Students' Perception Toward The Use of Deepl as a Machine Translation Tool for English Writing
Abstract
Students faced challenges in translating Indonesian to English for academic writing. DeepL provided a translation solution, leading this study to investigate students' perceptions of using DeepL as a translation tool for writing in English. This mixed-method study examined 117 students (31 males, 86 females) from the English Language Study Program, semester 6 and 8 programs, Universitas Tanjungpura, selected through purposive sampling. Data were collected using validated online questionnaires. Quantitative analysis utilized descriptive statistics and Mann-Whitney U tests, while qualitative data used thematic analysis. The findings showed that students demonstrated high positive perceptions across all dimensions: PEOU (3.92), PU (3.69), ATU (3.75), and BIU (3.62). DeepL was perceived as highly accessible and effective for enhancing writing quality, particularly in contextual vocabulary and grammar improvement, with no significant gender differences emerging (U = 1245.000, Z = -.544, p = 0.587). In conclusion, DeepL was widely accepted by both genders as a translation tool for writing effectively, improving the efficiency and quality of students' writing. The implication was that DeepL showed strong potential for strategic integration in English writing assignments. However, implementation should have emphasized critical evaluation skills and balanced usage to prevent over-dependence while maximizing benefits for writing efficiency and quality improvement.
References
Abdalhussein, H. F. (2021). Relationship between gender difference and dtudents’ perceptions of Google Translate use and accuracy along with difficulties. Review of International Geographical Education Online, 11(5). https://doi.org/10.48047/rigeo.11.05.120
Asmara, D. S. M., & Kembaren, F. R. B. (2024). Student’s 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/10.21009/ijlecr.v10i1.47937
Brezavšček, A., Šparl, P., & Žnidaršič, A. (2017). Factors influencing the behavioural intention to use statistical software: The perspective of the Slovenian students of social sciences. Eurasia Journal of Mathematics, Science and Technology Education, 13(3). https://doi.org/10.12973/eurasia.2017.00652a
Bunga, E. L. M., & Katemba, C. V. (2024). Comparing translation quality: Google Translate Vs Deepl for foreign language To English. EDUSAINTEK: Jurnal Pendidikan, Sains Dan Teknologi, 11(3), 1147–1171. https://doi.org/10.47668/edusaintek.v11i3.1264
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Fadhillah, M., Flora Oktaviani, C., Cut, S., & Juniarti, L. (2025). A contrastive analysis of DeepL translation vs. Google Translate’s performance in rendering academic texts: Insights from EFL learners. Jurnal Serambi Ilmu, 26(1). https://doi.org/10.20473/jovin.v1i1.19873
Hariroh, F., & Hamdani, B. (2025). Analysis of DeepL application as an alternative media for translating academic assignments. Professional Journal of English Education, 8(2), 507–515.
Huang, Z., Wang, P., Peng, L., Jin, H., & Liu, T. (2025). Exploring factors influencing learners’ continuance intention to use machine translation tools: a chain mediation model. Education and Information Technologie.
Iddrisu, H. M., Iddrisu, S. A., & Aminu, B. (2025). Gender differences in the adoption , usage , and perceived effectiveness of AI writing tools : A study among university for development studies students. 4(1), 100–111.
Kahlon, N. K., & Singh, W. (2023). Machine translation from text to sign language: A systematic review. Universal Access in the Information Society, 22(1). https://doi.org/10.1007/s10209-021-00823-1
Khairyah, P. N., Anastasya, M., & Natsir, R. Y. (2024). A phenomenological study of Dnglish education students ’ perceptions of using DeepL pro for text translation. IJM : Indonesian Journal of Multidisciplinary, 2, 610–619.
Kirana, A., Waty, F. R. K., & Rahman, B. I. (2024). The influence of DeepL translator on EFL students’ writing. Humaniora Dan Seni (JISHS), 02(4), 746–753. http://jurnal.minartis.com/index.php/jishs
Kothari, C. R. (2004). Research methodology: Methods & techniques. In New Age International (P) Ltd. New Age International. https://doi.org/10.1017/CBO9781107415324.004
Laksana, K. N., & Komara, C. (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/10.36312/jolls.v4i2.1931
Leavy, P. (2017). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. In Universitas Nusantara PGRI Kediri (Vol. 01).
Liang, L. (2024). The impact of using DeepL Translator on EFL students’ writing. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1109/ICALT61570.2024.00043
Murtisari, E. T. (2021). Use of translation strategies in writing: Advanced efl students. LLT Journal: Journal on Language and Language Teaching, 24(1). https://doi.org/10.24071/llt.v24i1.2663
Muyassaroh, S. (2022). Students’ perception difficulties in Indonesian-English translation: A survey study to the fifth semester students. JELLT (Journal of English Language and Literature Teaching), 7(1), 136–146.
Oktaviani, S., Bahrani, & Noor, W. N. (2022). Students’ perception towards the use of grammarly in English writing skill of fourth semester English education department at UINSI Samarinda in the academic year 2021/2022. Jurnal Sultan Idris Pendidikan Profesi Guru (SIPPG), x(x).
Poibeau, T. (2017). Machine translation. In 3D Printing. The MIT Press. https://doi.org/10.7551/mitpress/11800.003.0016
Qazi, A., Hasan, N., Abayomi-Alli, O., Hardaker, G., Scherer, R., Sarker, Y., Kumar Paul, S., & Maitama, J. Z. (2022). Gender differences in information and communication technology use & skills: a systematic review and meta-analysis. In Education and Information Technologies (Vol. 27, Issue 3). https://doi.org/10.1007/s10639-021-10775-x
Retnomurti, A. B., Lustyantie, N., & Murtadho, F. (2024). Evaluation of Indonesian to English translation at the undergraduate level using componential assessment. International Journal of Language and Ubiquitous Learning, 2(2). https://doi.org/10.70177/ijlul.v2i2.893
Samputra, M., Bunau, E., & Salam, U. (2024). Analyzing the translation quality of English to Indonesian translation between human translation. Jurnal Pengembangan Ilmu Pengetahuan, 05(4), 62–72.
Sari, D. M. (2022). Islamic students’ attitudes and perceptions toward learning English: A study from male-female students of STIT Madani Yogyakarta. At Turots: Jurnal Pendidikan Islam, 3(2). https://doi.org/10.51468/jpi.v3i2.76
Shaouf, A., & Altaqqi, O. (2018). The impact of gender differences on adoption of information technology and related responses: A review. International Journal of Management and Applied Research, 5(1). https://doi.org/10.18646/2056.51.18-003
Siahaan, E. B. (2020). Students’ perception of Edmodo use as a learning tool. JET (Journal of English Teaching), 6(1). https://doi.org/10.33541/jet.v6i1.1061
Škobo, M., & Petričević, V. (2023). Navigating the Challenges and Opportunities of Literary Translation in the Age of AI: Striking a Balance Between Human Expertise and Machine Power. Društvene i Humanističke Studije (Online), 8(2(23)). https://doi.org/10.51558/2490-3647.2023.8.2.317
Strzelecki, A., & Arabawy, S. El. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3). https://doi.org/10.1111/bjet.13425
Telaumbanua, Y. A., Marpaung, A., Gulo, C. P. D., WijayaWaruwu, D. K., Zalukhu, E., & Zai, N. P. (2024). An analysis of two translation applications : Why is Deepl translate more accurate than Google Translate ? Journal of Artificial Intelligence and Engineering Applications, 4(1).
Wang, J., & Ke, X. (2022). Integrating machine translation into EFL writing instruction: Process, product and perception. Journal of Language Teaching and Research, 13(1). https://doi.org/10.17507/JLTR.1301.15
Weng, F., Yang, R. J., Ho, H. J., & Su, H. M. (2018). A TAM-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1(3). https://doi.org/10.3390/asi1030036
Winarsih, R., Salam, U., & Riyanti, D. (2021). An analysis of students’ language learning strategies in academic writing. Jurnal JOEPALLT (Journal of English Pedagogy, Linguistics, Literature, and Teaching), 9(1). https://doi.org/10.35194/jj.v9i1.1298
Yuzar, E., & Rejeki, S. (2020). The correlation between productive and receptive language skills: An examination on ADFELPS test scores. SALEE: Study of Applied Linguistics and English Education, 1(02). https://doi.org/10.35961/salee.v1i02.111
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Article Metrics
Abstract view : 0 timesPDF - 44 times
Accreditation Journal
