Human and Artificial Intelligence in Literary Translation Evaluating Meaning, Style, and Cultural Representation
DOI:
https://doi.org/10.55927/fjst.v5i8.151Keywords:
Artificial Intelligence, Literary Translation, Meaning, Style, Cultural Representation.Abstract
The development of artificial intelligence in translation raises questions about its ability to maintain meaning, style, and cultural representation in English literature. This study aims to compare human and AI translations in parts of texts that contain metaphors, idioms, narrative styles, and cultural elements. The study used a comparative qualitative approach by involving three human translators, one AI model, and two translation experts as validators. Data was collected through translation documentation and expert review, then analyzed using comparative textual analysis and thematic coding. The results show that human translators more consistently retain figurative nuances, narrative voices, and cultural distinctiveness, while AI tends to produce more explicit and standardized translations. The findings confirm the importance of human interpretation in AI-assisted literary translation
References
Abdelhalim, S. M., Alsahil, A. A., & Alsuhaibani, Z. A. (2025). Artificial intelligence tools and literary translation: A comparative investigation of ChatGPT and Google Translate from novice and advanced EFL student translators’ perspectives. Cogent Arts & Humanities, 12(1), Article 2508031. https://doi.org/10.1080/23311983.2025.2508031
Abu Rumman, R., Haider, A. S., Yagi, S., & Al-Adwan, A. (2023). A corpus-assisted cognitive analysis of metaphors in the Arabic subtitling of English TV series. Cogent Social Sciences, 9(1), Article 2231622. https://doi.org/10.1080/23311886.2023.2231622
Ahmad, M., & Wilkins, S. (2025). Purposive sampling in qualitative research: A framework for the entire journey. Quality & Quantity, 59(2), 1461–1479. https://doi.org/10.1007/s11135-024-02022-5
Al Rousan, R., Jaradat, R., & Malkawi, M. (2025). ChatGPT translation vs. human translation: An examination of a literary text. Cogent Social Sciences, 11(1), Article 2472916. https://doi.org/10.1080/23311886.2025.2472916
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328–352. https://doi.org/10.1080/14780887.2020.1769238
Byrne, D. (2022). A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391–1412. https://doi.org/10.1007/s11135-021-01182-y
Chen, S., & Lin, Y. (2025). A multidimensional comparison of ChatGPT, Google Translate, and DeepL in Chinese tourism texts translation: Fidelity, fluency, cultural sensitivity, and persuasiveness. Frontiers in Artificial Intelligence, 8, Article 1619489. https://doi.org/10.3389/frai.2025.1619489
Farghal, M., & Haider, A. S. (2024). Translating classical Arabic verse: Human translation vs. AI large language models (Gemini and ChatGPT). Cogent Social Sciences, 10(1), Article 2410998. https://doi.org/10.1080/23311886.2024.2410998
Fu, L., & Liu, L. (2024). What are the differences? A comparative study of generative artificial intelligence translation and human translation of scientific texts. Humanities and Social Sciences Communications, 11, Article 1236. https://doi.org/10.1057/s41599-024-03726-7
Gao, R., Lin, Y., Zhao, N., & Cai, Z. G. (2024). Machine translation of Chinese classical poetry: A comparison among ChatGPT, Google Translate, and DeepL Translator. Humanities and Social Sciences Communications, 11, Article 835. https://doi.org/10.1057/s41599-024-03363-0
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, 36(2), 215–244. https://doi.org/10.1075/target.22134.gue
Haider, A. S., & Shuhaiber, R. (2024). Netflix English subtitling of idioms in Egyptian movies: Challenges and strategies. Humanities and Social Sciences Communications, 11, Article 949. https://doi.org/10.1057/s41599-024-03327-4
Jiménez-Crespo, M. A. (2025). Human-centered AI and the future of translation technologies: What professionals think about control and autonomy in the AI era. Information, 16(5), Article 387. https://doi.org/10.3390/info16050387
Kolb, W., Dressler, W. U., & Mattiello, E. (2023). Human and machine translation of occasionalisms in literary texts: Johann Nestroy’s Der Talisman and its English translations. Target, 35(4), 540–572. https://doi.org/10.1075/target.21147.kol
Saed, H., Haider, A. S., Abu Tair, S., & Asiri, E. (2024). Intrinsic managing and the English-Arabic translation of fictional registers. Cogent Arts & Humanities, 11(1), Article 2371659. https://doi.org/10.1080/23311983.2024.2371659
Wang, G., & Xin, Y. (2024). An analytical framework for corpus-based translation studies. Humanities and Social Sciences Communications, 11, Article 1709. https://doi.org/10.1057/s41599-024-04250-4
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
Zhao, P., Qi, W., Li, P.-J., & Li, P. (2024). Reconceptualizing the link between validity and translation in qualitative research: Extending the conversation beyond equivalence. International Journal of Qualitative Methods, 23, Article 16094069241260134.
Zhou, P., & Cheng, J. (2025). Stylistic variation across English translations of Chinese science fiction: Ken Liu versus ChatGPT. Frontiers in Artificial Intelligence, 8, Article 1576750. https://doi.org/10.3389/frai.2025.1576750
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Gunawan Tambunsaribu

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































