From the subjunctive to the ergative: A narrative review of AI mediation in Spanish-Hindi literary translation and its impact on translation competence in university teaching

Sabyasachi Mishra

doi:10.58763/rc2026613

Autores/as

Sabyasachi Mishra Vellore Institute of Technology. Vellore, India https://orcid.org/0000-0001-6676-8854

DOI:

https://doi.org/10.58763/rc2026613

Palabras clave:

Artificial intelligence, Higher education, Linguistics, Literary analysis, Multilingualism

Resumen

Introduction: The expansion of artificial intelligence has transformed translation and translator training, especially through neural networks and large-scale language models. In literary translation, these advances pose additional challenges due to the need to preserve linguistic, pragmatic, cultural, and stylistic dimensions. The aim was to critically analyze the mediation of AI in literary translation, with a special focus on Spanish–Hindi and its educational implications. Methodology: A narrative, qualitative, interdisciplinary, and diachronic review was conducted for the period 1990–2026. The final corpus consisted of 27 studies selected through purposive and theoretical sampling, organized around five axes: linguistic divergence, pragmatic-cultural transfer, technological evolution, translator competence and cognitive load, and voice, authorship, and agency. Results: The evidence showed that technological advances do not eliminate linguistic divergences nor guarantee stylistic preservation. LLMs expand creative possibilities, but they can also introduce unjustified expansions. Furthermore, technological development shifts human intervention from correction to evaluation, verification, and decision-making. In education, this process demands strengthening critical AI literacy, metacognitive regulation, and argumentative skills. Conclusions: Spanish-Hindi literary translation remains insufficiently studied. Specialized corpora, empirical evaluations, and educational designs that conceive of AI as a heuristic mediator under human governance are needed.

Citas

Al-Batineh, M., & Al Tenaijy, M. (2024). Adapting to technological change: An investigation of translator training and the translation market in the Arab world. Heliyon, 10(7), e28535. https://doi.org/10.1016/j.heliyon.2024.e28535

Bhattacharjee, S., Gain, B., & Ekbal, A. (2024). Domain Dynamics: Evaluating Large Language Models in English-Hindi Translation. En B. Haddow, T. Kocmi, P. Koehn, & C. Monz (Eds.), Proceedings of the Ninth Conference on Machine Translation (pp. 341–354). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.wmt-1.27

Bodart, R., Piette, J., & Lefer, M.-A. (2024). The Machine Translation Post-Editing Annotation System (MTPEAS): A standardized and user-friendly taxonomy for student post-editing quality assessment. Translation Spaces, 13(2), 265–292. https://doi.org/10.1075/ts.24002.bod

Bojar, O., Straňák, P., & Zeman, D. (2010). Data Issues in English-to-Hindi Machine Translation. En N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, & D. Tapias (Eds.), Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10). European Language Resources Association (ELRA). https://aclanthology.org/L10-1524/

Cook, A., Knaggs, A., & Sun, J. (2025). Exploring the goals of translation education: Adding human value to literary translation through deep meaning making and artistry. The Interpreter and Translator Trainer, 19(3–4), 254–276. https://doi.org/10.1080/1750399X.2025.2543208

Dave, S., Parikh, J., & Bhattacharyya, P. (2001). Interlingua-based English–Hindi Machine Translation and Language Divergence. Machine Translation, 16(4), 251–304. https://doi.org/10.1023/A:1021902704523

Diels, E., Ureel, J. J. J., Robert, I. S., & Strobl, C. (2025). The effects of corpus-focused instruction on the development of stylistic translation revision competence in future translators. Translation Spaces, 14(2), 221–252. https://doi.org/10.1075/ts.24038.die

Du, S., Arenas, A. G., Toral, A., Gerrits, K., & Borillo, J. M. (2025). Optimising ChatGPT for creativity in literary translation: A case study from English into Dutch, Chinese, Catalan and Spanish. En P. Bouillon, J. Gerlach, S. Girletti, L. Volkart, R. Rubino, R. Sennrich, A. C. Farinha, M. Gaido, J. Daems, D. Kenny, H. Moniz, & S. Szoc (Eds.), Proceedings of Machine Translation Summit XX: Volume 1 (pp. 578–591). European Association for Machine Translation. https://aclanthology.org/2025.mtsummit-1.44/

Ehrensberger-Dow, M., Delorme Benites, A., & Lehr, C. (2023). A new role for translators and trainers: MT literacy consultants. The Interpreter and Translator Trainer, 17(3), 393–411. https://doi.org/10.1080/1750399X.2023.2237328

Fan, A., Bhosale, S., Schwenk, H., Ma, Z., El-Kishky, A., Goyal, S., Baines, M., Celebi, O., Wenzek, G., Chaudhary, V., Goyal, N., Birch, T., Liptchinsky, V., Edunov, S., Auli, M., & Joulin, A. (2021). Beyond English-Centric Multilingual Machine Translation. Journal of Machine Learning Research, 22(107), 1–48. http://jmlr.org/papers/v22/20-1307.html

Freeth, P. J., & Toto, P. (2026). The impact of AI-in-the-loop pedagogy: An action research approach to integrating AI technologies in translator training. The Interpreter and Translator Trainer, 1–18. https://doi.org/10.1080/1750399X.2026.2646863

González-Pastor, D. (2024). La traducción automática y la formación de traductores en España: Perspectivas desde la industria y el ámbito académico. Mutatis Mutandis. Revista Latinoamericana de Traducción, 17(1). https://doi.org/10.17533/udea.mut.v17n1a06

Goyal, N., Gao, C., Chaudhary, V., Chen, P.-J., Wenzek, G., Ju, D., Krishnan, S., Ranzato, M., Guzmán, F., & Fan, A. (2022). The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation. Transactions of the Association for Computational Linguistics, 10, 522–538. https://doi.org/10.1162/tacl_a_00474

Goyal, V., Mishra, P., & Sharma, D. M. (2020). Linguistically Informed Hindi-English Neural Machine Translation. En N. Calzolari, F. Béchet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Eds.), Proceedings of the Twelfth Language Resources and Evaluation Conference (pp. 3698–3703). European Language Resources Association. https://aclanthology.org/2020.lrec-1.456/

Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Translation Spaces, 11(2), 184–212. https://doi.org/10.1075/ts.21025.gue

Guerberof-Arenas, A., & Toral, A. (2024). To be or not to be: A translation reception study of a literary text translated into Dutch and Catalan using machine translation. Target. International Journal of Translation Studies, 36(2), 215–244. https://doi.org/10.1075/target.22134.gue

Guerberof-Arenas, A., Valdez, S., & Dorst, A. G. (2024). Does training in post-editing affect creativity? The Journal of Specialised Translation, (41), 74–97. https://doi.org/10.26034/cm.jostrans.2024.4712

He, S., Hao, Y., Liu, S., Liu, H., & Li, H. (2022). Research on translation technology teaching in Chinese publications and in international English-language publications (1999-2020): A bibliometric analysis. The Interpreter and Translator Trainer, 16(3), 275–293. https://doi.org/10.1080/1750399X.2022.2101848

He, Y., & Tao, Y. (2022). Unity of knowing and acting: An empirical study on a curriculum approach to developing students’ translation technological thinking competence. The Interpreter and Translator Trainer, 16(3), 348–366. https://doi.org/10.1080/1750399X.2022.2101849

Huang, Y., & Cheung, A. K. F. (2026). Exploring AI’s performance in literary autobiography translation: How closely do AI models match human translation. Humanities and Social Sciences Communications, 13(1), 518. https://doi.org/10.1057/s41599-026-06630-4

Ibáñez Moreno, A., & Domínguez Mora, M. E. (2025). Google Translate versus DeepL in Spanish to English translation of Don Quixote. Translation and Translanguaging in Multilingual Contexts, 11(1), 65–87. https://doi.org/10.1075/ttmc.00154.iba

Jiao, H., Hu, W., & Zhang, X. (2025). To eat or to feed: Can large language models provide useful feedback in translation education? The Interpreter and Translator Trainer, 19(3–4), 317–337. https://doi.org/10.1080/1750399X.2025.2533074

Jiménez-Crespo, M. A. (2025). “If students translate like a robot … ” or how research on human-centered AI and intelligence augmentation can help realign translation education. The Interpreter and Translator Trainer, 19(3–4), 277–295. https://doi.org/10.1080/1750399X.2025.2542022

Karpinska, M., & Iyyer, M. (2023). Large Language Models Effectively Leverage Document-level Context for Literary Translation, but Critical Errors Persist. Proceedings of the Eighth Conference on Machine Translation, 419–451. https://doi.org/10.18653/v1/2023.wmt-1.41

Kenny, D., & Winters, M. (2020). Machine translation, ethics and the literary translator’s voice. Translation Spaces, 9(1), 123–149. https://doi.org/10.1075/ts.00024.ken

Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024). AI literacy and its implications for prompt engineering strategies. Computers and Education: Artificial Intelligence, 6, 100225. https://doi.org/10.1016/j.caeai.2024.100225

Krüger, R., & Hackenbuchner, J. (2024). A competence matrix for machine translation-oriented data literacy teaching. Target. International Journal of Translation Studies, 36(2), 245–275. https://doi.org/10.1075/target.22127.kru

Kwok, H. L., Shi, Y., Xu, H., Li, D., & Liu, K. (2025). GenAI as a translation assistant? A corpus-based study on lexical and syntactic complexity of GPT-post-edited learner translation. System, 130, 103618. https://doi.org/10.1016/j.system.2025.103618

Łoboda, K., & Mastela, O. (2023). Machine translation and culture-bound texts in translator education: A pilot study. The Interpreter and Translator Trainer, 17(3), 503–525. https://doi.org/10.1080/1750399X.2023.2238328

Ma, Q., Crosthwaite, P., Sun, D., & Zou, D. (2024). Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach. Computers and Education: Artificial Intelligence, 7, 100278. https://doi.org/10.1016/j.caeai.2024.100278

Macken, L., Vanroy, B., Desmet, L., & Tezcan, A. (2022). Literary translation as a three-stage process: Machine translation, post-editing and revision. En H. Moniz, L. Macken, A. Rufener, L. Barrault, M. R. Costa-jussà, C. Declercq, M. Koponen, E. Kemp, S. Pilos, M. L. Forcada, C. Scarton, J. Van den Bogaert, J. Daems, A. Tezcan, B. Vanroy, & M. Fonteyne (Eds.), Proceedings of the 23rd Annual Conference of the European Association for Machine Translation (pp. 101–110). European Association for Machine Translation. https://aclanthology.org/2022.eamt-1.13/

Manakhimova, S., Avramidis, E., Macketanz, V., Lapshinova-Koltunski, E., Bagdasarov, S., & Möller, S. (2023). Linguistically Motivated Evaluation of the 2023 State-of-the-art Machine Translation: Can ChatGPT Outperform NMT? Proceedings of the Eighth Conference on Machine Translation, 224–245. https://doi.org/10.18653/v1/2023.wmt-1.23

Mau, B.-R., Wu, Y.-P., & Feng, H.-H. (2026). Mind the cognitive load gap: Student translators’ cognitive demands and translation behaviors in GenAI-assisted legal translation. System, 139, 104033. https://doi.org/10.1016/j.system.2026.104033

Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024). Developing language teachers’ professional generative AI competence: An intervention study in an initial language teacher education course. System, 125, 103399. https://doi.org/10.1016/j.system.2024.103399

NLLB Team. (2024). Scaling neural machine translation to 200 languages. Nature, 630(8018), 841–846. https://doi.org/10.1038/s41586-024-07335-x

Noriega-Santiáñez, L., & Corpas Pastor, G. (2025a). Measuring Creative Phraseology in Literature: Machine Translation Systems Versus Large Language Models. Yearbook of Phraseology, 16(1), 125–152. https://doi.org/10.1515/phras-2025-0006

Noriega-Santiáñez, L., & Corpas Pastor, G. (2025b). Technology and GenAI adoption among literary translators in Spain: A survey study on uses, perceptions and attitudes. Tradumàtica tecnologies de la traducció, (23), 137–164. https://doi.org/10.5565/rev/tradumatica.505

Peng, K., Ding, L., Zhong, Q., Shen, L., Liu, X., Zhang, M., Ouyang, Y., & Tao, D. (2023). Towards Making the Most of ChatGPT for Machine Translation. Findings of the Association for Computational Linguistics: EMNLP 2023, 5622–5633. https://doi.org/10.18653/v1/2023.findings-emnlp.373

Prieto Ramos, F. (2024). Revisiting translator competence in the age of artificial intelligence: The case of legal and institutional translation. The Interpreter and Translator Trainer, 18(2), 148–173. https://doi.org/10.1080/1750399X.2024.2344942

Rajpoot, P., Bhat, N., & Shrivastava, A. (2024). Multimodal Machine Translation for Low-Resource Indic Languages: A Chain-of-Thought Approach Using Large Language Models. Proceedings of the Ninth Conference on Machine Translation, 833–838. https://doi.org/10.18653/v1/2024.wmt-1.79

Resende, N., & Hadley, J. (2026). Extending Creativity: Large Language Models and the Practice of Poetry Translation. En D. Shterionov, E. Vanmassenhove, M. De Sisto, F. Blain, J. Pourmostafa Roshan Sharami, L. Lepp, C. Manna, A. A. Rescigno, A. Karakanta, A. Rigouts Terryn, M. Lardelli, N. Resende, E. Murgolo, J. Hackenbuchner, A. Zaretskaya, M. Esplà-Gomis, T. Etchegoyhen, D. Gromann, R. Bawden, … H. Moniz (Eds.), Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) (pp. 787–799). European Association for Machine Translation. https://aclanthology.org/2026.eamt-1.50/

Robinson, N., Ogayo, P., Mortensen, D. R., & Neubig, G. (2023). ChatGPT MT: Competitive for High- (but Not Low-) Resource Languages. Proceedings of the Eighth Conference on Machine Translation, 392–418. https://doi.org/10.18653/v1/2023.wmt-1.40

Rojo López, A. M., Vicente López, M. I., & Hvelplund, K. T. (2024). Measuring cognitive effort in post-editing: An eye-tracking study comparing professional and student translators. Tradumàtica tecnologies de la traducció, (22), 112–135. https://doi.org/10.5565/rev/tradumatica.418

Sinha, K., Mahesh, R., & Thakur, A. (2005). Translation divergence in English-Hindi MT. Proceedings of the 10th EAMT Conference: Practical applications of machine translation. https://aclanthology.org/2005.eamt-1.33/

Sinha, R. M. K., & Jain, A. (2003). AnglaHindi: An English to Hindi machine-aided translation system. Proceedings of Machine Translation Summit IX: System Presentations. https://aclanthology.org/2003.mtsummit-systems.15/

Sinha, R. M. K., Sivaraman, K., Agrawal, A., Jain, R., Srivastava, R., & Jain, A. (1995). ANGLABHARTI: A multilingual machine aided translation project on translation from English to Indian languages. 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, 2, 1609–1614. https://doi.org/10.1109/ICSMC.1995.538002

Sinha, R. M. K., & Thakur, A. (2005). Handling ki in Hindi for Hindi-English MT. Proceedings of Machine Translation Summit X: Posters, 356–353. https://aclanthology.org/2005.mtsummit-posters.6/

Su, Y., Xu, S., & Liu, K. (2025). Adapt or adopt? Examining the efficacy of ChatGPT in providing translation feedback. The Interpreter and Translator Trainer, 19(3–4), 296–316. https://doi.org/10.1080/1750399X.2025.2541486

Taivalkoski-Shilov, K. (2019). Ethical issues regarding machine(-assisted) translation of literary texts. Perspectives, 27(5), 689–703. https://doi.org/10.1080/0907676X.2018.1520907

Tewari, P., & Baghel, A. S. (2026). STYLISTICALLY-AWARE HINDI-ENGLISH POETIC TRANSLATION WITH MBART AND LLM-BASED POST- EDITING. Journal of Theoretical and Applied Information Technology, 104(3). https://www.jatit.org/volumes/Vol104No3/36Vol104No3.pdf

Tian, S., Wang, D., Wang, J., & Zhong, W. (2025). Empowering GenAI with a guidance-based approach in MTPE learning: Effect on student translators’ cognitive process, final translation quality and learning motivation. The Interpreter and Translator Trainer, 19(3–4), 379–404. https://doi.org/10.1080/1750399X.2025.2534269

Tian, X. (2024). Personalized translator training in the era of digital intelligence: Opportunities, challenges, and prospects. Heliyon, 10(20), e39354. https://doi.org/10.1016/j.heliyon.2024.e39354

Toral, A., & Way, A. (2015). Machine-assisted translation of literary text: A case study. Translation Spaces, 4(2), 240–267. https://doi.org/10.1075/ts.4.2.04tor

Toral, A., & Way, A. (2018). What Level of Quality Can Neural Machine Translation Attain on Literary Text? En J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation Quality Assessment (Vol. 1, pp. 263–287). Springer International Publishing. https://doi.org/10.1007/978-3-319-91241-7_12

Toral, A., Wieling, M., & Way, A. (2018). Post-editing Effort of a Novel With Statistical and Neural Machine Translation. Frontiers in Digital Humanities, 5, 9. https://doi.org/10.3389/fdigh.2018.00009

Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D., Shi, S., & Tu, Z. (2023). Document-Level Machine Translation with Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 16646–16661. https://doi.org/10.18653/v1/2023.emnlp-main.1036

Wang, L., Sun, S., Wang, X., Gu, J., & Liu, K. (2026). Workflow matters: Comparing human translators and multi-agent LLMs in literary translation. Target. International Journal of Translation Studies. https://doi.org/10.1075/target.25081.wan

Wu, Y.-P., Feng, H.-H., & Mau, B.-R. (2025). Didactic potential of working with DIY corpora and text mining approaches in literary translation training. The Interpreter and Translator Trainer, 19(2), 170–196. https://doi.org/10.1080/1750399X.2025.2488714

Yao, X., Kang, Y.-B., & McCosker, A. (2025). Missing the human touch?: A computational stylometric analysis of GPT-4 translations of online Chinese literature. Translation Spaces, 14(2), 303–330. https://doi.org/10.1075/ts.24043.yao

Zerva, C., Blain, F., C. De Souza, J. G., Kanojia, D., Deoghare, S., Guerreiro, N. M., Attanasio, G., Rei, R., Orasan, C., Negri, M., Turchi, M., Chatterjee, R., Bhattacharyya, P., Freitag, M., & Martins, A. (2024). Findings of the Quality Estimation Shared Task at WMT 2024: Are LLMs Closing the Gap in QE? Proceedings of the Ninth Conference on Machine Translation, 82–109. https://doi.org/10.18653/v1/2024.wmt-1.3

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