Rethinking Higher Education/Chapter 4/en-zh

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Chapter 4: The End of Translation

Martin Woesler

English (Source)	中文 (Target)
== The End of Translation: How AI is Transforming Cross-Cultural Communication in Education ==	== 翻译的终结：人工智能如何变革教育中的跨文化交流 ==
Martin Woesler	Martin Woesler
Hunan Normal University	湖南师范大学
Abstract	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.	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。
Keywords: machine translation, AI in education, DeepL, ChatGPT, post-editing, translation literacy, EU-China comparison, language education	关键词：机器翻译、教育中的人工智能、DeepL、ChatGPT、译后编辑、翻译素养、欧中比较、语言教育
1. Introduction	1. 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?	然而，这一挑衅性的表述需要加以限定。正在终结的不是翻译作为一种智力和文化活动，而是翻译作为一种主要由人类执行的常规专业服务。正在出现的是一种新的人机协作生态，在这种生态中，人类的角色从生产翻译转向评估、完善和语境化机器输出。对于教育——特别是欧洲和中国大学的语言系和翻译项目——这一转变提出了紧迫的问题。我们应该教给那些将进入一个与二十世纪前身截然不同的职业的学生什么？我们如何培养机器无法复制的独特人类能力？欧洲和中国这两种不同的教育体系如何应对这些挑战？
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).	本文通过考察人工智能翻译技术的现状、其劳动力市场后果、"机器翻译素养"的新兴概念、译后编辑在教育中的角色以及欧洲和中国机构的不同应对来回答这些问题。本文通过将人工智能在教育中的技术维度与配套章节中提出的适切性理论伦理框架（Woesler，本卷）和人工智能辅助语言学习的实证发现（Woesler，本卷）联系起来，为更广泛的论文集做出贡献。
2. Historical Context: Translation and Technology	2. 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.	在考察当前人工智能驱动的转型之前，有必要将其置于技术与翻译关系的更长历史中。从最广义上讲，翻译一直受到技术的中介——从使翻译作为文本实践成为可能的书写发明，到创造了对翻译作品大量需求的印刷术，再到改变了译者物理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.	最常被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，但它确实消灭了绘画对视觉再现的垄断，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、概念艺术。同样，人工智能翻译可能不会消灭人工翻译，但它正在消灭人工翻译对跨语言交流的垄断，有望使人类译者专注于机器无法复制的跨文化意义创造的维度。
3. The AI Translation Landscape: Tools and Capabilities	3. 人工智能翻译格局：工具与能力
3.1 Neural Machine Translation vs. Generative AI	3.1 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.	专用神经机器翻译（NMT）系统与生成式人工智能（GenAI）工具之间的区别对于理解当前格局至关重要。正如Ohashi（2024）在其系统比较中所展示的，谷歌翻译和DeepL等NMT系统是专为翻译而构建的，在平行语料库上训练，针对特定语言对的流畅性和充分性进行优化。ChatGPT、Claude和中国的文心一言等GenAI工具将翻译作为众多能力之一来处理，利用更广泛的语言理解，但缺乏专用NMT系统的专业训练数据。
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	3.2 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.	中国的人工智能翻译生态系统值得单独关注，因为它在很大程度上独立于西方生态系统运作。百度翻译支持203种语言，包括稀有方言和古典中文，使用具有上下文感知句子级处理能力的NMT技术。有道翻译由网易开发，2023年发布了"子曰"教育大语言模型，随后在2025年推出子曰-o1推理模型，将翻译能力与深度词典和学习工具功能整合，涵盖约107种语言。腾讯翻译和科大讯飞的系统同样将翻译与更广泛的教育和商业应用相结合。
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.	一项使用Likert量表对有道人工智能翻译、百度翻译和腾讯翻译在中国音乐史文本翻译方面的比较质量评估发现，在专业领域准确性方面存在显著差异，没有一个系统在专业文化内容方面达到了一致的专家级质量（Zhang等人 2025）。这一发现印证了一个反复出现的主题：人工智能翻译在通用交流方面表现出色，但在专业领域、文化嵌入型和风格要求高的文本方面力有不逮。
2.3 The State of the Art: Strengths and Persistent Weaknesses	3.3 技术现状：优势与持续弱点
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:	当前的人工智能翻译系统处理常规信息性文本——新闻文章、商业信函、技术文档、用户界面——的水平接近或匹配普通人类译者。当考虑译后编辑因素时，质量差距进一步缩小。然而，在若干领域依然存在系统性弱点：
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).	文学翻译，其中声音、节奏、歧义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足，正如出版业关于人工智能模型是否"足够先进以翻译文学"的持续论争所记录的那样（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.	幽默、讽刺和反语，这些依赖于共享的文化知识、语境推理和对语言期望的有意违背。机器可以通过训练数据识别一些讽刺模式，但它们无法理解某事为什么有趣或讽刺在修辞上如何运作。
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.	文化嵌入式表达缺乏直接等价物——不仅仅是习语（NMT系统越来越多地通过模式匹配来处理），还有反映不同世界观的概念框架。例如，中国哲学术语"仁"（rén）无法翻译成任何单一的英文词汇（"benevolence"、"humaneness"、"goodness"、"humanity"），因为每个选项都突出了一个概念的不同面向，而在中文中该概念同时涵盖了所有这些面向。
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.	伦理和政治敏感性，其中翻译选择所产生的后果超出了语言准确性的范围。政治敏感术语的翻译——"发展中国家"、"人权"、"民主"——涉及的判断基于训练数据中的统计模式而非伦理审慎。
3.3 A Note on Evaluation Methodology	3.4 关于评估方法论的说明
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.	人工智能翻译质量的评估仍然存在争议。传统指标如BLEU（双语评估替补）和更新的神经指标如COMET和BERTScore捕捉翻译质量的不同方面，有时会产生矛盾的结果。人工评估虽然被视为金标准，但成本高昂、耗时且受评估者偏见的影响。越来越多地使用人工智能系统来评估其他人工智能系统翻译的做法引发了循环论证的问题。
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.	对于教育情境而言，最相关的评估标准不是抽象的质量而是目的适切性。用于帮助学生理解外语文本的机器翻译与用于出版、法律诉讼或外交交流的翻译有不同的质量要求。这种务实的视角——与翻译研究中的目的论（Skopos Theory）传统以及本卷其他章节提出的适切性框架相呼应——表明"机器翻译够好吗？"这个问题只能相对于特定的目的、语境和后果来回答。
4. Labour Market Transformation	4. 劳动力市场转型
4.1 The Contraction of Translation Demand	4.1 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.	人工智能对翻译职业的影响是严重而迅速的。经济政策研究中心（CEPR）利用谷歌翻译在美国各地方劳动力市场的采用变化进行的研究发现，"采用率更高的地区出现了译者就业的下降"，且"机器翻译的改进总体上减少了对外语技能的需求"（Frey & Llanos-Paredes 2025）。2024年接受调查的四分之三以上的专业译者预计生成式人工智能将对其未来收入产生不利影响。
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（CNN 2026）。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.	这些数据必须审慎解读。翻译行业涵盖了广泛的活动——从认证法律翻译到网站本地化，再到文学翻译和会议口译——人工智能的影响在这些子行业之间差异巨大。美国劳工统计局预计，到2032年美国对口译员和笔译员的需求将持续增长，受全球化和移民推动，即使工作性质发生变化。正在萎缩的不是对跨语言交流的需求，而是对人类独立翻译常规文本这一传统模式的需求。
4.2 The Rise of Post-Editing	4.2 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.	传统翻译就业的下降部分被机器翻译译后编辑（MTPE）作为一种专业活动的增长所抵消。译后编辑人员审查、纠正和完善机器生成的翻译，而不是从零开始翻译。这代表了译者角色的根本性转变——从作者到编辑，从创造者到质量控制员。
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.	关于ChatGPT-4o在增强译后编辑方面潜力的研究发现，它"可以补充人类在译后编辑方面的专业知识"，但"不能在没有人工干预的情况下提供完全准确的翻译"，全面整合将"显著降低成本、时间和精力"（Chen 2025）。Jiang、Wei和Al-Shaibani（2025）研究了中国非物质文化遗产文本的中译英NMT译后编辑，记录了MTPE工作流程如何处理专业文化内容，同时证明人类专业知识对文化敏感材料仍然不可或缺。
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.	然而，译后编辑模式也引发了自身的问题。"译后编辑疲劳"——由不断评估和纠正机器输出而非进行创造性翻译而导致的认知耗竭——已在专业译者中得到记录（The Markup 2025）。一些人认为，译后编辑训练人类大脑像机器一样思考，而非发展区分专家级人工翻译的文化和文学敏感性。
4.3 Differentiated Impact	4.3 差异化影响
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.	劳动力市场影响在翻译各领域中并不均匀。常规商业、技术和行政翻译受影响最大，人工智能系统以可接受的质量和最少的人工干预处理这些任务。文学、法律和外交翻译——精确性、细微差别和文化敏感性至关重要的领域——受影响较小，尽管人工智能工具越来越多地被用作这些领域的初稿生成器。
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.	影响的地理分布也各不相同。在欧洲，翻译职业组织相对完善且受到监管，转型伴随着专业辩论和一些机构应对。在中国，翻译市场规模更大但更碎片化，转变更快且较少受到专业组织的调节。中国翻译公司积极采用人工智能工具，驱动力来自市场竞争以及针对中文语言对优化的国内人工智能平台的可用性。
5. Machine Translation Literacy: A New Educational Paradigm	5. 机器翻译素养：一种新的教育范式
5.1 Defining MT Literacy	5.1 机器翻译素养的定义
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.	这一框架将教育重点从翻译能力（翻译的能力）转向翻译素养（有效利用翻译技术的能力）。这种转变类似于数字时代所特有的更广泛教育转型——从信息生产到信息评估。
5.2 Student Preparedness: Current Gaps	5.2 学生准备度：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.	一项对翻译教育中新手翻译学生人工智能素养的调查发现，学生"经常提到'大数据'、'深度学习'和'神经网络'等术语，但对这些词语的含义或它们与人工智能翻译工具的关系知之甚少"（Zhang等人 2025）。表面熟悉与真正理解之间的差距是许多机构翻译教育现状的典型特征。
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.	一项关于大学生对在翻译实践中使用生成式人工智能看法的调查发现，GenAI工具在"增强翻译效率、质量、学习和实践"方面提供了优势，培养了积极的前景——但这种积极前景可能反映的是不加批判的接受而非知情的评估（Zhang等人 2025）。教育者面临的挑战是培养对人工智能工具的批判性参与，既不妖魔化也不不加批判地赞美它们。
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.	关于英语教学中机器翻译的研究发现，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）。一个重要发现是"教师倾向于将机器翻译视为'敌人'，而学生将其视为'朋友'"——这种代际分歧使教学应对更加复杂。
5.3 Towards a New Professional Role	5.3 走向新的专业角色
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.	机器翻译素养作为教育范式的出现催生了对新专业角色的提议。Ehrensberger-Dow、Delorme Benites和Lehr（2023）提出了"机器翻译素养顾问"的角色——一位在机器翻译技术与教学或组织需求之间架起桥梁的专业人士（Ehrensberger-Dow 2023）。这一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和培训最终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.	Ren等人（2025）提出了一个将人机协作整合到翻译教育中的全面方案，概述了评估、诊断和策略制定方法，将人机协作定位为核心教学框架而非传统翻译培训的附加项。
6. Post-Editing in the Curriculum	6. 课程中的译后编辑
6.1 MTPE as Core Competency	6.1 MTPE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:	将MTPE整合到翻译课程中不再是可选的，而是必不可少的。进入翻译行业的学生几乎肯定将使用机器生成的初稿，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).	错误检测和分类——识别NMT和GenAI系统特征性产生的错误类型（遗漏、添加、误译、语域错误、文化不当）。
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.	高效编辑策略——区分"轻度译后编辑"（用于大意理解的最少纠正）和"完全译后编辑"（达到出版质量的全面修订），以及了解每种方式何时适用。
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.	源文本评估——根据领域专业性、文化嵌入性、语域要求和错误后果等因素，评估文本是否适合机器翻译或需要人工翻译。
Quality assessment — developing frameworks for evaluating MT output systematically, moving beyond impressionistic judgement to structured evaluation criteria.	质量评估——开发系统评估机器翻译输出的框架，超越印象式判断，走向结构化评估标准。
6.2 A Taxonomy of Machine Translation Errors	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:	有效的译后编辑需要系统理解人工智能翻译系统特征性产生的错误类型。基于研究文献和作者在中英、中德机器翻译方面的自身经验，我们可以确定以下反复出现的错误类别：
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.	遗漏错误：NMT系统有时会跳过源文本的部分内容，特别是从句、括号内容或文化特定解释。这在中译英翻译中尤为突出，因为缺乏显式语法标记（没有冠词、没有强制复数标记、没有屈折动词形式）意味着机器必须推断可能导致简化的结构关系。
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.	添加错误：相反，GenAI系统——特别是ChatGPT——偶尔会插入源文本中不存在的信息，从其训练数据中提取以"补全"它们认为不完整的信息。这种"幻觉"问题在其他GenAI应用中有充分记录，在翻译中表现为插入解释性短语、隐含文化知识或推断的逻辑联系。
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.	语域错误：机器翻译系统在处理语域——对给定文本类型适当的正式度、技术性或亲密度水平——方面存在困难。以随意对话语域翻译的正式外交公报，或以学术文体呈现的儿童故事，可能在语义上准确但在语用上不当。
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.	文化替代错误：面对文化特定引用时，人工智能系统有时会替换目标文化的等价物，而非保留源文化的特异性。这是Venuti（1995）在人工翻译中批评的归化倾向的一种表现，如今被自动化因而更加普遍。
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.	连贯性错误：虽然单个句子可能被准确翻译，但更长文本的连贯性——论证的逻辑流转、主题线索的维护、术语的一致性——可能因句子级处理而受到损害，丧失了对语篇层面结构的整体把握。
Understanding these error types is essential for effective post-editing and should form a core component of MTPE curricula.	理解这些错误类型对于有效的译后编辑至关重要，应成为MTPE课程的核心组成部分。
6.3 Challenges in MTPE Education	6.3 MTPE教育中的挑战
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.	有效教授MTPE面临若干教学挑战。学生必须首先具备足够的翻译能力来识别机器错误——一个人无法有效编辑自己无法独立产出或至少独立评估的翻译。这就产生了一个悖论：MTPE似乎使之过时的那些能力（深厚的语言知识、文化理解、风格敏感性）恰恰是良好执行MTPE所需要的。
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.	此外，MTPE训练有将翻译教育简化为错误纠正技术练习的风险，忽视了将翻译与其他文本处理形式区分开来的创造性、文化和伦理维度。教育者必须找到同时发展技术MTPE技能和赋予这些技能意义与方向的更广泛人文能力的方法。
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.	一种颇具前景的教学方法是结构化比较练习，让学生评估同一源文本由多个系统（NMT、GenAI和人类译者）翻译的结果，并分析每个系统的特征性优势和弱点。这种练习培养批判性评估技能，建立对错误类型的认知，并培养区分知情译后编辑者和被动的机器输出纠正者的元语言意识。
7. European and Chinese Responses Compared	7. 欧洲和中国的应对比较
7.1 The European Approach	7.1 欧洲的方法
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.	欧洲对人工智能翻译革命的应对受到几个因素的影响：欧盟强大的多语言传统（24种官方语言）、通过欧洲翻译硕士（EMT）网络等组织实现的翻译职业化的相对成熟，以及欧盟《人工智能法》提供的监管框架。
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.	EMT网络已开始修订其能力框架以纳入人工智能相关技能，尽管机构变革的速度落后于技术发展。多所欧洲大学在现有翻译项目中引入了专门的MTPE课程或模块。欧盟《人工智能法》将某些教育人工智能应用归类为高风险（参见本卷关于教育中人工智能伦理的配套章节，Woesler），提供了一个鼓励谨慎、以伦理为导向地整合人工智能工具而非不加批判地采用的监管背景。
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.	欧洲一个值得注意的发展是越来越强调将"人工智能素养"作为一般教育要求。欧盟《人工智能法》要求人工智能系统的部署者接受人工智能素养培训，该义务于2025年2月生效。虽然这一要求针对的是组织而非直接针对学生，但它为大学开发包含翻译相关能力的人工智能素养课程创造了制度激励。
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.	欧洲翻译硕士（EMT）网络协调了80多所欧洲大学的翻译培训，已开始根据人工智能发展对其能力框架进行系统审查。2024年的审查过程确定了机器翻译素养、译后编辑技能和人工智能伦理作为课程整合的优先领域。然而，各成员机构之间的实施速度差异巨大——一些机构已经围绕人机协作重新构建了其项目，而另一些则继续以技术格局没有根本改变的方式教授翻译技能。
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.	在德国，大学翻译系会议（Konferenz der Universitätstranslationsinstitute）就传统翻译考试——通常要求学生在没有技术辅助的时间压力下翻译未见过的文本——作为评估工具是否仍然适当进行了辩论。新兴的共识倾向于双重方法：保留传统翻译能力评估的要素，同时增加测试译后编辑能力、机器翻译输出评估和对人工智能工具批判性反思的新评估形式。
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.	法国采取了略有不同的方法，法国翻译协会（Société française des traducteurs, SFT）发布了将人工智能工具整合到专业实践中的指南，这些指南影响了大学课程设置。这些指南强调译者正在演变为"语言技术专家"的角色，能够就不同目的下机器翻译和人工翻译的适当使用为客户提供建议。
7.2 The Chinese Approach	7.2 中国的方法
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.	中国的应对特征是快速的机构采用、国家层面的政策指导以及国内人工智能平台的可用性。中国教育部自2025年9月起在所有中小学强制实施人工智能教育——小学侧重于人工智能素养和接触，初中侧重于逻辑和批判性思维，高中侧重于应用创新和算法设计。在大学层面，中国机构将人工智能工具整合到语言和翻译课程中的速度比许多欧洲同行更快，部分源于市场竞争的驱动，部分源于国家对采用人工智能的鼓励。
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.	国内人工智能生态系统——百度翻译、有道及其子曰教育模型、腾讯翻译、科大讯飞——为中国学生和教育者提供了专门针对中文语言对优化并与更广泛教育平台整合的人工智能工具。这与欧洲主要依赖西方商业工具（DeepL、谷歌翻译、ChatGPT）形成对比，后者针对欧洲语言组合优化，在服务中欧语言对方面可能不够理想。
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.	然而，中国的方法也面临独特的挑战。对ChatGPT、Claude和Google Gemini等全球人工智能工具的访问限制意味着中国学生和研究者主要使用国内替代品，这可能限制了他们对人工智能翻译能力和方法全貌的接触。对快速采用的强调也可能以牺牲批判性评估和伦理反思为代价——而这正是受人工智能法框架影响的欧洲机构开始优先考虑的方面。
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.	中国应对的另一个维度是将人工智能翻译与更广泛的国家战略相整合。国务院的"新一代人工智能发展规划"（2017年）和"教育现代化2035"都将包括翻译在内的人工智能能力界定为国家战略能力。这意味着在中国教育中采用人工智能翻译工具不仅仅是一个教学决策，更是对国家技术发展的贡献。因此，中国大学面临的激励结构与其欧洲同行不同：欧洲机构可能由于对学术诚信和人类技能退化的担忧而对人工智能采用持谨慎态度，而中国机构面临着将人工智能整合作为现代化和与国家政策一致之证据的制度压力。
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.	人工智能翻译融入中国大学英语教育体系说明了这一动态。《大学英语课程要求》（2024年修订版）明确将"使用人工智能工具进行跨文化交流的能力"列为毕业生能力要求。包括北京大学、复旦大学和湖南师范大学在内的多所中国重点大学在其外语项目中引入了人工智能辅助翻译和交流模块，通常由自身也在学习驾驭新技术格局的教职人员教授。
7.3 Convergent Challenges	7.3 趋同的挑战
Despite these differences, European and Chinese institutions face convergent challenges:	尽管存在上述差异，欧洲和中国机构面临着趋同的挑战：
Curriculum lag — both systems struggle to update curricula at the pace of technological change, with accreditation processes and institutional inertia slowing adaptation.	课程滞后——两个体系都难以按照技术变革的速度更新课程，认证过程和机构惯性减缓了适应速度。
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.	教职人员准备度——在两种背景下，许多翻译教职人员缺乏使用人工智能工具的亲身经验，不确定如何将它们有效整合到教学中。
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.	评估设计——当学生可以使用人工智能工具时，测试从零开始翻译文本能力的传统翻译考试变得有问题，需要新的评估方法来评价机器翻译素养、批判性评估和译后编辑能力，而非单纯的翻译能力。
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.	平衡技术和人文教育——两个体系都必须在为MTPE主导的市场的即时需求培训学生和发展区分人类译者与机器的更广泛文化、伦理和创造能力之间寻求平衡。
8. Beyond Machine Translation: What Remains Irreducibly Human	8. 超越机器翻译：不可还原的人类特质
8.1 Literary and Cultural Translation	8.1 文学和文化翻译
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.	人类译者最明显不可或缺的领域是文学和文化翻译。文学翻译不仅仅是将意义从一种语言转换到另一种语言，而是在不同的语言和文化语境中重新创造声音、节奏、歧义、幽默、讽刺和审美效果。机器可以翻译诗歌的文字，但它无法翻译诗歌本身。
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.	这不仅仅是以人文主义信念对抗技术进步的宣言。它反映了模式匹配（人工智能所擅长的）和意义创造（需要具身文化体验、情感理解和审美判断）之间的根本区别。当Emily Wilson翻译荷马的《奥德赛》或Pevear和Volokhonsky重新翻译陀思妥耶夫斯基时，他们带来的不仅是语言专长，还有个人解释视野——这是区分伟大翻译和称职译文的品质。
8.2 Ethical Judgement in Translation	8.2 翻译中的伦理判断
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.	关于适切性理论的配套章节（Woesler，本卷）认为，翻译不能纯粹基于语言标准来评估，而必须在一个考虑生产条件、历史语境和翻译选择潜在后果的伦理框架内进行评估。这一伦理维度——识别翻译选择何时可能造成伤害、文化敏感性何时要求偏离字面准确性、政治语境何时需要审慎措辞的能力——仍然超出了当前人工智能系统的能力范围。
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.	人工智能翻译系统基于训练数据中的统计模式做出选择。它们无法在权衡竞争价值观和为选择后果承担责任的意义上行使伦理判断。审查机器输出并决定某一特定译法虽然在语言上准确但在文化上不恰当或政治上敏感的译后编辑者，行使的是当前任何人工智能系统都无法复制的一种人类能动性。
8.3 The „Complementarity Thesis“ Extended	8.3 "互补性论题"的延伸
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.	本卷的实证研究（Woesler，本卷）发现，人工智能和人类教学在语言学习中服务于"不同的、互补的功能"——我们称之为互补性论题。这一论题自然延伸到翻译领域：人工智能和人类译者服务于不同的、互补的功能。人工智能在速度、一致性、覆盖面和处理常规信息性文本方面表现出色。人类译者在文化解读、文学创造力、伦理判断和处理精确性与细微差别攸关的高风险文本方面表现出色。
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.	翻译的未来既不是用机器取代人类，也不是维持传统的纯人工翻译实践。而是一种协作生态，在这种生态中，人类和机器之间的劳动分工随着人工智能能力的演进以及我们对何为"好翻译"的理解的深化而不断重新协商。
8.4 The Case of Chinese Literature in Translation	8.4 中国文学翻译的案例
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.	这些例子不仅仅是有趣的个案，而是一个普遍原则的例证：文本对文化嵌入性、审美效果和多层次意义的依赖程度越高，人工智能翻译就越显不足。这一原则对教育有直接启示，表明文学翻译的研习——远非过时的专业化——可能成为翻译教育中最具独特人类性因而最有价值的组成部分。
9. Conclusion	9. 结论
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.	"翻译的终结"如果从字面上理解是一个误导性的说法。正在终结的是翻译作为一种主要由人类执行的常规专业服务。正在出现的是一种新的人机协作生态，它需要新的能力、新的专业角色、新的课程和新形式的伦理反思。
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.	对于教育而言，这一转型所要求的不亚于对翻译教与学之含义的重新概念化。机器翻译素养——涵盖技术理解、批判性评估、有效的译后编辑和伦理判断——必须成为语言和翻译课程的核心。同时，机器无法复制的人文能力——文化敏感性、文学感受力、伦理推理、创造性解读——必须得到保存和加强，而不是作为过时之物被抛弃。
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.	欧洲和中国应对策略的比较揭示了欧盟谨慎的、以监管为导向的方法与中国快速的、国家支持的人工智能工具采用之间的生产性张力。两种方法单独都不够充分。欧盟对伦理框架和批判性评估的强调为负责任的人工智能整合提供了必要的护栏。中国在采用的速度和规模方面产生了欧盟可以借鉴的实践经验和制度适应。综合两种方法——将伦理严谨性与实践敏捷性相结合——提供了最有前景的前进道路。
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.	本分析得出几项实用建议。第一，翻译和语言项目应从第一学期起将机器翻译素养作为核心能力引入，确保学生在发展传统语言能力的同时培养批判性评估技能。第二，MTPE的教授不应仅仅作为一种技术技能，而应在包含错误分类法、质量评估方法论和对翻译选择后果之伦理反思的框架中进行。第三，文学和文化翻译应得到保存和加强，作为人类专长最明显不可替代——也在人工智能饱和的格局中最明显有价值——的领域。第四，应加强翻译教育领域的欧中学术合作，利用欧洲在监管和伦理框架方面的优势与中国在实际人工智能整合和规模方面的优势之互补性。
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.	对于更广泛的教育数字化领域，"翻译的终结"案例研究既提供了一个警示，也提供了一个令人鼓舞的先例。警示的是，人工智能颠覆的速度和规模可能超过机构适应的步伐，使学生对转型后的职业格局准备不足。令人鼓舞的是，这一转型并没有消除对人类专长的需要，而是将其重新定向——从常规执行到批判性评估、创造性解读和伦理判断。这些恰恰是高等教育最有能力培养的能力。
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.	正如本论文集通过关于人工智能辅助语言学习、替代学习形式、人工智能伦理和未来大学的配套章节所展示的，人工智能在教育中引发的变革不是单一现象，而是必须整体应对的一系列相互关联的变化。我们所熟知的翻译的终结，同时也是某种新事物的开始——一种人类智能和人工智能协作以跨越语言、文化和世界观之间鸿沟的跨文化交流形式。
Acknowledgments	致谢
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.	本研究得到了让·莫内卓越中心"欧盟研究中心：中欧数字化"（EUSC-DEC）的支持，由欧盟资助协议编号101126782提供资金。所表达的观点和意见仅代表作者本人，不一定反映欧盟的立场。
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