Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)?

Authors

Keywords:

Generative AI, guidelines, higher education institutions, machine translation, policies

Abstract

Since the advent of ChatGPT and other automatic text generators, educators from many disciplines, including language learning and teaching, have published numerous articles exploring this technology’s “pitfalls and potentials” (Barrot, 2023) and offering recommendations based on their own practice to teachers, users, and institutional decision-makers. But it is early days yet, and, while recognising the need to offer guidelines, there is not enough scientific data to create evidence-based guidelines. Having been working on machine translation (MT) literacy (Bowker & Buitrago Ciro, 2019; Cotelli Kureth & Summers, 2023) for several years, we have developed guidelines for the use of machine translation (MT) tools in higher education, which have been implemented in a Swiss university. Given that MT tools share technical features with generative AI (GenAI) tools like ChatGPT, we believe that applied knowledge of the former could facilitate understanding of the latter. This article will draw on both our own experience and a thorough literature review of recommendations for the use of GenAI for higher education institutions (HEI) to map what guidelines on the use of GenAI should include and how they should be presented to teachers and users.

References

Allen, T., Villaflor-Wilson, R., Muljana, P., & Romero-Hall, E. (2024). AI-generated content: Guidelines, higher-order thinking skills, and copyrights. Educational TechnologyJournal, 4(1), 1‑5. https://journal.unesa.ac.id/index.php/etj

Alm, A., & Ohashi, L. (2024). A worldwide study on language educators’ initial response to ChatGPT. Technology in Language Teaching & Learning, 6(1), 1141. https://doi.org/10.29140/tltl.v6n1.1141

Alqahtani, N., & Wafula, Z. (2025). Artificial intelligence integration: Pedagogical strategies and policies at leading universities. Innovative Higher Education, 50, 665-684. https://doi.org/10.1007/s10755-024-09749-x

Aquino, K. C., Lalor, A. R., & Parnther, C. (2024a). Address accessibility in generative AI policy development. Campus Legal Advisor, 25(3), 4‑5. https://doi.org/10.1002/cala.41468

Aquino, K. C., Lalor, A. R., & Parnther, C. (2024b). Are institutions addressing accessibility in generative AI policy development? Disability Compliance for Higher Education, 30(2), 1-2. https://doi.org/10.1002/dhe.31804

Azevedo, L., Mallinson, D. J., Wang, J., Robles, P., & Best, E. (2024). AI policies, equity, and morality and the implications for faculty in higher education. Public Integrity, 1‑16. https://doi.org/10.1080/10999922.2024.2414957

Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1), 59. https://doi.org/10.1186/s41239-023-00427-043

Barrot, J. S. (2023). Using ChatGPT for second language writing: Pitfalls and potentials. Assessing Writing, 57, 100745. https://doi.org/10.1016/j.asw.2023.100745

Benites, F., Delorme Benites, A., & Anson, C. M. (2023). Automated text generation and summarization for academic writing. In O. Kruse, C. Rapp, C. M. Anson, K. Benetos, E. Cotos, A. Devitt, & A. Shibani (Eds.), Digital Writing Technologies in Higher Education (pp. 279‑301). Springer. https://doi.org/10.1007/978-3-031-36033-6_18BFH:

Bern University of Applied Sciences. (2023, July). Machine translation use in university contexts policy. https://tinyurl.com/mt7fjvrk

BFH: Bern University of Applied Sciences. (2023b, July). Checklist for the machine translation use in university contexts policy. https://tinyurl.com/27hapc77

Bowker, L. (2021). Promoting linguistic diversity and inclusion: Incorporating machine translation literacy into information literacy instruction for undergraduate students. The International Journal of Information, Diversity, & Inclusion, 5(3), 127-151. https://doi.org/10.33137/ijidi.v5i3.36159

Bowker, L. (2023). Translating research into practice: Plain language and writing for machine translation guidelines. Proceedings of the Association for Information Science and Technology, 60(1), 892‑894. https://doi.org/10.1002/pra2.889

Bowker, L., & Buitrago Ciro, J. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Group Publishing. https://doi.org/10.1108/9781787567214

Cacho, R. M. (2024). Integrating generative ai in university teaching and learning: Amodel for balanced guidelines. Online Learning Journal, 28(3), 55‑81. https://doi.org/10.24059/olj.v28i3.4508

Campbell, L. O., Frawley, C., Lambie, G. W., Cabrera, K. S., & Vizcarra, B. D. (2025). Examining artificial intelligence policies in counsellor education. Counselling and Psychotherapy Research, 25(1), e12880. https://doi.org/10.1002/capr.12880

Cardon, P., Fleischmann, C., Aritz, J., Logemann, M., & Heidewald, J. (2023). The Challenges and opportunities of AI-assisted writing: Developing AI literacy for the AI age. Business and Professional Communication Quarterly, 86(3), 257‑295. https://doi.org/10.1177/23294906231176517

Chaka, C., Shange, T., Nkhobo, T., & Hlatshwayo, V. (2024). An environmental review of the Generative Artificial Intelligence policies and guidelines of South African higher education institutions: A content analysis. International Journal of Learning, Teaching and Educational Research, 23(12), 487‑511. https://doi.org/10.26803/ijlter.23.12.25

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in HigherEducation, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3

Christ-Brendemühl, S. (2025). Leveraging generative AI in higher education: An analysis of opportunities and challenges addressed in university guidelines. European Journal of Education, 60(1), e12891. https://doi.org/10.1111/ejed.12891

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. In N. Froeliger, C. Larsonneur, & G. Sofo, Human translation and natural language processing towards a new consensus? (chap. 18221). Fondazione Università Ca’Foscari. https://doi.org/10.30687/978-88-6969-762-3/006

Cotelli Kureth, S., & Summers, E. (2023). Tackling the elephant in the language classroom: Introducing machine translation literacy in a Swiss language centre. Language Learning in Higher Education, 13(1), 213‑230. https://doi.org/10.1515/cercles-2023-2015

Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11(1), 1006. https://doi.org/10.1057/s41599-024-03526-z

Dai, Y., Lai, S., Lim, C. P., & Liu, A. (2024). University policies on generative AI inAsia: Promising practices, gaps, and future directions. Journal of Asian Public Policy, 1-22. https://doi.org/10.1080/17516234.2024.2379070

De Maio, C. (2024). Institutional responses to ChatGPT. Analysing the academic integrity of four public and private institutions of higher education in Australia. Journal of Academic Language and Learning, 18(1), T1‑T8. https://journal.aall.org.au/index.php/jall/article/view/917

Delorme Benites, A., Cotelli Kureth, S., Lehr, C., & Steele, E. (2021). Machine translation literacy: A panorama of practices at Swiss universities and implications for language teaching. In N. Zoghlami, C. Brudermann, C. Sarré, M. Grosbois, L. Bradley, &S. Thouësny (Eds.), CALL and professionalisation: Short papers from EUROCALL2021(pp. 80‑87). Research-publishing.net. https://doi.org/10.14705/rpnet.2021.54.1313

Delorme Benites, A., & Lehr, C. (2021). Neural machine translation and language teaching:

Possible implications for the CEFR. Bulletin suisse de linguistique appliquée, 114, 47-66. https://doi.org/10.5169/SEALS-1030137

Driessens, O., & Pischetola, M. (2024). Danish university policies on generative AI Problems, assumptions and sustainability blind spots. MedieKultur, 40(76), 31‑52. https://doi.org/10.7146/mk.v40i76.143595

Dusza, D. G. (2024). Machine translation in the writing process: Pedagogy, plagiarism, policy, and procedures. In S. E. Eaton (Ed.), Second handbook of academic integrity (pp. 1487‑1509). Springer. https://doi.org/10.1007/978-3-031-54144-5

Evangelista, E. D. L. (2025). Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education. Contemporary Educational Technology, 17(1), ep559. https://doi.org/10.30935/cedtech/15775

Foung, D., Lin, L., & Chen, J. (2024). Reinventing assessments with ChatGPT and other online tools: Opportunities for GenAI-empowered assessment practices. Computers and Education: Artificial Intelligence, 6, 100250. https://doi.org/10.1016/j.caeai.2024.100250

Giray, L. (2024). The problem with false positives : AI detection unfairly accuses scholars of AI plagiarism. The Serials Librarian, 85(5‑6), 181‑189. https://doi.org/10.1080/0361526X.2024.2433256

González Pastor, D. (2021). Introducing machine translation in the translation classroom: A survey on students’ attitudes and perceptions. Tradumàtica: technologies de la traducció, 19, 47‑65. https://doi.org/10.5565/rev/tradumatica.273

Hamerman, E. J., Aggarwal, A., & Martins, C. (2025). An investigation of generativeAI in the classroom and its implications for university policy. Quality Assurance in Education, 33(2), 253‑266. https://doi.org/10.1108/QAE-08-2024-0149

Hellmich, E. A. (2021). Machine Translation in foreign language writing: Student use to guide pedagogical practice. Apprentissage des langues et systèmes d’informationet decommunication, 24(1). https://doi.org/10.4000/alsic.5705

Hua, S., Jin, S., & Jiang, S. (2024). The limitations and ethical considerations of ChatGPT. Data Intelligence, 6(1), 201‑239. https://doi.org/10.1162/dint_a_00243

Irfan, M., Murray, L., & Sajjad, D. (2023). The role of AI in shaping Europe’s higher education landscape: Policy implications and guidelines with a focus on Ireland. Research Journal of Social Sciences & Economics Review, 4(2), 231-243. https://doi.org/10.36902/rjsser-vol4-iss2-2023(231-243)

Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and education. Artificial intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348

Jolley, J. R., & Maimone, L. (2022). Thirty years of machine translation in language teaching and learning: A review of the literature. L2 Journal, 14(1), 26‑44. https://doi.org/10.5070/L214151760

Kam, S., & Kim, M. (2024). A study on the development of AI utilization guide components at a Christian university. Journal of Christian Education in Korea, 77, 171‑201. https://doi.org/10.17968/JCEK.2024.77.009

Klimova, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez-Stockhammer, C. (2023). Neural machine translation in foreign language teaching and learning: Asystematic review. Education and Information Technologies, 28(1), 663‑682. https://doi.org/10.1007/s10639-022-11194-2

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537‑550. https://doi.org/10.1177/00336882231162868

Kohnke, L., Zou, D., & Moorhouse, B. L. (2024). Technostress and English language teaching in the age of generative AI. Educational Technology Journal, 27(2), 306‑320. https://www.jstor.org/stable/48766177

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

Kurokawa, S. & Salingre, M. (2025). Syntactic and Lexical Comparison Between AI-Generated Reading Passages and Japanese Universities' National Test. Journal of Studies in Language, Culture, and Society (JSLCS), 8(1).

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacyin higher and adult education: A scoping literature review. Computers and Education:Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101

Loock, R., & Léchauguette, S. (2021). Machine translation literacy and undergraduate students in applied languages: Report on an exploratory study. Tradumàtica: tecnologies de la traducció, 19, 204‑225. https://doi.org/10.5565/rev/tradumatica.281

Loock, R., Léchauguette, S., & Holt, B. (2022). The use of online translators by students not enrolled in a professional translation program: beyond copying and pasting for a professional use. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 23-29). European Association for MachineTranslation. https://aclanthology.org/2022.eamt-1.5/

Luo, J. (Jess). (2024). A critical review of GenAI policies in higher education assessment: A call to reconsider the “originality” of students’ work. Assessment &Evaluation in Higher Education, 49(5), 651‑664. https://doi.org/10.1080/02602938.2024.2309963

Massardo, I., van der Meer, J., O’Brien, S., Hollowood, F., Aranberri, N., &Drescher, K. (2016). MT Post-Editing Guidelines. https://www.taus.net/insights/reports/taus-post-editing-guidelines

Marcel, F., & Kang, P. (2024). Examining AI guidelines in Canadian universities: Implications on academic integrity in academic writing. Discourse and Writing/Rédactologie, 34, 93‑126. https://doi.org/10.31468/dwr.1051

Martindale, M. J., & Carpuat, M. (2018). Fluency over adequacy: A pilot study in measuring user trust in imperfect MT. arXiv. https://doi.org/10.48550/ARXIV.1802.06041

McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, 100121. https://doi.org/10.1016/j.chbah.2025.100121

Mehar Singh, M. K., & Phan Kar Jun, J. (2024). Examining exemplar elements of selected Malaysian higher education institutions’ academic integrity policy: document analysis based evidence. Journal of Nusantara Studies (JONUS), 9(1), 269‑293. https://doi.org/10.24200/jonus.vol9iss1pp269-293

Michalak, R. (2023). From ethics to execution: The role of academic librarians in Artificial Intelligence (AI) policy-making at colleges and Universities. Journal of Library Administration, 63(7), 928‑938. https://doi.org/10.1080/01930826.2023.2262367

Moore, S., & Lookadoo, K. (2024). Communicating clear guidance: Advice for Generative AI policy development in higher education. Business and Professional Communication Quarterly, 87(4), 610‑629. https://doi.org/10.1177/23294906241254786

Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5, 1000151. https://doi.org/10.1016/j.caeo.2023.100151

Mundt, K., & Groves, M. (2016). A double-edged sword: The merits and the policy implications of Google Translate in higher education. European Journal of HigherEducation, 6(4), 387‑401. https://doi.org/10.1080/21568235.2016.1172248

Nassau, G., Molle, N., & Kalyaniwala, C. (2022). Usages et perceptions des outils de traduction automatique: une enquête auprès d’apprenants Lansad. Apprentissage des langues et systèmes d’information et de communication, 25(2). https://doi.org/10.4000/alsic.6239

Niraula, S. (2024). The impact of ChatGPT on Academia: A comprehensive analysis of AI policies across UT system academic institutions. Advances in Mobile Learning Educational Research, 4(1), 973‑982. https://doi.org/10.25082/AMLER.2024.01.009

Nurminen, M. (2019). Decision-making, risk, and gist machine translation in the work of patent professionals. Proceedings of the 8th Workshop on Patent and Scientific Literature Translation (pp. 32‑42). https://aclanthology.org/W19-7204.pdf

O’Brien, S., & Ehrensberger-Dow, M. (2020). MT literacy – A cognitive view. Translation, Cognition & Behavior, 3(2), 145‑164. https://doi.org/10.1075/tcb.00038.obr

Omeh, C. B., Olelewe, C. J., & Hu, X. (2025). Application of artificial intelligence (AI) technology in TVET education: Ethical issues and policy implementation. Educationand Information Technologies, 30, 5989-6018. https://doi.org/10.1007/s10639-024-13018-x

O’Neill, E.M. (2019). Online translator, dictionary, and search engine use among L2 students. Computer-Assisted Language Learning-Electronic Journal, 20(1), 154-177. https://old.callej.org/journal/20-1/O%27Neill2019.pdf

Paterson, K. (2023). Machine translation in higher education: Perceptions, policy, and pedagogy. TESOL Journal, 14(2), e690. https://doi.org/10.1002/tesj.690

Perera, P., & Lankathilake, M. (2023). Preparing to revolutionize education with the multi-model GenAI tool Google Gemini? A journey towards effective policy making. Journal of Advances in Education and Philosophy, 7(8), 246‑253. https://doi.org/10.36348/jaep.2023.v07i08.001

Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2024). Detectionof GPT-4 generated text in higher education: Combining academic judgement and software to identify Generative AI tool misuse. Journal of Academic Ethics, 22(1), 89‑113. https://doi.org/10.1007/s10805-023-09492-6

Piedad, E. Jr., Tabud, S. L. C., Aline-Llabdo, J. A., Danao, D. A., Gironella, Ma. C. A., Lim, J., Francisco, A. S., & Yong, T. K. (2024). Regulating Generative AI in scholarly works: A policy brief for academic institutions in the Philippines. De La Salle University Publishing House. https://animorepository.dlsu.edu.ph/apipmibookseries/6

Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), 100062. https://doi.org/10.1016/j.chbah.2024.100062

Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging research and policy themes on academic integrity in the age of ChatGPT and Generative AI. Asian Journal of University Education, 19(4), 743‑758. https://doi.org/10.24191/ajue.v19i4.24697

Rana, N. K. (2025). Generative AI and academic research: A review of the policies from selected HEIs. Higher Education for the Future, 12(1), 97‑113. https://doi.org/10.1177/23476311241303800

Resende, N., & Way, A. (2021). Can Google Translate rewire your L2 English processing? Digital, 1(1), 66‑85. https://doi.org/10.3390/digital1010006

Sánchez-Gijón, P., & Kenny, D. (2022). Selecting and preparing texts for machine translation: Pre-editing and writing for a global audience. In D. Kenny (Ed.) Machine translation for everyone. Empowering users in the age of artificial intelligence (pp. 81-103). Language Science Press. https://doi.org/10.5281/zenodo.6759980

Sarin, S., & Kimkong, H. (2024). Generative AI in higher education: The need to develop or revise academic integrity policies to ensure the ethical use of AI in Cambodia. Cambodia Development Center, 6(1). https://www.cd-center.org/generative-ai-in-higher-education-the-need-to-develop-or-revise-academic-integrity-policies-to-ensurethe-ethical-use-of-ai-in-cambodia/

Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., &Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755‑759. https://doi.org/10.1038/s41586-024-07566-y

Sui, S. C. (2025). Revolutionising Translation with AI: Unravelling Neural Machine Translation and Generative Pre-Trained Large Language Models. In Y. Peng, H. Huang, & D. Li (Eds.), New Advances in Translation Technology (pp. 29-54). Springer. https://doi.org/10.1007/978-981-97-2958-6_3

Suonpää, M., Heikkilä, J., & Dimkar, A. (2024). Students’ perceptions of generative AI usageand risks in a Finnish higher education institution. In INTED 2024 Proceedings (pp. 3071‑3077). https://doi.org/10.21125/inted.2024.0825

Ullah, M., Bin Naeem, S., & Kamel Boulos, M. N. (2024). Assessing the Guidelines on the Use of Generative Artificial Intelligence Tools in Universities: A Survey of the World’s Top 50 Universities. Big Data and Cognitive Computing, 8(12), 194. https://doi.org/10.3390/bdcc8120194

Vanmassenhove, E., Hardmeier, C., & Way, A. (2018). Getting gender right in neural machine translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 3003‑3008). https://aclanthology.org/D18-1334.pdf

Vanmassenhove, E., Shterionov, D., & Way, A. (2019). Lost in translation: Loss and decay of linguistic richness in machine translation. In Proceedings of MT Summit XVII (vol. 1, pp. 222‑232). https://aclanthology.org/W19-6622.pdf

Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, 100326. https://doi.org/10.1016/j.caeai.2024.100326

Downloads

Published

2025-05-25

How to Cite

Cotelli Kureth, S. ., & Summers, E. . (2025). Policy On The Use Of Machine Translation (mt): A Good Model For Wider Policies On Generative Ai (genai)?. The Journal of Studies in Language, Culture, and Society, 8(1), 30–48. Retrieved from https://univ-bejaia.dz/revue/jslcs/article/view/574