Students' Perceptions and Challenges of Using Google Translate in EFL Writing Classes: A Mixed Methods Study in Vocational Education
DOI:
https://doi.org/10.61987/educazione.v3i2.2455Keywords:
Google Translate, EFL Writing, Students' Perceptions, Technology Acceptance Model, Machine TranslationAbstract
Google Translate has become a routine writing aid for students learning English as a foreign language, and vocational high school learners in Indonesia rely on it heavily. Research on this group remains limited, and most existing studies emphasize the technical accuracy of machine translation rather than how students themselves perceive the tool. This study examines how vocational high school students perceive the use of Google Translate in writing classes and identifies the challenges they encounter while using it. The study adopted an explanatory sequential mixed methods design grounded in the Technology Acceptance Model. Quantitative data were collected through a questionnaire completed by 175 eleventh-grade students at a tourism vocational school in Bali, and qualitative data were drawn from structured interviews with twelve of them. The findings reveal a clear gradient of acceptance. Positive endorsement was strongest for perceived ease of use and declined steadily through perceived usefulness and attitude toward using to behavioral intention, where reservations were strongest. Students valued the tool for its speed, simplicity, and vocabulary support, and they deliberately limited their reliance on it because they feared dependence and distrusted its accuracy in longer sentences. The pedagogical implication is that teachers should position Google Translate as a resource for critical review and revision rather than a substitute for learning, so that its convenience supports writing development without eroding language competence. This study contributes a perception-based account that extends acceptance research in vocational EFL contexts.
References
Ahmed, A. M. T., & Lenchuk, I. (2024). The Interaction Between Morphosyntactic Features and the Performance of Machine Translation Tools: The Case of Google Translate, Systran, and Microsoft Bing in English-Arabic Translation. Theory and Practice in Language Studies, 14(2), 614–625. https://doi.org/10.17507/tpls.1402.35
Al-Smadi, H. M. (2022). Challenges in Translating Scientific Texts: Problems and Reasons. Journal of Language Teaching and Research, 13(3), 550–560. https://doi.org/10.17507/jltr.1303.11
Alghamdi, S., & Soh, B. (2023). Relationship of Perceived Usefulness, Perceived Ease of Use, and Integrating Personal Innovativeness in Information Technology (Piit) With the Intention To Use Moocs Continuously Using the Technology Acceptance Model. Proceedings on Engineering Sciences, 5(4), 767–780. https://doi.org/10.24874/PES05.04.019
Ali, E. H. F., Kottaparamban, M., Usmani, S., & Mohammed, J. K. K. (2025). Saudi Students’ Perceptions of the Influence of Social Media New Vocabulary on English Language Proficiency. Theory and Practice in Language Studies, 15(2), 452–461. https://doi.org/10.17507/tpls.1502.15
Almahasees, Z., & Mahmoud, S. (2022). Evaluation of Google Image Translate in Rendering Arabic Signage into English. World Journal of English Language, 12(1), 185–197. https://doi.org/10.5430/wjel.v12n1p185
Alrajhi, A. S. (2023). Genre Effect on Google Translate-Assisted L2 Writing Output Quality. ReCALL, 35(3), 305–320. https://doi.org/10.1017/S0958344022000143
Amrullah, A., Lail, H., Sumayani, S. R., & Hamzah, N. H. B. (2023). Students’ Perspectives on the Usefulness of ICT-Based Learning by Using Technology Acceptance Model (TAM). Eralingua: Jurnal Pendidikan Bahasa Asing Dan Sastra, 7(1), 162. https://doi.org/10.26858/eralingua.v7i1.41955
Avsheniuk, N., Seminikhyna, N., Ruban, L., & Sviatiuk, Y. (2025). Exploring Overreliance on AI Tools in English for Specific Purposes Courses: Challenges and Implications for Learning and Academic Integrity. Arab World English Journal, 2025(Special Issue), 3–20. https://doi.org/10.24093/awej/AI.1
Bausells-Espín, A. (2022). Audio Description as a Pedagogical Tool in the Foreign Language Classroom: An Analysis of Student Perceptions of Difficulty, Usefulness and Learning Progress. Journal of Audiovisual Translation, 5(2 Special Issue), 152–175. https://doi.org/10.47476/jat.v5i2.2022.208
Borodina, M., Golubeva, T. I., Korotaeva, I. E., Shumakova, S. Y., Bessonova, T. V., & Zharov, A. N. (2021). Impact of the Google Translate Machine Translation System on the Quality of Training Student Translators. Webology, 18(Special Issue), 68–78. https://doi.org/10.14704/WEB/V18SI05/WEB18214
Braun, V., & Clarke, V. (2021). Conceptual and Design Thinking for Thematic Analysis. Qualitative Psychology, 9(1), 3–26. https://doi.org/10.1037/qup0000196
Braun, V., & Clarke, V. (2023). Is Thematic Analysis Used Well in Health Psychology? A Critical Review of Published Research, With Recommendations for Quality Practice and Reporting. Health Psychology Review, 17(4), 695–718. https://doi.org/10.1080/17437199.2022.2161594
Braun, V., & Clarke, V. (2024). A Critical Review of the Reporting of Reflexive Thematic Analysis in Health Promotion International. Health Promotion International, 39(3). https://doi.org/10.1093/heapro/daae049
Cai, H. (2022). Examining Social E-Commerce Platforms by Mediating the Effect of Perceived Usefulness and Perceived Trust Using the Technology Acceptance Model. Journal of Organizational and End User Computing, 34(8), 1–20. https://doi.org/10.4018/joeuc.315621
CAN, S. (2023). Instructors’ Perceptions of Students’ Google Translate Use in Language Learning. Söylem Filoloji Dergisi, Çeviribilim Özel Sayısı, 474–482. https://doi.org/10.29110/soylemdergi.1186593
DuBay, M., Sideris, J., & Rouch, E. (2022). Is Traditional Back Translation Enough? Comparison of Translation Methodology for an ASD Screening Tool. Autism Research, 15(10), 1868–1882. https://doi.org/10.1002/aur.2783
Fingerhut, J., & Moeyaert, M. (2022). Selecting and Justifying Quantitative Analysis Techniques in Single-Case Research Through a User-Friendly Open-Source Tool. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.1064807
Gumartifa, A., Yuliani, S., Marliasari, S., & Tarmizi, M. (2022). English Language Translation Through Students’ Opinions Toward Google Translate Machine in the EFL Class. English Education Journal, 12(4), 479–488. https://doi.org/10.15294/eej.v12i4.65314
H Aldawsari, H. A. (2023). Comparing the Performance of Google Translate and SYSTRAN on Arabic Lexical Ambiguity. Arab World English Journal For Translation and Literary Studies, 7(3), 19–34. https://doi.org/10.24093/awejtls/vol7no3.2
Hamzah, M. L., Ansharullah, A., Rahmat, Z., Zatrahadi, M. F., & Purwati, A. A. (2024). Acceptance of Virtual Reality-Based Hadith Learning Model With Technology Acceptance Model (TAM) Approach. JPPI (Jurnal Penelitian Pendidikan Indonesia), 10(3), 205. https://doi.org/10.29210/020244277
Haynes-Brown, T. K. (2023). Using Theoretical Models in Mixed Methods Research: An Example From an Explanatory Sequential Mixed Methods Study Exploring Teachers’ Beliefs and Use of Technology. Journal of Mixed Methods Research, 17(3), 243–263. https://doi.org/10.1177/15586898221094970
Hussain, A., & Ud Din, M. (2025). Reimagining EFL Learning: AI Tools vs. Traditional Methods in an Underserved Area. English Education Journal, 16(4), 238–253. https://doi.org/10.24815/eej.v16i4.49861
Ibrahim, A., & Shiring, E. (2022). The Relationship Between Educators’ Attitudes, Perceived Usefulness, and Perceived Ease of Use of Instructional and Web-Based Technologies: Implications From Technology Acceptance Model (TAM). International Journal of Technology in Education, 5(4), 535–551. https://doi.org/10.46328/ijte.285
Jung, S. Y., Moon, H.-W., Park, D. S. M., Sung, S., & Jung, H. (2023). Nurses’ Burden of Elimination Care: Sequential Explanatory Mixed-Methods Design. International Journal of General Medicine, 16, 4067–4076. https://doi.org/10.2147/ijgm.s424424
Kane, V. L. (2021). Interpretation and Machine Translation Towards Google Translate as a Part of Machine Translation and Teaching Translation. Applied Translation, 15(1). https://doi.org/10.51708/apptrans.v15n1.1337
Kim, H.-K., & Han, S. (2021). College Students’ Perceptions of AI-Based Writing Learning Tools: With a Focus on Google Translate, Naver Papago, and Grammarly. Modern English Education, 22(4), 90–100. https://doi.org/10.18095/meeso.2021.22.4.90
Leahy, K., Ozer, E., & Cummins, E. P. (2025). AI-ENGAGE: A Multicentre Intervention to Support Teaching and Learning Engagement with Generative Artificial Intelligence Tools. Education Sciences, 15(7). https://doi.org/10.3390/educsci15070807
Lieshout, C. van, & Cardoso, W. (2022). Google Translate as a Tool for Self-Directed Language Learning. Language Learning and Technology, 26(1), 1–19. https://doi.org/10.64152/10125/73460
Mageda, K., Kulemba, K., Kapologwe, N., Katalambula, L., & Petrucka, P. (2023). Factors Associated With Low Antiretroviral Therapy Enrollment of Children in the Simiyu Region: A Cross-Sectional Creswell Mixed-Methods Sequential Explanatory Design. Medicine (United States), 102(14), E33454. https://doi.org/10.1097/MD.0000000000033454
Mohammadi, K., Jafarpour, A., Alipour, J., & Hashemian, M. (2024). The Impact of Different Kinds of Collaborative Prewriting on EFL Learners’ Degree of Engagement in Writing and Writing Self-Efficacy. Reading and Writing Quarterly, 40(1), 1–18. https://doi.org/10.1080/10573569.2022.2157783
Otanjac, M. I. (2025). Machine Translation and Post-Editing in Special Education Research Titles: A Comparative Study of Google Translate and ChatGPT-4. ASp, 88, 321–344. https://doi.org/10.4000/15c2l
Pan, Z., Cho, H. J., & Jo, D. H. (2023). Determinants of Live Commerce Acceptance: Focusing on the Extended Technology Acceptance Model (TAM). KSII Transactions on Internet and Information Systems, 17(10), 2750–2767. https://doi.org/10.3837/tiis.2023.10.009
Piccinini, F., Drudi, L., Pyun, J. C., Lee, M., Kwak, B., Ku, B., Carbonaro, A., Martinelli, G., & Castellani, G. (2024). Two-Dimensional Segmentation Fusion Tool: An Extensible, Free-to-Use, User-Friendly Tool for Combining Different Bidimensional Segmentations. Frontiers in Bioengineering and Biotechnology, 12. https://doi.org/10.3389/fbioe.2024.1339723
Rashed Alkatheery, E. (2023). Google Translate Errors in Legal Texts: Machine Translation Quality Assessment. Arab World English Journal For Translation and Literary Studies, 7(1), 208–219. https://doi.org/10.24093/awejtls/vol7no1.16
Sushma Rani. (2025). Exploring the Influence of Perceived Usefulness and Ease of Use of Social Media Influencers on Skincare Product Purchases: A Technology Acceptance Model (TAM) Perspective. Journal of Information Systems Engineering and Management, 10(36s), 902–905. https://doi.org/10.52783/jisem.v10i36s.6612
Van Nguyen, M. (2023). Google Translate for Writing in an Online English Class: Vietnamese Learners’ Perceptions and Performances. The EuroCALL Review, 30(1), 5–17. https://doi.org/10.4995/eurocall.2023.18246
Zhang, Z. (Victor), & Hyland, K. (2023). Student Engagement With Peer Feedback in L2 Writing: Insights From Reflective Journaling and Revising Practices. Assessing Writing, 58. https://doi.org/10.1016/j.asw.2023.100784
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 I Kadek Dian Satriawan, Anak Agung Putri Maharani, Ida Bagus Nyoman Mantra

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
