Reflective Regulation of AI-Assisted Translation: Longitudinal Development of Technology Competence and Critical AI Literacy in an Indonesian EFL Classroom

Authors

DOI:

10.70211/ltsm.3026-7196.622

Published:

2026-09-15

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Abstract

Artificial intelligence (AI) is now routinely used in translation education, but frequent use does not demonstrate critical or professionally responsible technology competence. This study examined how 20 undergraduate EFL translation students regulated AI assistance during a one-semester course. A qualitative-dominant embedded mixed-methods design followed six Indonesian-to-English translation phases structured by Assessment-as-Learning and repeated Plan–Monitor–Evaluate reflection. The dataset comprised 120 reflective journals, phase-based Likert responses used only as descriptive corroboration, and participant-observation notes. Longitudinal thematic analysis began with inductive coding and then interpreted cross-phase patterns through the European Master’s in Translation Technology Competence framework and metacognitive regulation. In Phase 1, 11 students articulated no AI-use plan, six offered vague plans, and three described relatively explicit plans; AI was used mainly for post-draft correction. In Phases 2–3, students increasingly compared, modified, or rejected suggestions according to meaning, grammatical accuracy, academic register, and contextual fit. In Phases 4–6, reflections more consistently defined human authorship and accountability and positioned AI as a verification resource rather than a primary text generator. The trajectory was cohort-dominant rather than uniform, and the design does not establish that reflection alone caused the change. The findings conceptualize technology competence as the reflective regulation of language-mediated human–AI interaction and identify structured metacognitive reflection as a plausible way to develop critical AI literacy in translation education.

Keywords:

AI-Assisted Translation Critical AI Literacy EFL Translation Machine Translation Literacy Metacognition Technology Competence

References

[1] S. Ali and M. Jarrah, “From benchmarking to human–AI collaboration: A systematic review of ChatGPT in translation studies (2022–2025),” Frontiers in Education, vol. 11, 2026, Art. no. 1847081. https://doi.org/10.3389/feduc.2026.1847081

[2] S. Chen and T. Zhou, “Machine translation and post-editing in translator training: A systematic review of integration models, challenges, and pedagogical implications,” Humanities and Social Sciences Communications, 2026. https://doi.org/10.1057/s41599-026-08740-5

[3] J.-B. Son, N. K. Ružić, and A. Philpott, “Artificial intelligence technologies and applications for language learning and teaching,” Journal of China Computer-Assisted Language Learning, vol. 5, no. 1, pp. 94–112, 2025. https://doi.org/10.1515/jccall-2023-0015

[4] M. Muftah, “The impact of artificial intelligence (AI) on translation students’ training practices: A case study of ChatGPT translation output,” Computer Assisted Language Learning, 2025. https://doi.org/10.1080/09588221.2025.2599148

[5] Y. Guo and Z. Lin, “Generative AI as a catalyst for data-driven learning: Efficacy, equity, and engagement in translation education,” SAGE Open, vol. 15, no. 4, 2025. https://doi.org/10.1177/21582440251406099

[6] Y. Yao, T. Han, and D. Li, “Measuring translation trainees’ effort in AI-assisted post-editing: A multi-method approach,” The Interpreter and Translator Trainer, vol. 19, nos. 3–4, pp. 357–378, 2025. https://doi.org/10.1080/1750399X.2025.2535239

[7] N. Abdelaal, “Register and pragmatic adjustment in post-editing machine-assisted translations: A cultural-pragmatic study of Arabic–English student translators,” Humanities and Social Sciences Communications, vol. 13, Art. no. 1425, 2026. https://doi.org/10.1057/s41599-026-07546-9

[8] Y. Hao, “The mirror and the scaffold: Cognitive shifts and identity reconstruction in AI-assisted translation education,” Innovation in Language Learning and Teaching, 2026. https://doi.org/10.1080/17501229.2026.2659787

[9] A. Grieve, A. Rouhshad, E. Petraki, A. Bechaz, and D. W. Dai, “Nursing and midwifery students’ ethical views on the acceptability of using AI machine translation software to write university assignments,” Journal of English for Academic Purposes, vol. 70, Art. no. 101379, 2024. https://doi.org/10.1016/j.jeap.2024.101379

[10] S. O’Brien and M. Ehrensberger-Dow, “MT literacy—A cognitive view,” Translation, Cognition & Behavior, vol. 3, no. 2, pp. 145–164, 2020. https://doi.org/10.1075/tcb.00038.obr

[11] L. Bowker, “Machine translation literacy instruction for international business students and business English instructors,” Journal of Business & Finance Librarianship, vol. 25, nos. 1–2, pp. 25–43, 2020. https://doi.org/10.1080/08963568.2020.1794739

[12] M. Ehrensberger-Dow, A. Delorme Benites, and C. Lehr, “A new role for translators and trainers: MT literacy consultants,” The Interpreter and Translator Trainer, vol. 17, no. 3, pp. 393–411, 2023. https://doi.org/10.1080/1750399X.2023.2237328

[13] S. Öner Bulut and N. Alimen, “Translator education as a collaborative quest for insights into the re-positioning of the human translator (educator) in the age of machine translation,” The Interpreter and Translator Trainer, vol. 17, no. 3, pp. 375–392, 2023. https://doi.org/10.1080/1750399X.2023.2237837

[14] M. D. High, A. McIntosh, S. Li, and Y. Ji, “Student machine translation use in a transnational English-medium instruction university: Navigating development and expedience,” Journal of English-Medium Instruction, vol. 4, no. 2, pp. 238–258, 2025. https://doi.org/10.1075/jemi.24010.hig

[15] K. Liu, H. L. Kwok, J. Liu, and A. K. F. Cheung, “Sustainability and influence of machine translation: Perceptions and attitudes of translation instructors and learners in Hong Kong,” Sustainability, vol. 14, no. 11, Art. no. 6399, 2022. https://doi.org/10.3390/su14116399

[16] M. Kruk, “Investigating the role of AI tools in enhancing translation skills, emotional experiences, and motivation in L2 learning,” European Journal of Education, vol. 60, no. 1, e12859, 2025. https://doi.org/10.1111/ejed.12859

[17] H. M. Alotaibi and A. Salamah, “The impact of translation applications on translation students’ performance,” Education and Information Technologies, vol. 28, 2023. https://doi.org/10.1007/s10639-023-11578-y

[18] X. Chen, “Students helping Google Translate to learn how to improve its results,” Current Trends in Translation Teaching and Learning E, vol. 8, pp. 449–482, 2021. https://doi.org/10.51287/cttle2021516

[19] F. Li, “A survey of translation learners’ uses and perceptions of neural machine translation,” Theory and Practice in Language Studies, vol. 13, no. 11, pp. 3039–3048, 2023. https://doi.org/10.17507/tpls.1311.34

[20] M. Yamada, “The impact of Google Neural Machine Translation on post-editing by student translators,” The Journal of Specialised Translation, no. 31, pp. 87–106, 2019. https://doi.org/10.26034/cm.jostrans.2019.178

[21] Y. Jia, M. Carl, and X. Wang, “How does the post-editing of neural machine translation compare with from-scratch translation? A product and process study,” The Journal of Specialised Translation, no. 31, pp. 60–86, 2019. https://doi.org/10.26034/cm.jostrans.2019.177

[22] M. Flanagan and T. P. Christensen, “Testing post-editing guidelines: How translation trainees interpret them and how to tailor them for translator training purposes,” The Interpreter and Translator Trainer, vol. 8, no. 2, pp. 257–275, 2014. https://doi.org/10.1080/1750399X.2014.936111

[23] I. Robert, I. Schrijver, and J. J. J. Ureel, “Measuring translation revision competence and post-editing competence in translation trainees: Methodological issues,” Perspectives, vol. 32, no. 2, pp. 177–191, 2024. https://doi.org/10.1080/0907676X.2022.2030377

[24] M. del M. Sánchez Ramos, “Public service interpreting and translation training: A path towards digital adaptation to machine translation and post-editing,” The Interpreter and Translator Trainer, vol. 16, no. 3, pp. 294–308, 2022. https://doi.org/10.1080/1750399X.2022.2092829

[25] B. Klimova, M. Pikhart, A. Delorme Benites, C. Lehr, and S. Sanchez-Stockhammer, “Neural machine translation in foreign language teaching and learning: A systematic review,” Education and Information Technologies, vol. 28, pp. 663–682, 2023. https://doi.org/10.1007/s10639-022-11194-2

[26] X. Deng and Z. Yu, “A systematic review of machine-translation-assisted language learning,” Sustainability, vol. 14, no. 13, Art. no. 7598, 2022. https://doi.org/10.3390/su14137598

[27] C. K. Y. Chan and W. Hu, “Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education,” International Journal of Educational Technology in Higher Education, vol. 20, 2023. https://doi.org/10.1186/s41239-023-00411-8

[28] V. Chan and W. K.-W. Tang, “GPT for translation: A systematic literature review,” SN Computer Science, vol. 5, no. 8, Art. no. 986, 2024. https://doi.org/10.1007/s42979-024-03340-z

[29] D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, vol. 61, no. 2, pp. 228–239, 2024. https://doi.org/10.1080/14703297.2023.2190148

[30] M. Almhasees, K. K. Mohsen, and M. O. Amin, “Student perceptions of ChatGPT in translation,” Language Value, vol. 17, no. 2, pp. 1–23, 2024. https://doi.org/10.6035/languagev.7925

[31] A. Ginel and J. Moorkens, “Translator attitudes toward the adoption of ChatGPT,” Tradumàtica, no. 22, pp. 258–275, 2024. https://doi.org/10.5565/rev/tradumatica.369

[32] A. Ginel and J. Moorkens, “Translators’ trust and distrust in generative AI,” Translation Studies, vol. 18, no. 2, pp. 283–299, 2025. https://doi.org/10.1080/14781700.2025.2507594

[33] A. Latorraca, “Lost in post-editing: An exploratory study on translation trainees’ perceived EN>IT post-editing vs. translation performance,” Ampersand, vol. 11, Art. no. 100144, 2023. https://doi.org/10.1016/j.amper.2023.100144

[34] M. Algaraady and M. Mahyoob, “Augmenting post-editing across domains with ChatGPT,” Frontiers in Artificial Intelligence, vol. 8, Art. no. 1526293, 2025. https://doi.org/10.3389/frai.2025.1526293

[35] V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qualitative Research in Psychology, vol. 3, no. 2, pp. 77–101, 2006. https://doi.org/10.1191/1478088706qp063oa

[36] J. H. Flavell, “Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry,” American Psychologist, vol. 34, no. 10, pp. 906–911, 1979. https://doi.org/10.1037/0003-066X.34.10.906

[37] B. J. Zimmerman, “Becoming a self-regulated learner: An overview,” Theory Into Practice, vol. 41, no. 2, pp. 64–70, 2002. https://doi.org/10.1207/s15430421tip4102_2

[38] E. Panadero, “A review of self-regulated learning: Six models and four directions for research,” Frontiers in Psychology, vol. 8, Art. no. 422, 2017. https://doi.org/10.3389/fpsyg.2017.00422

[39] G. Saldanha and S. O’Brien, Research Methodologies in Translation Studies. London, U.K.: Routledge, 2013. https://doi.org/10.4324/9781315760100

[40] C. D. Mellinger and T. A. Hanson, Quantitative Research Methods in Translation and Interpreting Studies. New York, NY, USA: Routledge, 2016. https://doi.org/10.4324/9781315647844

[41] M. Haiyudi, H. Pratama, and S. Art-in, “Indonesian university students’ engagement with post-editing machine translation,” ELT Forum: Journal of English Language Teaching, vol. 12, no. 2, pp. 90–97, 2023. https://doi.org/10.15294/elt.v12i2.67163

[42] W. Alharbi, “The use and abuse of artificial intelligence-enabled machine translation in the EFL classroom: An exploratory study,” Journal of Education and e-Learning Research, vol. 10, no. 4, pp. 689–701, 2023. https://doi.org/10.20448/jeelr.v10i4.5091

[43] L. Cai, H. Tan, and M. Huang, “Technology competence training of translation educators in the age of technology-and-information empowerment: A Chinese perspective,” SAGE Open, vol. 15, no. 3, 2025. https://doi.org/10.1177/21582440251365384

[44] A. Alkhofi, “Man vs. machine: Can AI outperform ESL student translations?” Frontiers in Artificial Intelligence, vol. 8, Art. no. 1624754, 2025. https://doi.org/10.3389/frai.2025.1624754

[45] C. Combrinck and N. Loubser, “Student self-reflection as a tool for managing GenAI use in large class assessment,” Discover Education, vol. 4, Art. no. 72, 2025. https://doi.org/10.1007/s44217-025-00461-2

[46] X. Quan and Y. Sun, “Artificial intelligence-powered evaluation model for English translation education in university: Combining quantitative and qualitative methods,” Scientific Reports, vol. 16, Art. no. 15896, 2026. https://doi.org/10.1038/s41598-026-46314-2

[47] European Commission, European Master’s in Translation (EMT) Competence Framework 2022, 2022.

Author Biographies

Sri Widyarti Ali, Universitas Negeri Gorontalo

Author Origin : Indonesia

Karmila Machmud, Universitas Gorontalo

Author Origin : Indonesia

Novriyanto Napu, Universitas Gorontalo

Author Origin : Indonesia

Suleman Bouti, Universitas Gorontalo

Author Origin : Indonesia

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