JHASS

Article http://dx.doi.org/10.26855/jhass.2026.06.002

An AI‑enhanced Synergistic “English + Translation” Pedagogy in a Marine Culture Course: A Mixed‑methods Action Research Study

TOTAL VIEWS: 707

Tianjiao Song

Faculty of Foreign Languages, Guangdong Ocean University, Zhanjiang 524088, Guangdong, China.

*Corresponding author: Tianjiao Song

This paper is a research result of the 2025 Special Project on Foreign Language Teaching Reform Research and Practice for Guangdong Province Undergraduate Universities (Grant No. 25GWYB06): “Innovation and Practice of the ‘English + Translation’ Multi‑Collaborative Teaching Model Integrating AI Technology”, supported by the Guangdong Provincial Higher Education Association, Guangdong Ocean University Teaching Quality Enhancement and Teaching Reform Project (Project Name: Constructing a “Marine + English” Characteristic Value-Oriented Classroom for the Course “Marine Culture and Translation”, Grant No. PX-52024005), and the School of Foreign Languages Smart Course Construction Project, Guangdong Ocean University.

Published: June 12,2026

Abstract

This mixed‑methods action research study evaluated an AI‑enhanced synergistic “English + Translation” pedagogy within a 16‑week Marine Culture course at a Chinese university. The intervention used an AI Teaching Assistant on the Chaoxing platform to operationalise three synergy axes: human‑AI, peer, and teacher‑student. Participants were 125 fourth‑year English majors across four intact classes. Quantitative data included pre‑, mid‑, and post‑course translation tests, AI platform logs, and Technology Acceptance Model (TAM) questionnaires. Qualitative data comprised reflective journals, observations, peer feedback, teacher reflections, and interviews. Quantitative analyses showed significant improvements in translation performance across the three time points with moderate to large effect sizes. Regression analysis identified AI task completion rate as the strongest predictor of post‑course translation scores. TAM dimensions increased significantly with large effect sizes. Thematic analysis generated four themes: AI lowering translation initiation thresholds, peer feedback promoting metacognitive awareness, irreplaceability of teacher feedback, and enhanced motivation through marine cultural content. The results indicate that a synergistically designed AI intervention substantially improves translation competence and technology acceptance while preserving essential human collaboration. The findings extend Kintsch’s Construction‑Integration model and collaborative learning frameworks to AI‑mediated translation pedagogy, providing a replicable model for integrating AI as a critical partner rather than a replacement.

Keywords

AI assisted translation pedagogy; synergistic learning; marine culture; mixed methods action research

References

Bowker, L. (2002). Computer-aided translation technology: A practical introduction. University of Ottawa Press.

https://doi.org/10.7202/007488ar

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

https://doi.org/10.1191/1478088706qp063oa

Brinton, D. M., Snow, M. A., & Wesche, M. B. (1989). Content-based second language instruction. Newbury House.

Burston, J. (2014). MALL: The pedagogical challenges. Computer Assisted Language Learning, 27(4), 344-357.

https://doi.org/10.1080/09588221.2014.914539

Chaoxing. (2025). Chaoxing smart teaching platform AI teaching assistant. https://tlc.mh.chaoxing.com

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. Transaction Publishers. (Original work published 1970)

Dillenbourg, P. (Ed.). (1999). Collaborative learning: Cognitive and computational approaches. Pergamon.

EMT Expert Group. (2022). EMT competence framework 2022. European Commission, Directorate-General for Translation.

Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023). Automated feedback and writing: A multi-level meta-analysis of effects on students’ performance. Frontiers in Artificial Intelligence, 6, 1162454.

https://doi.org/10.3389/frai.2023.1162454

Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378-382.

Grgurović, M. (2017). Blended language learning. In C. A. Chapelle & S. Sauro (Eds.), The handbook of technology and second language teaching and learning (pp. 149-168). Wiley-Blackwell.

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in mixed methods research. Journal of Mixed Methods Research, 9(3), 227-244. https://doi.org/10.1177/1558689814534392

Heift, T. (2010). Prompting in CALL: A longitudinal study of learner uptake. The Modern Language Journal, 94(2), 198-216. https://doi.org/10.1111/j.1540-4781.2010.01020.x

Hemingway, E. (1952). The old man and the sea. Charles Scribner’s Sons.

Hu, X., & Gong, W. (2025). Modeling Chinese EFL learners’ intention to use generative AI for L2 writing through an integrated model of the TAM and TTF. Education and Information Technologies, 30(13), 18157-18179.

https://doi.org/10.1007/s10639-025-13505-9

Huang, X., Hew, K. F., & Dewa, F. (2023). AI in the foreign language classroom: A pedagogical overview of automated writing assistance tools. Education and Information Technologies, 28(10), 13407-13434.

https://doi.org/10.1007/s10639-023-11765-3

IBM Corp. (2020). IBM SPSS Statistics for Windows (Version 27.0) [Computer software].

Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365-379. https://doi.org/10.3102/0013189X09339057

Kemmis, S., McTaggart, R., & Nixon, R. (2014). The action research planner: Doing critical participatory action research. Springer.

Kenny, D., & Doherty, S. (2014). Statistical machine translation in the translation curriculum: Overcoming obstacles and empowering translators. The Interpreter and Translator Trainer, 8(2), 276-294.

https://doi.org/10.1080/1750399X.2014.937556

Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration model. Psychological Review, 95(2), 163-182. https://doi.org/10.1037/0033-295X.95.2.163

Kiraly, D. (2000). A social constructivist approach to translator education: Empowerment from theory to practice. St. Jerome.

Kress, G., & van Leeuwen, T. (2001). Multimodal discourse: The modes and media of contemporary communication. Arnold.

Lyster, R. (2007). Learning and teaching languages through content: A counterbalanced approach. John Benjamins.

https://doi.org/10.1075/lllt.18

McNiff, J. (2013). Action research: Principles and practice (3rd ed.). Routledge.
https://doi.org/10.4324/9780203112755

Melville, H. (2002). Moby-Dick; or, the whale. Modern Library. (Original work published 1851)

Muñoz Martín, R. (2017). Looking toward the future of cognitive translation studies. In J. W. Schwieter & A. Ferreira (Eds.), The handbook of translation and cognition (pp. 555-572). Wiley Blackwell.

O’Brien, S. (2014). Peer collaboration in translation education: Enhancing metacognitive awareness through structured feedback. In S. Colina & C. V. Angelelli (Eds.), Translation and interpreting pedagogy in dialogue with other disciplines. John Benjamins.

Salomon, G. (Ed.). (1993). Distributed cognitions: Psychological and educational considerations. Cambridge University Press.

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equa-tion modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Educa-tion, 128, 13-35.
https://doi.org/10.1016/j.compedu.2018.09.009

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Tai, J., Ajjawi, R., & Boud, D. (2018). Developing evaluative judgement: Enabling students to make decisions about the quality of work. Higher Education, 76(3), 467-482.
https://doi.org/10.1007/s10734-017-0225-8

Verne, J. (1998). 20,000 leagues under the sea (Unabridged ed.). Recorded Books. (Original work published 1870)

Warschauer, M., & Ware, P. (2008). Automated writing evaluation in the classroom. Pedagogies: An International Journal, 3(1), 22-36.
https://doi.org/10.1080/15544800701771536

Yamada, M. (2019). The impact of Google neural machine translation on post-editing by student translators. The Journal of Specialised Translation, 31, 87-106.
https://www.jostrans.org/issue31/art_yamada.pdf

Zhou, Y. (Ed.). (2018). Haiyang yingyu fanyi jiaocheng [A course in marine English translation]. China Agriculture Press.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.

https://doi.org/10.1207/s15430421tip4102_2

How to cite this paper

An AI‑enhanced Synergistic “English + Translation” Pedagogy in a Marine Culture Course: A Mixed‑methods Action Research Study

How to cite this paper: Tianjiao Song. (2026) An AI‑enhanced Synergistic “English + Translation” Pedagogy in a Marine Culture Course: A Mixed‑methods Action Research Study. Journal of Humanities, Arts and Social Science, 10(6), 609-623.

DOI: http://dx.doi.org/10.26855/jhass.2026.06.002