DeepL'in Kullanılabilirliğinin Ölçümü: Çevirmen Eğitiminde Çeviri Teknolojisi Derslerinin Etkisi

Öz

DeepL gibi nöral makine çevirisi (NMÇ) araçları, çeviri eğitiminde giderek daha görünür hale gelmektedir. Artık öğrenciler bu araçları çeviri sürecinde giderek daha yaygın biçimde deneyimlemektedir. Bununla birlikte, çeviri teknolojisi derslerinin kullanılabilirlik algısı üzerindeki etkisi, sınırlı sayıda nicel çalışmada incelenmiştir. Bu çalışma, lisans öğrencilerinin DeepL’nin algılanan kullanılabilirliğini Sistem Kullanılabilirlik Ölçeği (SUS) kullanarak ölçmeyi ve çeviri öğrencileri arasında çeviri teknolojisi dersi alan ve almayanlar arasındaki kullanılabilirlik algısını değerlendirmeyi amaçlamaktadır. Bu nicel çalışmada veriler; betimleyici istatistikler, açımlayıcı faktör analizi ve bağımsız örneklem t-testi kullanılarak analiz edilmiştir. Sonuçlar, DeepL’nin genel kullanılabilirliğinin “kabul edilebilir/iyi” düzeyde olduğunu ve ölçeğin iç tutarlılığının yeterli olduğunu göstermiştir. Faktör analizi, baskın, genel bir kullanılabilirlik unsurunu ortaya koymuştur. Çeviri teknolojileri üzerine bir dersi tamamlamış öğrenciler, DeepL’yi bu tür bir eğitim almamış akranlarına göre önemli ölçüde daha kullanışlı olarak algılamışlardır. İstatistiksel testler, orta düzeyde bir etki büyüklüğüne karşılık gelen bu farklılığı doğrulamıştır. Bu sonuçlar, çeviri teknolojisinde hedeflenen eğitimin yalnızca daha sık kullanımı teşvik etmekle kalmayıp, aynı zamanda bu tür araçların daha olumlu öznel değerlendirmelerini de desteklediğini göstermektedir. Çalışma, bu tür araçların yapılandırılmış, eleştirel bir pedagojik yaklaşımla akademik programlara entegre edilmesinin gerekliliğini vurgulamaktadır.

Anahtar Kelimeler

• çeviribilim
• yapay-zeka destekli çeviri
• çevirmen eğitimi
• DeepL
• sistem kullanılabilirlik ölçeği (SUS)

Measuring the Usability of DeepL: Impact of Translation Technology Courses in Translator Training

Öz

Neural machine translation (NMT) tools, such as DeepL, are becoming increasingly visible in Translation education. Students are experiencing these tools in the translation process. However, the impact of translation technology courses on perceived usability has been examined in a limited number of quantitative studies. This study aims to measure undergraduate students’ perceived usability of DeepL using the System Usability Scale (SUS) and to evaluate the impact of taking a translation technology course on these perceptions. In this quantitative study, the data were analyzed using descriptive statistics, exploratory factor analysis, and an independent-samples t-test. The results showed that DeepL’s overall usability was at an “acceptable/good” level, and the scale’s internal consistency was adequate. The factor analysis revealed a dominant, overarching usability factor. Students who had completed a course on translation technologies perceived DeepL as significantly more usable than their peers without such training. Statistical tests confirmed this disparity, which corresponded to a moderate effect size. These outcomes imply that targeted education in translation technology not only promotes more frequent use but also fosters more favorable subjective assessments of such tools. The study emphasizes the need to incorporate such tools into academic programs through a structured, critically engaged pedagogical approach.

Anahtar Kelimeler

• translation studies
• ai-powered translation
• translator training
• DeepL
• system usability scale (SUS)

Kaynakça

1. Alotaibi, H. M., & Salamah, D. (2023). The impact of translation apps on translation students’ performance. Education and Information Technologies, 28(8), 10709–10729. https://doi.org/10.1007/s10639-023-11578-y google scholar
2. Alzghoul, M. A., Abualasal, J., Alazzam, T. S., Al-Ghammaz, S. A. D., & Alzghoul, R. M. (2025). The Status Quo of Translation Technology Tools in Translator Training Programs at Jordanian Universities. World Journal of English Language, 15(7), 398. https://doi.org/10.5430/wjel.v15n7p398 google scholar
3. Bangor, A., Kortum, P., & Miller, J. (2008). An Empirical Evaluation of the System Usability Scale. International Journal of Human-Computer Interaction, 24(6), 574–594. https://doi.org/10.1080/10447310802205776 google scholar
4. Barut, E. (2026). DeepL usability — System Usability Scale (SUS) survey raw data [Data set]. Zenodo. http://dx.doi.org/10.5281/zenodo.20511024 google scholar
5. Baumgartner, J., Ruettgers, N., Hasler, A., Sonderegger, A., & Sauer, J. (2021). Questionnaire experience and the hybrid System Usability Scale: Using a novel concept to evaluate a new instrument. International Journal of Human Computer Studies, 147. https://doi.org/10.1016/j.ijhcs.2020.102575 google scholar
6. Birdsell, B. (2022). Student Writings with DeepL: Teacher Evaluations and Implications for Teaching. JALT Postconference Publication, 2021(1), 117. https://doi.org/10.37546/jaltpcp2021-14 google scholar
7. Borsci, S., Federici, S., & Lauriola, M. (2009). On the dimensionality of the System Usability Scale: A test of alternative measurement models. Cognitive Processing, 10(3), 193–197. https://doi.org/10.1007/s10339-009-0268-9 google scholar
8. Brooke, J. (1996). SUS: A quick and dirty usability scale. In P. W. Jordan, Thomas B., McClelland Ian Lyall, & Weerdmeester (Eds.), Usability Evaluation in Industry (Number 194, pp. 189–194). Taylor&Francis. https://www.researchgate.net/publication/228593520 google scholar

9. Chen, P. (2024). The Impact of Generative AI on the Role of Translators and Its Implications for Translation Education. In Education Insights Educ. Insights (Vol. 1, Number 2). https://soapubs.com/index.php/EI google scholar
10. Clark, N., Dabkowski, M., Driscoll, P. J., Kennedy, D., Kloo, I., & Shi, H. (2021). Empirical Decision Rules for Improving the Uncertainty Reporting of Small Sample System Usability Scale Scores. International Journal of Human-Computer Interaction, 37(13), 1191–1206. https://doi.org/10.1080/10447318.2020.1870831 google scholar
11. Demirkol, D., & Şeneler, Ç. (2018). Sistem Kullanılabilirlik Ölçeğinin Türkçeye Çevirisi: SUS-TR. Usak University Journal of Social Sciences, 11(Eylül-2018), 238–253. https://doi.org/10.29217/uujss.495 google scholar
12. Derisma, D. (2020). The Usability Analysis Online Learning Site for Supporting Computer programming Course Using System Usability Scale (SUS) in a University. International Journal of Interactive Mobile Technologies (IJIM), 14(9), 182. https://doi.org/10.3991/ijim.v14i09.13123 google scholar
13. Fini, A. (2009). The Technological Dimension of a Massive Open Online Course: The Case of the CCK08 Course Tools. The International Review of Research in Open and Distributed Learning, 10(5). https://doi.org/10.19173/irrodl.v10i5.643 google scholar
14. Gao, M., Kortum, P., & Oswald, F. L. (2020). Multi-Language Toolkit for the System Usability Scale. International Journal of Human-Computer Interaction, 36(20), 1883–1901. https://doi.org/10.1080/10447318.2020.1801173 google scholar
15. Ghasemi, M. S., Aghajafari, R., Abolaghasemi, J., Danesh, M. K., & Garosi, E. (2025). Development and validation of a virtual learning management system usability questionnaire: A case study. Work, 80(1), 75–86. https://doi.org/10.3233/WOR-230290 google scholar
16. Harrati, N., Bouchrika, I., Tari, A., & Ladjailia, A. (2016). Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis. Computers in Human Behavior, 61, 463–471. https://doi.org/10.1016/j.chb.2016.03.051 google scholar
17. Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context. Frontiers in Psychology, 13, 1–8. https://doi.org/10.3389/fpsyg.2022.1049401 google scholar
18. Kasperė, R., Horbačauskienė, J., Motiejūnienė, J., Liubinienė, V., Patašienė, I., & Patašius, M. (2021). Towards sustainable use of machine translation: Usability and perceived quality from the end-user perspective. Sustainability (Switzerland), 13(23). https://doi.org/10.3390/su132313430 google scholar
19. Kenny, D. (2019). Technology and translator training. In Routledge eBooks (pp. 498–515). Informa. https://doi.org/10.4324/9781315311258-30 google scholar
20. Ko, U. H., Hartley, K., & Hayak, M. (2025). Exploring AI in Education: Preservice Teacher Perspectives, Usage, and Considerations. Journal of Information Technology Education: Research, 24. https://doi.org/10.28945/5592 google scholar
21. Krüger, R. (2022). Integrating professional machine translation literacy and data literacy. Lebende Sprachen, 67(2), 247–282. https://doi.org/10.1515/les-2022-1022 google scholar
22. Krüger, R. (2023). Some Reflections on the Interface between Professional Machine Translation Literacy and Data Literacy. Journal of Data Mining and Digital Humanities, 1. https://doi.org/10.46298/jdmdh.9045 google scholar
23. Krüger, R. (2024). Outline of an Artificial Intelligence Literacy Framework for Translation, Interpreting and Specialised Communication. Lublin Studies in Modern Languages and Literature, 48(3), 11–23. https://doi.org/10.17951/lsmll.2024.48.3.11-23 google scholar
24. Kusuma Nata Laksana, & Cahya Komara. (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 google scholar
25. Lewis, J. R., & Sauro, J. (2009). The Factor Structure of the System Usability Scale. In Lecture notes in computer science (pp. 94–103). Springer Science+Business Media. https://doi.org/10.1007/978-3-642-02806-9_12 google scholar
26. Lewis, J. R., & Sauro, J. (2017). Revisiting the Factor Structure of the System Usability Scale. Journal of Usability Studies Archive, 12(4), 183–192. https://doi.org/10.5555/3190867.3190870 google scholar
27. Li, F. (2023). A Survey of Translation Learners’ Uses and Perceptions of Neural Machine Translation. Theory and Practice in Language Studies, 13(11), 3039–3048. https://doi.org/10.17507/tpls.1311.34 google scholar
28. Li, L. (2024). Artificial Intelligence Translator DeepL Translation Quality Control. Procedia Computer Science, 247(C), 710–717. https://doi.org/10.1016/j.procs.2024.10.086 google scholar
29. Li, X., Gao, Z., & Liao, H. (2024). An empirical investigation of college students’ acceptance of translation technologies. PLoS ONE, 19(2). https://doi.org/10.1371/journal.pone.0297297 google scholar
30. Liang, L. (2025). The impact of using DeepL Translator on Chinese EFL students’ story writing. Journal of China Computer-Assisted Language Learning, 5(1), 56–93. https://doi.org/10.1515/jccall-2024-0009 google scholar
31. Long, Y. (2025). Reimagining Translation Education: Fostering Student Engagement With AI Technologies in Cultural Translation Assessment. Journal of Language Teaching and Research, 16(5), 1506–1514. https://doi.org/10.17507/jltr.1605.07 google scholar
32. Lu Jie, Schmidt, M., & Shin Jinnie. (2025). Beyond Technological Usability: Exploratory Factor Analysis of the Comprehensive Assessment of Usability Scale for Learning Technologies (CAUSLT) Jie Lu**1*. google scholar
33. Mohammed, S., Samad, M. S., & Mahdi, S. (2020). The Attitudes of Professional Translators and Translation Students towards Computer-Assisted Translation Tools in Yemen. Journal of Language and Linguistic Studies, 16(2), 1084–1095. https://doi.org/10.17263/jlls.759371 google scholar
34. Munawwarah, M., & Martriwati, ; (2024). STUDENTS’ PERCEPTION OF USING DEEPL AS MACHINE TRANSLATION IN ENGLISH LEARNING. 5(2), 284–295. https://doi.org/10.22236/ellter.v5i2.15813.g4779 google scholar
35. Orfanou, K., Tselios, Ν., & Katsanos, C. (2015). Perceived usability evaluation of learning management systems: Empirical evaluation of the System Usability Scale. The International Review of Research in Open and Distributed Learning, 16(2). https://doi.org/10.19173/irrodl.v16i2.1955 google scholar
36. Özmat, D., & Akkoyunlu, B. (2024). Artificial Intelligence-Assisted Translation in Education: Academic Perspectives and Student Approaches. Participatory Educational Research, 11(Gift-Issue), 151–167. https://doi.org/10.17275/per.24.99.11.6 google scholar
37. Pietrzak, P. (2020). Inside and Outside the Translation Classroom. Research in Language, 18(2), 109–117. https://doi.org/10.18778/1731-7533.18.2.07 google scholar
38. 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/10.1016/j.heliyon.2023.e18595 google scholar
39. Prado, M. C. de A. P. (2024). Promoting AI Literacy among English Language and Literature and Translation Majors. Innovative Teaching and Learning, 6(1), 7–25. https://doi.org/10.4208/itl.20240103 google scholar
40. Preciado, J. S. A., & Vallejo, R. G. (2025). Self-Efficacy and Perception in the Use of GAI Tools in the Translation Classroom. Seminars in Medical Writing and Education, 4(526). https://doi.org/10.56294/mw2025526 google scholar
41. Rapa, A. A., Saja, I., & Azmi, A. A. A. (2024). The Use of Artificial Intelligence (AI) Translation Tools: Implications for Third Language Proficiency. International Journal of Research and Innovation in Social Science, VIII(IX), 1952–1960. https://doi.org/10.47772/IJRISS google scholar
42. Şahin, M., & Aybek, E. (2020). Jamovi: An Easy to Use Statistical Software for the Social Scientists. International Journal of Assessment Tools in Education, 6(4), 670–692. https://doi.org/10.21449/ijate.661803 google scholar
43. Salinas, M. J. V., & Burbat, R. (2023). Google Translate and DeepL: breaking taboos in translator training. Observational study and analysis. Iberica, 2023(45), 243–266. https://doi.org/10.17398/2340-2784.45.243 google scholar
44. Son, J., & Kim, B. (2023). Translation performance from the user’s perspective of large language models and neural machine translation systems. Information, 14(10), 574. https://doi.org/10.3390/info14100574 google scholar
45. Su, W., & Li, D. (2023). The effectiveness of translation technology training: a mixed methods study. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02066-2 google scholar
46. Sugiyama, K., & Yamanaka, T. (2023). Proposals and Methods for Foreign Language Learning Using Machine Translation and Large Language Model. Procedia Computer Science, 225, 4750–4757. https://doi.org/10.1016/j.procs.2023.10.474 google scholar
47. Tian, S., Jia, L., & Zhang, Z. (2023). Investigating students’ attitudes towards translation technology: The status quo and structural relations with translation mindsets and future work self. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1122612 google scholar
48. Vlachogianni, P., & Tselios, N. (2023). Perceived Usability Evaluation of Educational Technology Using the Post-Study System Usability Questionnaire (PSSUQ): A Systematic Review. Sustainability, 15(17), 12954. https://doi.org/10.3390/su151712954 google scholar
49. Wang, Y. X., Chen, L. P., & Han, J. Y. (2024). Exploring factors influencing students’ willingness to use translation technology. Education and Information Technologies, 29(13), 17097–17118. https://doi.org/10.1007/s10639-024-12511-7 google scholar
50. Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers and Education, 133, 116–126. https://doi.org/10.1016/j.compedu.2019.01.015 google scholar
51. Zaghlool, Z. D. M., & Khasawneh, M. A. S. (2023). Aligning Translation Curricula with Technological Advancements; Insights from Artificial Intelligence Researchers and Language Educators. Studies in Media and Communication, 12(1). https://doi.org/10.11114/smc google scholar
52. Zhang, J., & Doherty, S. (2025). Investigating novice translation students’ AI literacy in translation education. The Interpreter and Translator Trainer, 19(3–4), 234–253. https://doi.org/10.1080/1750399X.2025.2541478 google scholar