A reception study of AI-translated idioms and proverbs between Arabic and English
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Abstract
This study investigates the perceptions, experiences, and evaluative judgments of translation students toward AI-translated idioms and proverbs between Arabic and English. Drawing on a large-scale sample of 3,000 students from public and private Jordanian universities, the research examines how students engage with AI tools in translation tasks that involve culturally embedded figurative language. Using a structured 25-item questionnaire, the study explores six core dimensions: confidence in translating idioms and proverbs, frequency and patterns of AI tool use, perceived accuracy and error detection, attitudes toward AI’s role in translation, preferences for human versus AI translation in idiomatic contexts, and willingness to use AI tools in academic or professional translation. Statistical analysis revealed significant relationships between students’ translation confidence and their ability to identify errors in AI-generated outputs. Participants who frequently encountered idiomatic content and received formal training in translation evaluation demonstrated greater skepticism toward AI’s handling of figurative meaning. While students acknowledged the efficiency and utility of AI tools, a strong preference remained for human translation when dealing with nuanced, culturally rich expressions. The findings support the growing need for AI literacy, critical evaluation skills, and ethical awareness in translator education. Recommendations are provided for educators, curriculum designers, and translation programs to develop balanced, context-aware approaches that integrate human expertise with technological tools.
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References
Al-Darabee, M., & Farghal, M. (2025). Veed. AI Arabic subtitling of English taboo expressions in the movie The Wolf of Wall Street. Babel Revue internationale de la traduction / International Journal of Translation, 71(2), 1-25. doi:https://doi.org/10.1075/babel.24193.far
Al-khresheh, M. H., & Almaaytah, S. A. (2018). English proverbs into Arabic through machine translation. International Journal of Applied Linguistics and English Literature, 7(5), 158-166. doi:https://doi.org/10.7575/aiac.ijalel.v.7n.5p.158
Al-Salman, S., & Haider, A. S. (2021). Jordanian University Students' Views on Emergency Online Learning during COVID-19. Online Learning, 25(1), 286-302. doi:https://doi.org/10.24059/olj.v25i1.2470
Al-Salman, S., & Haider, A. S. (2024). Assessing the accuracy of MT and AI tools in translating humanities or social sciences Arabic research titles into English: Evidence from Google Translate, Gemini, and ChatGPT International Journal of Data and Network Science, 8(4), 1-16. doi:doi: 10.5267/j.ijdns.2024.5.009
Al-Salman, S., Haider, A. S., & Saed, H. (2022). The psychological impact of COVID-19’s e-learning digital tools on Jordanian university students’ well-being. The Journal of Mental Health Training, Education and Practice, 14(4), 342–354. doi:10.1108/JMHTEP-09-2021-0106
Alkatheery, E. R. (2023). Google Translate Errors in Legal Texts: Machine Translation Quality Assessment. AWEJ for Translation and Literary Studies, 7(1).
Almahasees, Z. (2017). Machine Translation Quality of Khalil Gibran’s The Prophet. AWEJ for translation & literary studies, 1(4), 151-159. doi:10.2139/ssrn.3068518
Almahasees, Z. (2021). Analysing English-Arabic Machine Translation: Google Translate, Microsoft Translator and Sakhr. London: Routledge.
Almhasees, Z., Abu-Rayyash, H. K., Olimat, S. N., & Mahadin, D. K. (2024). An Analytical Study on Student Perceptions of Using ChatGPT in Language Translation. Language Value, 17(2), 1-23.
Bouguesmia, M. T. (2020). Using AI in Translation, a Technological Leap, or a Translator’s Nightmare. ALTRALANG Journal, 2(02), 78-102.
Chan, V., & Tang, W. K.-W. (2024). GPT for translation: A systematic literature review. SN computer science, 5(8), 986.
Ervas, F. (2012). The Definition of Translation in Davidson’s Philosophy: Semantic Equivalence versus Functional Equivalence. TTR, 25(1), 243-265. doi:https://doi.org/10.7202/1015354ar
Farghal, M. (2019). Jordan’s Proverbs as a Window into Arab Popular Culture: The Fox in the Blackberries. Cambridge: Cambridge Scholars Publishing.
Farghal, M., & Al-Hamly, M. (2015). Arabic proverbs in fiction translation: Girls of Riyadh as an example. Jordan Journal of Modern Languages Literature, 7(1), 1-20.
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), 2410998. doi:https://doi.org/10.1080/23311886.2024.2410998
Farghal, M., & Haider, A. S. (2025). A Cogno-Prosodic Approach to Translating Arabic Poetry into English: Human vs. Machine. 3L, Language, Linguistics, Literature, 31(1), 255-271. doi:https://doi.org/10.17576/3l-2025-3101-17
Gao, F. (2024). When Minds Meet Machines: A Pilot Programme of using Chat-GPT to Teach Political Speech Translation in a Chinese-to-English Translation Classroom. International Journal of Chinese and English Translation & Interpreting.
Haider, A. S., & Al-Salman, S. (2020). Dataset of Jordanian University Students’ Psychological Health Impacted by Using E-learning Tools during COVID-19. Data in Brief, 32. doi:https://doi.org/10.1016/j.dib.2020.106104
Haider, A. S., & Shohaibar, R. S. (2024). Netflix English subtitling of idioms in Egyptian movies: challenges and strategies. Humanities & Social Sciences Reviews, 11, 949. doi:https://doi.org/10.1057/s41599-024-03327-4
Hamat, A. (2024). The Language of AI and Human Poetry: A Comparative Lexicometric Study. 3L, Language, Linguistics, Literature, 30(2), 1-20. doi:http://doi.org/10.17576/3L-2024-3002-01
Jarrah, S., Haider, A. S., & Al-Salman, S. (2023). Strategies of Localizing Video Games into Arabic: A Case Study of PUBG and Free Fire. Open Cultural Studies, 7(1), 20220179. doi:https://doi.org/10.1515/culture-2022-0179
Jibreel, I. (2023). Online Machine Translation Efficiency in Translating Fixed Expressions Between English and Arabic (Proverbs as a Case-in-Point). Theory Practice in Language Studies, 13(5), 1148-1158. doi:https://doi.org/10.17507/tpls.1305.07
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.
Mahmood, K. A., & Ahmed, E. S. (2022). Word Equivalence: An Investigation of Fiction Translation from English to Kurdish Between 2003 and 2021. Arab World English Journal (AWEJ) 2022(1), 89-99. doi:https://doi.org/10.24093/awej/kust.7
Malika, B. (2016). The Deficiencies of Machine Translation of Proverbs and Poetry: Google and Systran Translations as a case study. Algerian Scientific Journal Platform, 10(1), 222-240.
Obeidat, M. M., Haider, A. S., Tair, S. A., & Sahari, Y. (2024). Analyzing the Performance of Gemini, ChatGPT, and Google Translate in Rendering English Idioms into Arabic. FWU Journal of Social Sciences, 18(4), 1-18. doi:http://doi.org/10.51709/19951272/Winter2024/1
Saed, H., Haider, A. S., Abu Tair, S., & Asiri, E. (2024a). Intrinsic managing and the English-Arabic translation of fictional registers. Cogent Arts & Humanities, 11(1), 2371659. doi:https://doi.org/10.1080/23311983.2024.2371659
Saed, H., Haider, A. S., Tair, S. A., & Darwish, N. (2024b). Dubbing English edutainment: Localizing educational content for Arab children. Research Journal in Advanced Humanities, 5(3), 391-406. doi:https://doi.org/10.58256/bh9fhj11
Shhaiber, R., & Haider, A. S. (2023). Strategies of Subtitling Egyptian Idiomatic Expressions and Proverbs into English. Language Value, 16(2), 60-99. doi:https://doi.org/10.6035/languagev.7015
Sianipar, N. N., Sipayung, K. T., & Octavianna, Y. (2025). An Analysis of Translation Techniques Between Students and Chat-Gpt In Translating Sarcasm and Irony In The Gadis Kretek Novel. Jurnal Kajian Ilmu Pendidikan (JKIP), 6(2), 286-304.
Syahid, A. H., Mu'izzuddin, M., Uyuni, Y. R., & Nuroh, N. (2025). Future Arabic Translator and Chat Gpt-3 Change Work Dynamics in The Translation Profession: Students' Voices. Al-Ta'rib: Jurnal Ilmiah Program Studi Pendidikan Bahasa Arab IAIN Palangka Raya, 13(1), 31-44.
Syarfuni, S. (2014). An Analysis of English and Indonesian Idioms and Proverbs. Getsempena English Education Journal, 1(1), 26-50.
Weld-Ali, E. W., Obeidat, M. M., & Haider, A. S. (2023). Religious and Cultural Expressions in Legal Discourse: Evidence from Interpreting Canadian Courts Hearings from Arabic into English. International Journal for the Semiotics of Law-Revue internationale de Sémiotique juridique, 36(6), 2283–2301. doi:https://doi.org/10.1007/s11196-023-10016-z
Yilmaz, E. D., Naumovska, I., & Aggarwal, V. A. (2023). AI-Driven Labor Substitution: Evidence from Google Translate and ChatGPT. INSEAD.