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Translation and Foreign Language Learning

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ArticleTranslation Theories and Skills http://dx.doi.org/10.26855/tfll.2025.08.004

Enhancing Translation Quality through Human-centered AI: A Case Study on Popular Science Book Translation

Meichen Liu, Rong Chen*

School of Foreign Studies, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China.

*Corresponding author: Rong Chen

The Educational Reform of Xi’an University of Posts and Telecommunications for Postgraduate Programs “Inte-grat-ing Chinese and English Discourses: An Exploration of the Optimization Path for the MTl Training Model Driven by PBL Theory” (YJGJ2024034).
Published: August 20,2025

Abstract

This study explores the Human-Centered AI (HCAI) translation paradigm, which emphasizes the central role of human translators while integrating Artificial Intelligence (AI) tools as supportive elements. Using the English-to-Chinese translation of a popular science book as a case study, the research demonstrates how human-AI collaboration can improve translation quality. The team utilized various AI tools, including the Kimi assistant for background research, Google Neural Machine Translation (NMT) for initial translation, and a Large Language Model (LLM) such as GPT for semantic clarification and cultural adaptation. Crucially, all AI-generated outputs were supervised and refined by human translators. To evaluate the effectiveness of the HCAI approach, a comparative analysis was conducted between two versions: one produced under the HCAI model (experimental group) and one generated solely by machine translation (control group). Translation quality was assessed through manual evaluation—focusing on fidelity, fluency, and accuracy—and automatic evaluation using ROUGE metrics. Results from both methods indicate that the HCAI model produced translations that were significantly more accurate, coherent, and culturally appropriate. These findings highlight the value of human oversight in ensuring translation quality and suggest that the HCAI paradigm offers an effective and scalable model for translating complex, domain-specific texts such as popular science.

Keywords

Human-Centered AI; Machine Translation; Translation Quality; Human-AI Col-laboration

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How to cite this paper

Enhancing Translation Quality through Human-centered AI: A Case Study on Popular Science Book Translation

How to cite this paper: Meichen Liu, Rong Chen. (2025). Enhancing Translation Quality through Human-centered AI: A Case Study on Popular Science Book Translation. Translation and Foreign Language Learning, 1(1), 23-32.

DOI: http://dx.doi.org/10.26855/tfll.2025.08.004