Rethinking Higher Education/Chapter 4/en-zh

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The End of Translation: How AI is Transforming Cross-Cultural Communication in Education	(zu übersetzen)翻译的终结：AI如何改变教育中的跨文化沟通
Martin Woesler	(zu übersetzen)马丁 沃斯勒
'Abstract'	(zu übersetzen)摘要
The rise of neural machine translation (NMT) and generative AI has fundamentally altered the landscape of cross-cultural communication and language education. This article examines the transformation wrought by AI-powered translation tools — including DeepL, ChatGPT, and Chinese platforms such as Baidu Translate and Youdao — on translation practice, the translation labour market, and language pedagogy. Drawing on recent comparative studies and labour market data, we document a dramatic contraction in professional translation demand alongside an expansion of machine translation post-editing (MTPE) workflows. We argue that this shift necessitates a new educational paradigm centred on „machine translation literacy„ rather than traditional translation competence. Through a systematic comparison of European and Chinese responses to this transformation, we identify divergent regulatory approaches and converging pedagogical challenges. We conclude that the „end of translation“ is not the end of cross-cultural understanding but rather a reorientation of human expertise toward what remains irreducibly human: cultural nuance, ethical judgement, and literary sensibility.	(zu übersetzen)神经机器翻译（NMT）和生成式AI的兴起，从根本上改变了跨文化沟通和语言教育的格局。本文探讨了AI所驱动的翻译工具—包括DeepL,ChatGpt,以及中国的百度翻译和有道等平台，对翻译实践，翻译劳动力市场以及语言教学方法所带来的变革。通过借鉴近期比较研究和劳动力市场的数据，我们记录了专业翻译需求的急剧萎缩，以及机器翻译后编辑(MTPE)工作流程的扩张。我们认为，这一转变要建立一种以机器翻译素养为核心的新教育范式，而并非传统的翻译能力。我们发现了不同的监管逻辑和趋同的教学挑战。我们得出的结论是：翻译的“终结”并非跨文化理解的终结，而是人类专业能力的一次重新定位—转向的是那些仍然不可还原为机器的人类特质：即文化的细微差别，伦理判断和文学的敏感性。”
Keywords: machine translation, AI in education, DeepL, ChatGPT, post-editing, translation literacy, EU-China comparison, language education	(zu übersetzen)关键词：机器翻译，AI教育，DeepL，ChatGPT，译后编辑，中欧比较，语言教育
'1. Introduction'	(zu übersetzen)引言
On 30 May 2024, as part of the Jean Monnet Centre of Excellence lecture series „Digitalization in China and Europe“ at Hunan Normal University, the author delivered a lecture entitled „The End of Translation.“ The title was deliberately provocative. Translation, one of humanity’s oldest intellectual activities, appears to face an existential challenge from artificial intelligence. Neural machine translation systems now process billions of words daily with accuracy levels that would have seemed inconceivable a decade ago. The International Monetary Fund reported in January 2026 that its translator and interpreter staff had declined from 200 to approximately 50, with the remainder increasingly reassigned to quality control of machine-generated output (CNN 2026). A 2024 survey by the Chartered Institute of Linguists (CIOL) found that over 70 percent of freelance translators reported decreased work volumes (CIOL 2024).	2024年5月30日，作为湖南师范大学让·莫内卓越中心"中欧数字化"系列讲座的一部分，作者发表了题为"翻译的终结"的演讲。这个标题是有意为之的挑衅。翻译，作为人类最古老的智力活动之一，似乎面临着来自人工智能的生存性挑战。神经机器翻译系统现在每天处理数十亿字，其准确度在十年前看来是不可想象的。国际货币基金组织于2026年1月报告称，其翻译和口译人员已从200人减少到约50人，其余人员日益被重新分配到机器生成输出的质量控制工作中（CNN 2026）。英国特许语言学家学会（CIOL）2024年的一项调查发现，超过70%的自由译者报告工作量有所下降（CIOL 2024）。
Yet the provocative framing demands qualification. What is ending is not translation as an intellectual and cultural activity but rather translation as a routine professional service performed primarily by humans. What is emerging is a new ecology of human-machine collaboration in which the human role shifts from producing translations to evaluating, refining, and contextualizing machine output. For education — particularly for language departments and translation programmes in both European and Chinese universities — this shift poses urgent questions. What should we teach students who will enter a profession that looks radically different from its twentieth-century predecessor? How do we cultivate the distinctly human competencies that machines cannot replicate? And how do different educational systems — the European and the Chinese — respond to these challenges?	(zu übersetzen)然而，这种具有挑衅性的表述则更需要加以限定。正在终结的，并非作为智识和文化活动的翻译，而是作为主要由人类操作的常规专业服务性的翻译，正在出现的是一种人机协作的新生态，其中关于人类的角色从生产翻译转变为评估，完善和语境化的机器输出
This article addresses these questions by examining the current state of AI translation technology, its labour market consequences, the emerging concept of „machine translation literacy,“ the role of post-editing in education, and the divergent responses of European and Chinese institutions. It contributes to the broader anthology project by connecting the technological dimension of AI in education with the ethical framework developed in the companion chapter on Appropriateness Theory (Woesler, this volume) and the empirical findings on AI-assisted language learning (Woesler, this volume).本文通过审视人工智能翻译技术的现状、其对劳动力市场的影响、新兴的‘机器翻译素养’概念、后编辑在教育中的作用，以及欧洲与中国机构的差异化回应，来探讨这些问题。本文通过将语言学习与本卷中的相关论述（参见 Woesler）联系起来，为该选集项目做出贡献。	(zu übersetzen)“本文通过审视人工智能翻译技术的现状、其对劳动力市场的影响、新兴的‘机器翻译素养’概念、后编辑在教育中的作用，以及欧洲与中国机构的差异化回应，来探讨这些问题。本文通过将语言学习与本卷中的相关论述（参见 Woesler）联系起来，为该选集项目做出贡献。”
'2. Historical Context: Translation and Technology'	(zu übersetzen)“历史背景：翻译与技术”
Before examining the current AI-driven transformation, it is useful to situate it within the longer history of technology’s relationship with translation. Translation has always been mediated by technology in the broadest sense — from the invention of writing itself, which made translation possible as a textual practice, to the printing press, which created mass demand for translated works, to the typewriter and word processor, which transformed the translator’s physical workflow.	(zu übersetzen)在审视当前人工智能驱动的变革之前，有必要将其置于技术与翻译之间更长远的历史关系之中。从最广泛的意义上讲，翻译始终受到技术的调节——从使翻译成为文本实践的书写本身的发明，到创造了对翻译作品的大众需求的印刷机，再到改变了翻译人员实际工作流程的打字机和文字处理器。
The computational approach to translation dates to Warren Weaver’s 1949 memorandum, which proposed applying information theory and cryptographic techniques to the „translation problem.“ The subsequent decades of rule-based machine translation (RBMT) and statistical machine translation (SMT) produced systems that were useful for gisting — obtaining a rough understanding of a foreign text — but inadequate for producing publishable translations. The neural revolution of the mid-2010s, beginning with the attention mechanism (Bahdanau et al. 2014) and culminating in the Transformer architecture (Vaswani et al. 2017), changed this fundamentally. For the first time, machine output was often indistinguishable from competent human translation for routine texts.	计算方法处理翻译的历史可追溯到Warren Weaver 1949年的备忘录，他提出将信息论和密码学技术应用于"翻译问题"。此后数十年间，基于规则的机器翻译（RBMT）和统计机器翻译（SMT）所产生的系统虽然对获取大意——粗略理解外文文本——有所帮助，但不足以产出可发表的翻译。2010年代中期的神经网络革命——始于注意力机制（Bahdanau等人 2014），最终形成Transformer架构（Vaswani等人 2017）——从根本上改变了这一局面。对于常规文本而言，机器输出首次往往与称职的人工翻译难以区分。
The historical parallel most often invoked is the impact of photography on painting. Photography did not eliminate painting but it did eliminate painting’s monopoly on visual representation, freeing painters to explore dimensions of visual experience — abstraction, expressionism, conceptual art — that photography could not capture. Similarly, AI translation may not eliminate human translation but it is eliminating human translation’s monopoly on cross-linguistic communication, potentially freeing human translators to focus on dimensions of cross-cultural meaning-making that machines cannot replicate.	(zu übersetzen)人们最常援引的历史类比是摄影对绘画的影响。摄影并没有消灭绘画，但它消除了绘画在视觉表现领域的垄断地位，使画家得以探索摄影无法捕捉的视觉体验维度——抽象、表现主义、观念艺术。同样，人工智能翻译可能不会消灭人类翻译，但它正在消除人类翻译在跨语言交流中的垄断地位，从而有可能将人类译者解放出来，专注于机器无法复制的跨文化意义构建维度。
'3. The AI Translation Landscape: Tools and Capabilities'	(zu übersetzen)人工智能翻译的现状：工具与能力
'3.1 Neural Machine Translation vs. Generative AI'	(zu übersetzen)神经机器翻译与生成式人工智能
The distinction between dedicated neural machine translation (NMT) systems and generative AI (GenAI) tools is crucial for understanding the current landscape. As Ohashi (2024) demonstrate in their systematic comparison, NMT systems such as Google Translate and DeepL are purpose-built for translation, trained on parallel corpora, and optimized for fluency and adequacy in specific language pairs. GenAI tools such as ChatGPT, Claude, and China‘s Ernie Bot handle translation as one capability among many, leveraging broader linguistic understanding but lacking the specialized training data of dedicated NMT systems.	(zu übersetzen)要理解当前的人工智能翻译格局，区分专用的神经机器翻译系统与生成式人工智能工具至关重要。正如 Ohashi（2024）在其系统比较中所展示的，神经机器翻译系统（如 Google Translate 和 DeepL）是专门为翻译任务而构建的，在平行语料库上进行训练，并针对特定语言对的流畅性和充分性进行了优化。而生成式人工智能工具（如 ChatGPT、Claude 以及中国的文心一言）则将翻译作为其众多能力之一来处理，利用了更广泛的语言理解能力，但缺乏专用神经机器翻译系统所具备的专门训练数据。
This distinction has practical consequences. A multidimensional comparison of ChatGPT, Google Translate, and DeepL on Chinese tourism texts found that ChatGPT outperformed dedicated NMT across metrics of fidelity, fluency, cultural sensitivity, and persuasiveness — particularly when given culturally tailored prompts (Chen et al. 2025). However, ChatGPT occasionally introduced semantic shifts absent from the source text, a phenomenon that dedicated NMT systems avoid more reliably. Sun, R. (2024), evaluating Chinese-to-English literary translation through the lens of implied subjects, found that ChatGPT handled the implicit subject structures of contemporary Chinese prose (specifically Yu Qiuyu’s essays) with greater sophistication than DeepL, suggesting that GenAI’s broader contextual understanding compensates for its lack of specialized translation training in literary domains.	这种区别具有实际意义。一项对ChatGPT、谷歌翻译和DeepL在中国旅游文本翻译方面的多维度比较发现，在忠实度、流畅性、文化敏感性和说服力方面，ChatGPT优于专用NMT——特别是在给出文化定制提示时（Chen等人 2025）。然而，ChatGPT偶尔会引入源文本中不存在的语义偏移，这是专用NMT系统能更可靠避免的现象。Sun, R.（2024）通过隐含主语的视角评估中译英文学翻译，发现ChatGPT处理当代中国散文（特别是余秋雨的散文）的隐含主语结构比DeepL更加精妙，表明GenAI更广泛的语境理解弥补了其在文学领域缺乏专门翻译训练的不足。
'2.2 Chinese AI Translation Platforms'	(zu übersetzen)中国人工智能翻译平台
The Chinese AI translation ecosystem deserves separate attention, as it operates largely independently of the Western ecosystem. Baidu Translate (百度翻译) supports 203 languages, including rare dialects and classical Chinese, using NMT technology with context-aware sentence-level processing. Youdao (有道翻译), developed by NetEase, released its „Ziyue“ (子曰) educational large language model in 2023, followed by the Ziyue-o1 reasoning model in 2025, integrating translation capabilities with deep dictionary and study tool functions across approximately 107 languages. Tencent Translate and iFlytek’s systems similarly combine translation with broader educational and business applications.	神经机器翻译（NMT）和生成式人工智能的兴起从根本上改变了跨文化交流和语言教育的格局。本文考察了人工智能翻译工具——包括DeepL、ChatGPT以及百度翻译和有道等中国平台——对翻译实践、翻译劳动力市场和语言教学法所带来的变革。基于近期比较研究和劳动力市场数据，我们记录了专业翻译需求的急剧萎缩以及机器翻译译后编辑（MTPE）工作流程的扩展。我们认为，这一转变需要一种以"机器翻译素养"而非传统翻译能力为中心的新教育范式。通过对欧洲和中国应对这一转变的系统比较，我们发现了不同的监管路径和趋同的教学挑战。我们的结论是："翻译的终结"并非跨文化理解的终结，而是人类专长向不可还原的人类特质——文化细微差别、伦理判断和文学感受力——的重新定向。
A comparative quality assessment of Youdao AI Translate, Baidu Translate, and Tencent Translate for Chinese music historical texts using Likert scaling evaluation found significant variation in domain-specific accuracy, with none achieving consistent expert-level quality in specialized cultural content (Zhang et al. 2025). This finding underscores a recurring theme: AI translation excels at general-purpose communication but struggles with domain-specific, culturally embedded, and stylistically demanding texts.	(zu übersetzen)一项使用李克特量表评估方法对有道AI翻译、百度翻译和腾讯翻译在中国音乐历史文本上的比较质量评估发现，这些平台在特定领域的准确性上存在显著差异，没有一个平台在专业文化内容上达到一致的专家级质量（Zhang 等人，2025）。这一发现印证了一个反复出现的主题：人工智能翻译擅长通用性交流，但在特定领域、蕴含文化内涵和风格要求较高的文本上表现不佳。
'2.3 The State of the Art: Strengths and Persistent Weaknesses'	(zu übersetzen)2.3 技术现状：优势与持续存在的弱点
Current AI translation systems handle routine informational texts — news articles, business correspondence, technical documentation, user interfaces — at levels approaching or matching average human translators. The quality gap narrows further when post-editing is factored in. However, systematic weaknesses persist in several domains:	(zu übersetzen) 当前的人工智能翻译系统在处理常规信息类文本——新闻报道、商务信函、技术文档、用户界面——时，水平接近或等同于普通人类译者。当加入译后编辑环节时，质量差距进一步缩小。然而，在以下几个领域，系统性弱点依然存在：
Literary translation, where voice, rhythm, ambiguity, and cultural resonance are constitutive rather than incidental. Machine translation of poetry, for instance, remains largely inadequate, as documented by ongoing debates in the publishing industry about whether AI models are „advanced enough to translate literature“ (The Markup 2025).	(zu übersetzen)文学翻译——其中语气、节奏、歧义性和文化共鸣是构成性的，而非附带性的。例如，诗歌的机器翻译在很大程度上仍然不达标，出版业中关于AI模型是否"足够先进以翻译文学"的持续辩论也证明了这一点（The Markup 2025）。
Humour, irony, and sarcasm, which depend on shared cultural knowledge, contextual inference, and intentional violation of linguistic expectations. Machines can identify some patterns of irony through training data, but they cannot understand why something is funny or how irony functions rhetorically.	(zu übersetzen)幽默、反讽和挖苦——这些依赖于共享的文化知识、上下文推理以及对语言预期的有意违背。机器可以通过训练数据识别某些反讽模式，但它们无法理解某件事为何有趣，也无法理解反讽在修辞上是如何运作的。
Culturally embedded expressions that lack direct equivalents — not merely idioms (which NMT systems increasingly handle through pattern matching) but conceptual frameworks that reflect distinct worldviews. The Chinese philosophical term 仁 (rén), for example, resists translation into any single English word („benevolence,“ „humaneness,“ „goodness,“ „humanity“) because each option foregrounds different aspects of a concept that in Chinese encompasses all of them simultaneously.	(zu übersetzen)缺乏直接对应词的文化嵌入表达——不仅仅是习语（神经机器翻译系统正越来越多地通过模式匹配来处理习语），而是反映不同世界观的 conceptual frameworks。例如，中国哲学中的"仁"一词很难翻译成任何一个单一的英文单词（如 "benevolence"、"humaneness"、"goodness"、"humanity"），因为每个译词都凸显了该概念的不同侧面，而在中文中，"仁"同时涵盖了所有这些侧面。
Ethical and political sensitivity, where translation choices carry consequences beyond linguistic accuracy. The rendering of politically charged terms — „developing country,“ „human rights,“ „democracy“ — involves judgements that AI systems make based on statistical patterns in training data rather than ethical deliberation.	(zu übersetzen)伦理与政治敏感性——在这些领域，翻译选择带来的后果超越了语言准确性。对"发展中国家"、"人权"、"民主"等政治敏感术语的翻译，涉及到的判断是AI系统基于训练数据中的统计模式做出的，而非基于伦理考量。
'3.3 A Note on Evaluation Methodology'	(zu übersetzen)3.3 关于评估方法的说明
The evaluation of AI translation quality remains contested. Traditional metrics such as BLEU (Bilingual Evaluation Understudy) and more recent neural metrics like COMET and BERTScore capture different aspects of translation quality and sometimes yield contradictory results. Human evaluation, while considered the gold standard, is expensive, time-consuming, and subject to evaluator bias. The growing practice of using AI systems to evaluate other AI systems’ translations raises questions of circularity.	(zu übersetzen)对AI翻译质量的评估仍存在争议。传统的评估指标如BLEU，以及较新的神经指标如COMET和BERTScore，分别捕捉了翻译质量的不同方面，有时会得出相互矛盾的结果。人工评估虽然被认为是黄金标准，但成本高昂、耗时费力，并且容易受到评估者偏见的影响。而使用AI系统来评估其他AI系统翻译的做法日益增多，这引发了关于循环论证的问题。
For educational contexts, the most relevant evaluation criterion is not abstract quality but fitness for purpose. A machine translation used to help a student understand a foreign-language text has different quality requirements than one used for publication, legal proceedings, or diplomatic communication. This pragmatic perspective — which resonates with the Skopos Theory tradition in translation studies and with the Appropriateness framework proposed elsewhere in this volume — suggests that the question „Is machine translation good enough?“ can only be answered in relation to a specific purpose, context, and set of consequences.	(zu übersetzen)在教育语境中，最相关的评估标准不是抽象的质量，而是对目的的适用性。用于帮助学生理解外语文本的机器翻译，与用于出版、法律程序或外交交流的机器翻译，其质量要求是不同的。这种实用主义的视角——与翻译研究中的目的论传统以及本卷其他部分提出的" Appropriateness "框架相呼应——表明"机器翻译足够好吗？"这个问题只能针对特定的目的、语境和一系列后果来回答。
'4. Labour Market Transformation'劳动力市场的转型	(zu übersetzen)劳动力市场的转型
'4.1 The Contraction of Translation Demand'翻译需求的萎缩	翻译需求的萎缩
The impact of AI on the translation profession has been severe and rapid. Research by the Centre for Economic Policy Research (CEPR) using variation in Google Translate adoption across US local labour markets found that „areas with higher adoption experienced a decline in translator employment“ and that „improvements in machine translation have reduced the demand for foreign language skills in general“ (Frey & Llanos-Paredes 2025). Over three-quarters of professional translators surveyed in 2024 expected generative AI to adversely affect their future income.AI对翻译行业的影响即严重又迅速。经济政策研究中心利用谷歌翻译在美国地方劳动力市场中采纳程度的差异进行研究，发现“采纳程度更高的地区，议员就业人数下降”，并且“机器翻译的进步总体上降低了对多语言技能的需求”（Frey&Llanos-Paredes2025)。2024年接受调查的专业译员中，超过四分之三的人预计生成式AI将对其未来收入产生负面影响。	(zu übersetzen)AI对翻译行业的影响即严重又迅速。经济政策研究中心利用谷歌翻译在美国地方劳动力市场中采纳程度的差异进行研究，发现“采纳程度更高的地区，议员就业人数下降”，并且“机器翻译的进步总体上降低了对多语言技能的需求”（Frey&Llanos-Paredes2025)。2024年接受调查的专业译员中，超过四分之三的人预计生成式AI将对其未来收入产生负面影响。
The International Monetary Fund’s reduction of its translator staff from 200 to approximately 50 represents a concrete institutional example (CNN 2026). Duolingo laid off approximately 10 percent of its translation contractors in January 2024, shifting to AI-led content production. Translation agencies have been „increasingly switching to a business model revolving around MTPE, slashing rates and often compromising the quality of the final product“ (The Markup 2025).	“国际货币基金组织将其翻译人员从200人减少到约50人是一个具体的机构案例（CNN2026）”。Duolingo于2024年1月裁减了约10%的翻译承包商，转向人工智能主导的内容生产。翻译机构“越来越多地转向以MTPE为核心的商业模式，压低费率，且常常会损害最终产品的质量”（The Markup 2025)。
These figures must be interpreted with care. The translation industry encompasses a wide range of activities — from certified legal translation to website localization to literary translation to conference interpreting — and the impact of AI varies dramatically across these sub-sectors. The Bureau of Labor Statistics projects continued demand for interpreters and translators in the United States through 2032, driven by globalization and immigration, even as the nature of the work changes. What is contracting is not demand for cross-linguistic communication but demand for the traditional model of human-only translation of routine texts.对这些数据需要谨慎解读。翻译行业涵盖的活动范围很广-从认证法律翻译，网站本地化，文学翻译，到会议口译—AI对不同子领域的影响差异巨大。美国劳工统计局预测，尽管工作性质正在发生变化，但在全球化和移民的推动下，到2023年美国对口译员和笔译员的需求仍将持续存在。正在萎缩的，并不是跨语言沟通的本身需求。	(zu übersetzen)对这些数据需要谨慎解读。翻译行业涵盖的活动范围很广-从认证法律翻译，网站本地化，文学翻译，到会议口译—AI对不同子领域的影响差异巨大。美国劳工统计局预测，尽管工作性质正在发生变化，但在全球化和移民的推动下，到2023年美国对口译员和笔译员的需求仍将持续存在。正在萎缩的，并不是跨语言沟通的本身需求。
'4.2 The Rise of Post-Editing'	(zu übersetzen)译后编辑的兴起
The decline in traditional translation employment has been partially offset by the growth of machine translation post-editing (MTPE) as a professional activity. Post-editors review, correct, and refine machine-generated translations rather than translating from scratch. This represents a fundamental shift in the translator’s role — from author to editor, from creator to quality controller.	(zu übersetzen)传统翻译就业岗位的减少，在一定程度上被机器翻译译后编辑（MTEP)这一职业活动的增长有所抵消。译后编辑人员对机器生产的译文进行审校，修正和优化，而不是从零开始的翻译。这代表了译员角色的根本性转变—从作者转变为编辑，从创作转变为质量控制者。
Research on ChatGPT-4o’s potential for augmenting post-editing found that it „can complement human expertise in post-editing“ but „cannot provide completely accurate translations without human intervention,“ and that full integration would „significantly reduce costs, time, and effort“ (Chen 2025). Jiang, Wei, and Al-Shaibani (2025), studying NMT post-editing of Chinese intangible cultural heritage texts into English, documented how MTPE workflows can handle specialized cultural content while demonstrating that human expertise remains essential for culturally sensitive material.	(zu übersetzen)关于ChatGPT-4o在增强译后编辑中辅助人类专业知识，但如果没有人工干预，无法提供完全准确的翻译；完全整合将显著降低成本，时间和精力（Chen 2025)。Jiang Wei和AI-Shaibani(2025)在研究中国非物质文化遗产文本的NMT英译后编辑时，记录了MTPE工作流如何处理专门的文化内容，同时证明对于具有文化敏感性的材料，人类专业知识仍然不可或缺。
However, the post-editing model raises its own concerns. The practice of „post-editing fatigue“ — cognitive exhaustion from constantly evaluating and correcting machine output rather than engaging in creative translation — has been documented among professional translators (The Markup 2025). Some argue that post-editing trains the human mind to think like a machine rather than developing the cultural and literary sensitivity that distinguishes expert human translation.	(zu übersetzen)然而，译后编辑模式也引发了自身的担忧。“译后编辑疲劳”-即不断评估和纠正机器输出而非从事创造性翻译所带来的认知疲劳—已在专业译员中得到记录（The Markup2025)。一些人认为，译后编辑训练人的思维像机器一样运作，而不是培养区分人类专家翻译的文化与文学敏感性。
'4.3 Differentiated Impact'	(zu übersetzen)差异化的影响
The labour market impact is not uniform across translation domains. Routine business, technical, and administrative translation has been most affected, with AI systems handling these tasks at acceptable quality with minimal human intervention. Literary, legal, and diplomatic translation — domains where precision, nuance, and cultural sensitivity are paramount — have been less affected, though AI tools are increasingly used as first-draft generators even in these fields.	(zu übersetzen)劳动力市场的影响在翻译的不同领域并不均匀。常规的商业，技术和行政翻译受影响最大，AI系统可以在极少人工干预下以可接受的质量处理这些任务。而文学，法律和外交翻译-这些领域中精确性，细微差别和文化敏感性至关重要-受冲击相对较小，不过即使在这些领域，AI工具作为初稿生成器的使用也日益增多。
The geographic distribution of impact also varies. In Europe, where the translation profession is relatively well-organized and regulated, the transition has been accompanied by professional debate and some institutional responses. In China, where the translation market is larger but more fragmented, the shift has been faster and less mediated by professional organizations. Chinese translation companies have adopted AI tools aggressively, driven by market competition and the availability of domestic AI platforms optimized for Chinese language pairs.	(zu übersetzen)影响的区域分布也有所不同。在欧洲，翻译行业组织性较好，监管也相对完善。这一转型通常伴随着行业辩论和一定程度的制度性回应。而在中国，翻译市场规模更大但更加分散。在市场竞争和针对中文语对优化过的国产AI平台的推动下，中国翻译公司已积极采用AI工具。
'5. Machine Translation Literacy: A New Educational Paradigm'	(zu übersetzen)
'5.1 Defining MT Literacy'	(zu übersetzen)5.1 定义机器翻译素养
The concept of „machine translation literacy“ was developed by Bowker and Buitrago Ciro (2019) and further elaborated by Bowker (2023) in De-mystifying Translation: Introducing Translation to Non-translators. MT literacy encompasses several competencies: understanding how MT systems work (architecturally, not just functionally), understanding how MT systems are used in practice, appreciating the wider social and economic implications of MT, evaluating the MT-friendliness of source texts, creating or modifying texts for better MT output, and modifying MT output for quality and appropriateness.	"机器翻译素养"的概念由Bowker和Buitrago Ciro（2019）提出，并由Bowker（2023）在《揭秘翻译：向非译者介绍翻译》中进一步阐述。机器翻译素养涵盖多种能力：理解机器翻译系统的工作原理（从架构而非仅从功能角度）、理解机器翻译系统在实践中的使用方式、认识机器翻译更广泛的社会和经济影响、评估源文本的机器翻译友好性、创建或修改文本以获得更好的机器翻译输出，以及为保证质量和适切性而修改机器翻译输出。
This framework shifts the educational focus from translation competence (the ability to translate) to translation literacy (the ability to work effectively with translation technologies). The shift is analogous to the broader educational transformation from information production to information evaluation that characterizes the digital age.	(zu übersetzen)这一框架将教育重点从翻译能力（进行翻译的能力）转向翻译素养（有效使用翻译技术的能力）。这种转变类似于数字时代更广泛的教育转型——从信息生产转向信息评估。
'5.2 Student Preparedness: Current Gaps'	(zu übersetzen)5.2 学生准备情况：当前差距
A study investigating novice translation students’ AI literacy in translation education found that students „frequently mention terms like ‘big data,’ ‘deep learning,’ and ‘neural network’ but exhibit little knowledge of what these words mean or how they relate to AI translation tools“ (Zhang et al. 2025). This gap between superficial familiarity and genuine understanding characterizes the current state of translation education in many institutions.	(zu übersetzen)一项调查翻译教育中新手学生AI素养的研究发现，学生经常提及"大数据"、"深度学习"和"神经网络"等术语，但很少了解这些词的含义或它们与AI翻译工具的关系（Zhang 等人，2025）。这种表面熟悉与真正理解之间的差距，正是许多机构翻译教育当前状况的特征。
A survey of university students’ perceptions of using generative AI in translation practices found that GenAI tools offer advantages in „enhancing translation efficiency, quality, learning, and practice,“ fostering a positive outlook — but that this positive outlook may reflect uncritical acceptance rather than informed evaluation (Zhang et al. 2025). The challenge for educators is to cultivate critical engagement with AI tools without either demonizing or uncritically celebrating them.	(zu übersetzen)一项关于大学生在使用生成式AI进行翻译实践时看法的调查发现，生成式AI工具在提高翻译效率、质量、学习和实践方面具有优势，从而培养了积极的态度——但这种积极态度可能反映的是不加批判的接受，而非基于了解的评估（Zhang 等人，2025）。教育工作者面临的挑战是培养对AI工具的批判性参与能力，既不将其妖魔化，也不盲目追捧。
Research on machine translation in English language teaching found that Google Translate is the most popular MT tool among students, followed by DeepL and ChatGPT, primarily used for reading comprehension, grammar checking, and writing assistance (ELT Journal 2024). A significant finding was that „teachers tend to view MT as ‘a foe’ while students see it as ‘a friend’„ — a generational divide that complicates pedagogical responses.	(zu übersetzen)一项关于机器翻译在英语教学中应用的研究发现，谷歌翻译是学生中最受欢迎的机器翻译工具，其次是DeepL和ChatGPT，主要用于阅读理解、语法检查和写作辅助（ELT Journal 2024）。一个重要的发现是，教师倾向于将机器翻译视为"敌人"，而学生则将其视为"朋友"。
'5.3 Towards a New Professional Role'	(zu übersetzen) 5.3 迈向新的专业角色
The emergence of MT literacy as an educational paradigm has led to proposals for new professional roles. Ehrensberger-Dow, Delorme Benites, and Lehr (2023) proposed the role of „MT literacy consultant“ — a professional who bridges the gap between MT technology and pedagogical or organizational needs (Ehrensberger-Dow 2023). This role involves not only technical competence but also the ability to assess institutional needs, design MT integration strategies, and train end-users in effective MT use.	(zu übersetzen)机器翻译素养作为一种教育范式的出现，催生了新专业角色的提议。Ehrenberger-Dow、Delorme Benites 和 Lehr（2023）提出了机器翻译素养顾问的角色——这是一种弥合机器翻译技术与教学或组织需求之间差距的专业人员。该角色不仅涉及技术能力，还包括评估机构需求、设计机器翻译整合策略以及培训终端用户有效使用机器翻译的能力。
A comprehensive protocol for integrating human-AI collaboration into translation education was proposed in Ren et al. (2025), outlining assessment, diagnosis, and strategy development approaches that position human-AI collaboration as a core pedagogical framework rather than an add-on to traditional translation training.	(zu übersetzen)Ren 等人（2025）提出了一个将人机协作融入翻译教育的综合方案，概述了评估、诊断和策略开发等方法，将人机协作定位为核心教学框架，而非传统翻译训练的附加内容。
'6. Post-Editing in the Curriculum'	(zu übersetzen)6. 课程中的译后编辑
'6.1 MTPE as Core Competency'	(zu übersetzen)6.1 机器翻译译后编辑作为核心能力
The integration of MTPE into translation curricula is no longer optional but essential. Students entering the translation profession will almost certainly work with machine-generated first drafts rather than translating from blank pages. Curricula must therefore develop competencies in:	(zu übersetzen)将机器翻译译后编辑融入翻译课程已不再是可选项，而是必选项。进入翻译行业的学生几乎肯定会处理机器生成的初稿，而非从空白页面开始翻译。因此，课程必须培养以下几方面的能力：
Error detection and classification — identifying the types of errors that NMT and GenAI systems characteristically produce (omissions, additions, mistranslations, register errors, cultural inappropriateness).	(zu übersetzen) 错误检测与分类——识别神经机器翻译和生成式AI系统典型产生的错误类型（遗漏、添加、误译、语域错误、文化不恰当）。
Efficient editing strategies — distinguishing between „light post-editing„ (minimal corrections for gist comprehension) and „full post-editing“ (thorough revision to publication quality), and knowing when each is appropriate.	(zu übersetzen) 高效编辑策略——区分"轻度译后编辑"（为理解大意而进行的最小程度修正）和"充分译后编辑"（彻底修改以达到出版质量），并知道何时适用哪种方式。
Source text evaluation — assessing whether a text is suitable for MT or requires human translation, based on factors such as domain specificity, cultural embeddedness, register requirements, and the consequences of error.	(zu übersetzen)源文本评估——基于领域特异性、文化嵌入程度、语域要求以及错误可能带来的后果等因素，评估一篇文本是否适合由机器翻译，还是需要人工翻译。
Quality assessment — developing frameworks for evaluating MT output systematically, moving beyond impressionistic judgement to structured evaluation criteria.	(zu übersetzen)质量评估——建立系统性评估机器翻译输出的框架，从印象式判断转向结构化的评估标准。
'6.2 A Taxonomy of Machine Translation Errors'	(zu übersetzen)6.2 机器翻译错误分类法
Effective post-editing requires a systematic understanding of the types of errors that AI translation systems characteristically produce. Based on the research literature and the author’s own experience with Chinese-English and Chinese-German machine translation, we can identify the following recurring error categories:	(zu übersetzen)有效的译后编辑需要系统理解AI翻译系统典型产生的错误类型。基于研究文献以及作者在中英和中德机器翻译方面的经验，我们可以识别出以下几类反复出现的错误：
Omission errors: NMT systems sometimes skip portions of the source text, particularly subordinate clauses, parenthetical remarks, or culturally specific explanations. This is especially problematic in Chinese-to-English translation, where the absence of explicit grammatical markers (no articles, no obligatory plural marking, no inflected verb forms) means that the machine must infer structural relationships that may lead to simplification.	(zu übersetzen)
Addition errors: Conversely, GenAI systems — particularly ChatGPT — occasionally insert information not present in the source text, drawing on their training data to „complete“ what they perceive as incomplete information. This „hallucination“ problem, well-documented in other GenAI applications, manifests in translation as the insertion of explanatory phrases, implicit cultural knowledge, or inferred logical connections.	(zu übersetzen)
Register errors: Machine translation systems struggle with register — the level of formality, technicality, or intimacy appropriate to a given text type. A formal diplomatic communiqué translated at the register level of casual conversation, or a children’s story rendered in academic prose, may be semantically accurate but pragmatically inappropriate.	(zu übersetzen)
Cultural substitution errors: When faced with culturally specific references, AI systems sometimes substitute target-culture equivalents rather than preserving source-culture specificity. This is a manifestation of the domestication tendency that Venuti (1995) critiqued in human translation, now automated and therefore more pervasive.	(zu übersetzen)
Coherence errors: While individual sentences may be accurately translated, the coherence of longer texts — the logical flow of argument, the maintenance of thematic threads, the consistency of terminology — may be compromised by sentence-level processing that loses sight of discourse-level structure.	(zu übersetzen)
Understanding these error types is essential for effective post-editing and should form a core component of MTPE curricula.	(zu übersetzen)
'6.3 Challenges in MTPE Education'	(zu übersetzen)
Teaching MTPE effectively poses several pedagogical challenges. Students must first develop sufficient translation competence to recognize machine errors — one cannot effectively edit a translation one could not have produced or at least evaluated independently. This creates a paradox: the very competencies that MTPE seems to render obsolete (deep language knowledge, cultural understanding, stylistic sensitivity) are precisely those needed to perform MTPE well.	(zu übersetzen)
Furthermore, MTPE training risks reducing translation education to a technical exercise in error correction, neglecting the creative, cultural, and ethical dimensions that distinguish translation from other forms of text processing. Educators must find ways to develop both technical MTPE skills and the broader humanistic competencies that give those skills meaning and direction.	(zu übersetzen)
A promising pedagogical approach involves structured comparison exercises in which students evaluate the same source text translated by multiple systems (NMT, GenAI, and human translator) and analyze the characteristic strengths and weaknesses of each. Such exercises develop critical evaluation skills, build awareness of error types, and cultivate the metalinguistic awareness that distinguishes an informed post-editor from a passive corrector of machine output.	(zu übersetzen)
'7. European and Chinese Responses Compared'	(zu übersetzen)
'7.1 The European Approach'	(zu übersetzen)
European responses to the AI translation revolution have been shaped by several factors: the EU’s strong tradition of multilingualism (24 official languages), the relatively mature professionalization of translation through organizations such as the European Master’s in Translation (EMT) network, and the regulatory framework provided by the EU AI Act.	(zu übersetzen)
The EMT network has begun revising its competence framework to incorporate AI-related skills, though the pace of institutional change lags behind technological development. Several European universities have introduced dedicated MTPE courses or modules within existing translation programmes. The EU AI Act, which classifies certain educational AI applications as high-risk (see the companion chapter on AI Ethics in Education, Woesler, this volume), provides a regulatory context that encourages careful, ethically informed integration of AI tools rather than uncritical adoption.	(zu übersetzen)
A notable European development is the growing emphasis on „AI literacy„ as a general educational requirement. The EU AI Act mandates AI literacy training for deployers of AI systems, with this obligation having entered into force in February 2025. While this mandate targets organizations rather than students directly, it creates institutional incentives for universities to develop AI literacy curricula that include translation-related competencies.	(zu übersetzen)
The European Master’s in Translation (EMT) network, which coordinates translation training across more than 80 European universities, has begun a systematic review of its competence framework in light of AI developments. The 2024 review process identified machine translation literacy, post-editing skills, and AI ethics as priority areas for curriculum integration. However, the pace of implementation varies dramatically across member institutions — some have already restructured their programmes around human-AI collaboration, while others continue to teach translation skills as if the technological landscape had not fundamentally changed.	(zu übersetzen)
In Germany specifically, the Conference of University Translation Departments (Konferenz der Universitätstranslationsinstitute) has debated the extent to which traditional translation examinations — which typically require students to translate unseen texts under time pressure without technological aids — remain appropriate as assessment instruments. The emerging consensus favours a dual approach: retaining elements of traditional translation competence assessment while adding new evaluation forms that test post-editing ability, MT output evaluation, and critical reflection on AI tools.	(zu übersetzen)
France has taken a somewhat different approach, with the Société française des traducteurs (SFT) publishing guidelines for the integration of AI tools into professional practice that have influenced university curricula. These guidelines emphasize the translator’s evolving role as a „language technology expert“ who can advise clients on the appropriate use of machine and human translation for different purposes.	(zu übersetzen)
'7.2 The Chinese Approach'	(zu übersetzen)
China‘s response has been characterized by rapid institutional adoption, state-level policy direction, and the availability of domestic AI platforms. The Chinese Ministry of Education mandated AI education in all primary and secondary schools from September 2025 — primary schools focus on AI literacy and exposure, junior high schools on logic and critical thinking, and senior high schools on applied innovation and algorithm design. At the university level, Chinese institutions have been quicker than many European counterparts to integrate AI tools into language and translation curricula, driven partly by market competition and partly by state encouragement of AI adoption.	(zu übersetzen)
The domestic AI ecosystem — Baidu Translate, Youdao with its Ziyue educational model, Tencent Translate, iFlytek — provides Chinese students and educators with AI tools specifically optimized for Chinese language pairs and integrated with broader educational platforms. This contrasts with the European reliance on primarily Western commercial tools (DeepL, Google Translate, ChatGPT), which are optimized for European language combinations and may not serve Chinese-European language pairs as effectively.	(zu übersetzen)
However, China‘s approach also faces distinctive challenges. The restriction of access to global AI tools such as ChatGPT, Claude, and Google Gemini means that Chinese students and researchers work primarily with domestic alternatives, which may limit their exposure to the full range of AI translation capabilities and approaches. The emphasis on rapid adoption may also come at the expense of the critical evaluation and ethical reflection that European institutions, influenced by the AI Act framework, are beginning to prioritize.	(zu übersetzen)
A further dimension of the Chinese response is the integration of AI translation with broader national strategies. The State Council’s „New Generation Artificial Intelligence Development Plan“ (2017) and „Education Modernization 2035“ both frame AI capabilities — including translation — as strategic national competencies. This means that the adoption of AI translation tools in Chinese education is not merely a pedagogical decision but a contribution to national technological development. Chinese universities consequently face different incentive structures than their European counterparts: where European institutions may be cautious about AI adoption due to concerns about academic integrity and human skill degradation, Chinese institutions face institutional pressure to demonstrate AI integration as evidence of modernization and alignment with national policy.	(zu übersetzen)
The integration of AI translation into China‘s college English education system illustrates this dynamic. The College English Curriculum Requirements (2024 revision) explicitly include „the ability to use AI tools for cross-cultural communication“ as a graduate competency. Several major Chinese universities — including Peking University, Fudan University, and Hunan Normal University — have introduced modules on AI-assisted translation and communication within their foreign language programmes, often taught by faculty who are themselves learning to navigate the new technological landscape.	(zu übersetzen)
'7.3 Convergent Challenges'	(zu übersetzen)
Despite these differences, European and Chinese institutions face convergent challenges:	(zu übersetzen)
Curriculum lag — both systems struggle to update curricula at the pace of technological change, with accreditation processes and institutional inertia slowing adaptation.	(zu übersetzen)
Faculty preparedness — in both contexts, many translation faculty lack personal experience with AI tools and are uncertain about how to integrate them effectively into their teaching.	(zu übersetzen)
Assessment design — traditional translation examinations that test the ability to translate texts from scratch become problematic when students have access to AI tools, necessitating new assessment approaches that evaluate MT literacy, critical evaluation, and post-editing competence rather than raw translation ability.	(zu übersetzen)
Balancing technical and humanistic education — both systems must navigate the tension between training students for the immediate demands of the MTPE-dominated market and developing the broader cultural, ethical, and creative competencies that distinguish human translators from machines.	(zu übersetzen)
'8. Beyond Machine Translation: What Remains Irreducibly Human'	(zu übersetzen)
'8.1 Literary and Cultural Translation'	(zu übersetzen)
The domain where human translators remain most clearly indispensable is literary and cultural translation. Literary translation involves not merely converting meaning from one language to another but recreating voice, rhythm, ambiguity, humour, irony, and aesthetic effect in a different linguistic and cultural context. A machine can translate the words of a poem; it cannot translate the poem.	(zu übersetzen)
This is not merely an assertion of humanistic faith against technological progress. It reflects the fundamental difference between pattern matching (which AI excels at) and meaning-making (which requires embodied cultural experience, emotional understanding, and aesthetic judgement). When Emily Wilson translates Homer’s Odyssey or Pevear and Volokhonsky retranslate Dostoevsky, they bring not only linguistic expertise but personal interpretive vision — a quality that distinguishes great translation from competent rendering.	(zu übersetzen)
'8.2 Ethical Judgement in Translation'	(zu übersetzen)
The companion chapter on Appropriateness Theory (Woesler, this volume) argues that translation cannot be evaluated purely on linguistic criteria but must be assessed within an ethical framework that considers the conditions of production, the historical context, and the potential consequences of translation choices. This ethical dimension — the ability to recognize when a translation choice may cause harm, when cultural sensitivity demands deviation from literal accuracy, when political context requires careful framing — remains beyond the current capabilities of AI systems.	(zu übersetzen)
AI translation systems make choices based on statistical patterns in training data. They cannot exercise ethical judgement in the sense of weighing competing values and accepting responsibility for the consequences of those choices. The post-editor who reviews machine output and decides that a particular rendering, while linguistically accurate, is culturally inappropriate or politically sensitive exercises a form of human agency that no current AI system can replicate.	(zu übersetzen)
'8.3 The „Complementarity Thesis“ Extended'	(zu übersetzen)
The empirical study in this volume (Woesler, this volume) found that AI and human instruction serve „different, complementary functions“ in language learning — what we termed the Complementarity Thesis. This thesis extends naturally to translation: AI and human translators serve different, complementary functions. AI excels at speed, consistency, coverage, and the handling of routine informational texts. Human translators excel at cultural interpretation, literary creativity, ethical judgement, and the handling of texts where precision and nuance carry high stakes.	(zu übersetzen)
The future of translation is neither the replacement of humans by machines nor the preservation of traditional human-only translation practice. It is a collaborative ecology in which the division of labour between human and machine is continually renegotiated as AI capabilities evolve and as our understanding of what constitutes „good translation“ deepens.	(zu übersetzen)
'8.4 The Case of Chinese Literature in Translation'	(zu übersetzen)
A concrete illustration of the limits of AI translation can be drawn from the author’s own experience translating Chinese literary texts — including works by Lu Xun and classical Chinese novels — into German and English. Consider the opening line of Lu Xun’s 狂人日记 (Diary of a Madman, 1918): „今天晚上，很好的月光.“ A machine translation produces something like „Tonight, very good moonlight“ — grammatically adequate but aesthetically dead. The challenge for the human translator is to convey not only the semantic content but the deliberately flat, unsettling tone that establishes the narrator’s mental state: the juxtaposition of ordinary observation („very good moonlight“) with the extraordinary context of emerging madness.	人工智能翻译局限性的一个具体例证可以从作者自己翻译中国文学文本——包括鲁迅作品和古典中国小说——为德语和英语的经验中得出。考虑鲁迅《狂人日记》（1918年）的开头一行："今天晚上，很好的月光。"机器翻译产出的译文类似"Tonight, very good moonlight"——语法上尚可但审美上毫无生气。人类译者面临的挑战是传达不仅仅是语义内容，还有那种有意平淡、令人不安的语调——这种语调确立了叙述者的精神状态：普通观察（"很好的月光"）与疯狂浮现的非凡语境之间的并置。
Similarly, the translation of classical Chinese poetry — where a single character may carry multiple simultaneous meanings, where tonal patterns create musical structures, and where cultural allusions operate on several levels — remains fundamentally beyond current AI capabilities. Tang dynasty poetry, with its strict formal requirements (律诗 lüshi: regulated verse with tonal patterns, parallelism, and compressed imagery), resists not only machine translation but any translation that does not involve deep engagement with Chinese literary tradition, Buddhist and Daoist philosophical frameworks, and the specific historical contexts of individual poets.	同样，古典中国诗歌的翻译——其中一个字可能承载多重同时含义，声调模式创造音乐结构，文化典故在多个层面上运作——从根本上超出了当前人工智能的能力。唐诗及其严格的形式要求（律诗：具有声调模式、对仗和压缩意象的格律诗），不仅抵抗机器翻译，也抵抗任何不涉及对中国文学传统、佛道哲学框架和个别诗人特定历史语境之深入理解的翻译。
These examples are not mere curiosities but illustrations of a general principle: the more a text depends on cultural embeddedness, aesthetic effect, and multi-layered meaning, the less adequate AI translation becomes. This principle has direct implications for education, suggesting that the study of literary translation — far from being an obsolete specialization — may become the most distinctively human and therefore most valuable component of translation education.	(zu übersetzen)
'9. Conclusion'	(zu übersetzen)
The „end of translation“ is a misleading phrase if understood literally. What is ending is translation as a routine professional service performed predominantly by humans. What is emerging is a new ecology of human-machine collaboration that demands new competencies, new professional roles, new curricula, and new forms of ethical reflection.	(zu übersetzen)
For education, this transformation demands nothing less than a reconceptualization of what it means to teach and learn translation. Machine translation literacy — encompassing technical understanding, critical evaluation, effective post-editing, and ethical judgement — must become central to language and translation curricula. At the same time, the humanistic competencies that machines cannot replicate — cultural sensitivity, literary sensibility, ethical reasoning, creative interpretation — must be preserved and strengthened rather than abandoned as obsolete.	(zu übersetzen)
The comparison of European and Chinese responses reveals a productive tension between the EU’s cautious, regulation-informed approach and China‘s rapid, state-supported adoption of AI tools. Neither approach is sufficient alone. The EU’s emphasis on ethical frameworks and critical evaluation provides necessary guardrails for responsible AI integration. China’s speed and scale of adoption generate practical experience and institutional adaptation that the EU can learn from. A synthesis of both approaches — combining ethical rigour with practical agility — offers the most promising path forward.	(zu übersetzen)
Several practical recommendations emerge from this analysis. First, translation and language programmes should introduce machine translation literacy as a core competency from the first semester, ensuring that students develop critical evaluation skills alongside traditional language competencies. Second, MTPE should be taught not merely as a technical skill but within a framework that includes error taxonomy, quality assessment methodology, and ethical reflection on the consequences of translation choices. Third, literary and cultural translation should be preserved and strengthened as the domain where human expertise remains most clearly irreplaceable — and most clearly valuable in an AI-saturated landscape. Fourth, EU-China academic cooperation in translation education should be intensified, leveraging the complementary strengths of the European emphasis on regulation and ethical frameworks with the Chinese emphasis on practical AI integration and scale.	(zu übersetzen)
For the broader field of educational digitalization, the „end of translation“ case study offers a cautionary tale and an encouraging precedent. Cautionary, because the speed and scale of AI disruption can outpace institutional adaptation, leaving students inadequately prepared for a transformed professional landscape. Encouraging, because the transformation does not eliminate the need for human expertise but redirects it — from routine execution to critical evaluation, creative interpretation, and ethical judgement. These are precisely the competencies that higher education is best positioned to develop.	(zu übersetzen)
As this anthology demonstrates through its companion chapters on AI-assisted language learning, alternative learning forms, AI ethics, and the university of the future, the transformation wrought by AI in education is not a single phenomenon but a constellation of interconnected changes that must be addressed holistically. The end of translation as we knew it is, simultaneously, the beginning of something new — a form of cross-cultural communication in which human and artificial intelligence collaborate to bridge the gaps between languages, cultures, and worldviews.	(zu übersetzen)
'Acknowledgments'	(zu übersetzen)
This research was supported by the Jean Monnet Centre of Excellence „EU-Studies Centre: Digitalization in Europe and China„ (EUSC-DEC), funded by the European Union under Grant Agreement No. 101126782. Views and opinions expressed are those of the author only and do not necessarily reflect those of the European Union.	(zu übersetzen)
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