. T. N., Truc, T. T., An, N. N. H., Phat, L. H., San, N. H. M., & Thu, T. N. A. (2025). The benefits and challenges of AI translation tools in translation education at the tertiary level: A systematic review. International Journal of TESOL & Education, 5(2), 132–148. https://doi.org/https://doi.org/10.54855/ijte.25527

Novita, N., & Mudjiran, M. (2021). Student perceptions about the personality competence of the academic advisor and its role in helping student learning success. Jurnal Neo Konseling, 3(2), 102. https://doi.org/https://doi.org/10.24036/00427kons2021

Nusantara, B. A., & Izzah, N. (2024). Exploring the use of translator applications: Do they improve English language learners’ skills? JEEYAL (The Journal of English Teaching for Young and Adult Learners), 03(01), 1–8. https://doi.org/10.21137/jeeyal.2024.23.1.1

Polakova, P., & Klimova, B. (2023). Using DeepL translator in learning English as an applied foreign language – An empirical pilot study. Heliyon, 9(8). https://doi.org/https://doi.org/10.1016/j.heliyon.2023.e18595

Rivera-Trigueros, I. (2022). Machine translation systems and quality assessment: A systematic review. Language Resources and Evaluation, 56(2), 593–619. https://doi.org/https://doi.org/10.1007/s10579-021-09537-5

Rizky, A. N. N. S., & Rohmana, W. I. M. (2025). Exploring the role of online machine translation in language classrooms: Indonesian EFL learners’ views and practices at an Islamic university. Englisia : Journal of Language, Education, and Humanities, 12(2), 60. https://doi.org/https://doi.org/10.22373/ej.v12i2.26914

Sihombing, M., Permata, K., Manalu, S., & Tampubolon, N. (2025). A literature-based comparative analysis of DeepL and Google Translate: Strengths and limitations in English translation. Jurnal Pendidikan Tambusai, 9(1), 6757–6763. https://doi.org/https://doi.org/10.31004/jptam.v9i1.25558

Sugiyono. (2018). Metode penelitian pendidikan: Pendekatan kuantitatif, kualitatif, dan R&D. https://digi-lib.stekom.ac.id/assets/dokumen/ebook/feb_35efe6a47227d6031a75569c2f3f39d44fe2db43_1652079047.pdf

Wulansari, A., Syihabudin, S., & Waluyo, S. (2024). Post-editing machine translation of English-Indonesian by EFL students: a study on grammatical cohesion in abstract translation. Celtic : A Journal of Culture, English Language Teaching, Literature and Linguistics, 11(2), 337–352. https://doi.org/https://doi.org/10.22219/celtic.v11i2.33914

Yudianto, H., Surtikanti, M., & Agung, A. (2025). Students’ viewpoint of automatic translation tools in the EFL Classroom. Pedagogy : Journal of English Language Teaching, 13(1), 37–63. https://doi.org/https://doi.org/10.32332/678aav10.




DOI: http://dx.doi.org/10.21043/jetli.v8i2.33633

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Journal of English Teaching and Learning Issues

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License