This study investigates the knowledge transfer capability and cultural adaptability of artificial intelligence (AI) translation tools in specialized ceramic texts, grounded in Knowledge Translation Theory. Utilizing textual descriptions of Ming Dynasty blue-and-white porcelain plates featuring Zhong Kui (a Daoist deity) motifs from The Art of the Chinese Potter from the Han Dynasty to the End of the Ming as corpus, we developed a multi-dimensional “Human Scoring + Automated Metrics” evaluation framework centered on terminology translation and culture-laden terms. A systematic comparative analysis was conducted across six AI translation tools: ChatGPT, DeepSeek, DeepL, Doubao, Youdao Translate, and Baidu Translate. Results reveal significant disparities among tools in key areas: terminology recognition, semantic reconstruction, and cultural recontextualization. While contemporary AI tools demonstrate preliminary competence in processing ceramic texts, notable deficiencies persist in restoring cultural metaphors and maintaining terminological consistency. This research contributes to: (1) expanding the application scope of AI in specialized non-general domain translation, (2) enriching methodological frameworks for translation quality assessment, and (3) advancing the global dissemination of ceramic cultural heritage. 

Yujiao Feng. The Evaluation of AI Translation Competence in Ceramic Texts through the Lens of Knowledge Translation Theory: A Study of ChatGPT, DeepSeek, and Other Tools. Academic Journal of Humanities & Social Sciences (2025), Vol. 8, Issue 8: 118-127. https://doi.org/10.25236/AJHSS.2025.080818.

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