如果人工智能可以即时翻译，为什么要学习一门外语？
If AI can translate instantly, why learn another langua…
There’s a difference between using a tool to assist you, and using one to replace cognitive effort.
使用工具来辅助你，和使用工具来取代认知努力，两者之间存在区别。
From live speech translation in video calls to auto-dubbing on TikTok, the technology to dissolve language barriers has arrived. Real-time translation powered by artificial intelligence (AI) is now embedded in everyday life.
从视频通话中的实时语音翻译到TikTok上的自动配音，消除语言障碍的技术已经到来。由人工智能（AI）驱动的实时翻译现在已经融入了日常生活。
Tools from OpenAI, Meta, Google and many others now offer near-instant translation across dozens of languages, and they keep improving.
OpenAI、Meta、Google以及许多其他公司的工具现在提供数十种语言的近乎即时翻译，并且它们持续改进。
All this raises a vital question. If machines can do this faster and more accurately than humans, is investing years in learning another language still worth it?
所有这些引发了一个关键问题。如果机器能比人类更快、更准确地完成这些任务，那么投入数年时间学习一门新语言是否还值得？
The logic is appealing. Humans have always offloaded cognitive work onto tools. Writing reduced demands on our memory. Calculators removed the burden of mental arithmetic. AI sits within this long tradition. Used well, it can support learning and expand access in ways that matter enormously.
这个逻辑很有吸引力。人类一直习惯于将认知工作外包给工具。书写减轻了我们对记忆的依赖。计算器消除了心算负担。人工智能就处于这一悠久传统之中。如果使用得当，它可以以极其重要的方式支持学习和扩大获取知识的途径。
But there’s a difference between using a tool to extend your capabilities and using it to avoid doing something altogether. That distinction becomes important when you are not just replacing a skill, but a form of cognitive and cultural engagement.
但使用工具来扩展自身能力，和使用工具来完全避免做某事，两者之间存在区别。当您不仅仅是在取代一项技能，而是在取代一种认知和文化参与形式时，这种区别就变得至关重要了。
The effort is the point
努力本身就是关键
Effort plays a central role in how we acquire knowledge.
努力在获取知识的过程中起着核心作用。
Psychologists use the phrase “desirable difficulties” to describe challenges that may feel inefficient, but produce stronger long-term retention and understanding.
心理学家使用“可接受的困难”（desirable difficulties）这个短语来描述那些可能感觉效率低下，但却能产生更强长期记忆和理解的挑战。
Struggling with grammar, searching for the right word, or constructing meaning across multiple languages engages brain networks that support memory, attention and cognitive flexibility. Over time, they consolidate knowledge far more deeply than passive exposure.
在语法上挣扎、寻找合适的词汇，或跨多种语言构建意义，这些活动会激活支持记忆、注意力和认知灵活性的脑网络。随着时间的推移，这些活动巩固知识的深度，远超被动接触。
Sustained mental engagement contributes to what researchers call cognitive resilience – the brain’s capacity to maintain function as we age. Managing multiple languages is one form of this engagement. It requires the brain to resolve competition, monitor context and adapt dynamically.
持续的思维投入有助于提高研究人员所称的认知弹性——即我们随着年龄增长时，大脑维持功能的能力。管理多种语言就是其中一种投入形式。它要求大脑解决竞争、监控语境并进行动态调整。
These are not trivial demands. And they’re difficult to achieve if you just use translation tools passively, such as resolving the meaning of a foreign phrase with the click of a button.
这些都不是微不足道的要求。如果你只是被动地使用翻译工具，例如只需点击一下按钮就能解决一个外语短语的含义，那么这些要求就很难达到。
What multilingualism research actually shows
多语能力研究实际揭示了什么
The evidence on multilingualism is often presented as a simple “bilingual advantage”, a shorthand that obscures a more complicated picture. Some studies report benefits for attention or working memory, while others find no differences. The truth appears to be more selective.
关于多语能力的证据经常被呈现为简单的“双语优势”，这是一个掩盖了更复杂图景的简写词。一些研究报告了对注意力或工作记忆的益处，而另一些研究则未发现差异。事实似乎更加具有选择性。
Our recent study examined cognitive performance in 94 adults aged 18 to 83, using both visuospatial and auditory tasks across working memory, attention and inhibition. Put simply, we looked at how people process and respond to information they see or mentally map out in space (visuospatial) and information they hear (auditory) . Examples include remembering sounds, focusing on visual patterns, or ignoring distractions.
我们最近的一项研究考察了94名年龄介于18至83岁的成年人的认知表现，使用了涉及工作记忆、注意力和抑制的视觉空间和听觉任务。简单来说，我们研究了人们如何处理和回应他们所看到的或在空间中心智构建的信息（视觉空间），以及他们听到的信息（听觉）。例子包括记住声音、集中于视觉模式或忽略干扰。
Our study measured multilingualism as a spectrum, not a category. This allowed us to capture diverse language backgrounds and experiences. Multilingual participants spoke a range of languages with varying levels of proficiency and daily use, reflecting the linguistic diversity common within multicultural communities.
我们的研究将多语能力测量为一个谱系，而非一个类别。这使我们能够捕捉到多样化的语言背景和经验。多语参与者说的是一系列具有不同熟练度和日常使用水平的语言，反映了多文化社区中常见的语言多样性。
Across most tasks, multilinguals and monolinguals performed similarly. However, one pattern was striking. Individuals with richer, more diverse multilingual experience showed markedly better performance in visuospatial working memory. These effects were most pronounced in older people.
在大多数任务中，多语者和单语者的表现相似。然而，有一个模式非常引人注目。拥有更丰富、更多样化多语经验的个体在视觉空间工作记忆方面表现出明显更好的表现。这些效应在老年人中最为突出。
This suggests that multilingual experience doesn’t broadly enhance cognition, like some headlines claim. Instead, it may help preserve specific functions over time.
这表明，多语经验并不能像一些头条新闻声称的那样广泛增强认知能力。相反，它可能有助于随着时间的推移维持特定的功能。
Separate population-level research has also linked multilingualism to later onset of Alzheimer’s disease and better overall ageing outcomes, though the mechanisms continue to be debated.
其他人群层面的研究也已将多语能力与阿尔茨海默病发病晚期和更好的整体衰老结果联系起来，尽管其机制仍在争论中。
Overall, however, it appears that sustained use of multiple languages represents a form of mental activity with effects that accumulate across a lifetime.
总的来说，然而，持续使用多种语言似乎代表了一种精神活动形式，其影响是积累一生的。
What AI translation can’t replicate
AI 翻译无法复制的
AI translation excels at speed and accessibility. For many practical purposes, it works remarkably well. But it operates through pattern recognition, not lived understanding. It can struggle with cultural context, humour, register and emotionally embedded meaning, especially for languages with less representation in training data.
AI 翻译擅长速度和可及性。对于许多实际用途来说，它表现得非常出色。但它的运作是通过模式识别，而非生活理解。它在处理文化背景、幽默感、语域和情感内涵的意义时可能会遇到困难，特别是对于训练数据中代表性较少的语言。
At best, AI captures literal dimensions of language while missing social ones. Consider the scene in the 2003 film Love Actually where Jamie, played by Colin Firth, delivers an awkward but sincere proposal to Aurelia in broken Portuguese.
充其量，AI 只能捕捉语言的字面维度，而忽略了社会维度。想想2003年的电影《爱的二黄子月》，其中科林·菲斯饰演的杰米，用蹩脚的葡萄牙语向奥蕾莉亚求婚的场景。
It is moving because of the effort, vulnerability and intent his imperfect words carry. Resort to real-time translation software and what remains is information, not expression.
它之所以感人，是因为他不完美的话语所承载的努力、脆弱和意图。如果依赖实时翻译软件，剩下的只是信息，而非表达。
This is the deeper distinction: translation is not the same as participation. Learning a language involves understanding how people think, their values, and how meaning is shaped by context and history. This cultural literacy develops through interaction and experience. We can’t fully outsource that to systems that translate on demand.
这才是更深层次的区别：翻译不等于参与。学习一门语言，涉及到理解人们如何思考、他们的价值观，以及意义如何受到语境和历史的影响。这种文化素养是通过互动和经验培养的。我们不能完全将这种能力外包给按需翻译的系统。
The multilingual participants in our research spoke to this directly:
我们研究中的多语参与者直接谈到了这一点：
I definitely think in Telugu, but I remember numbers and count using English. Afrikaans is the language of my heart and best used to express intense emotion. English is the language of business and used mostly in everyday life.
我肯定是用泰卢固语思考的，但我记得数数和计数时用的是英语。阿非利汗语是我内心的语言，最适合表达强烈的情感。英语是商业的语言，主要用于日常生活。
These are not descriptions of switching between translation modes. They are descriptions of inhabiting different selves.
这些不是描述在不同翻译模式之间切换。它们是描述着栖居于不同自我状态。
AI will continue to change how we engage with language learning. It can personalise instruction, minimise barriers and provide feedback at scale. What it can’t do is replace the cognitive and cultural work that comes from learning a language. This work leads to a deeper relationship with how other people see the world, and with how you express yourself. And that difference still matters.
AI 将继续改变我们参与语言学习的方式。它可以个性化教学，最大限度地减少障碍，并大规模提供反馈。但它无法取代学习一门语言所带来的认知和文化劳动。这种劳动让人与他人看待世界的方式，以及你表达自己的方式，建立起更深层次的联系。而这种差异仍然至关重要。
Olivia Maurice completed her PhD at the MARCS Institute, Western Sydney University.
Olivia Maurice 在西悉尼大学 MARCS 学院完成了博士学位。
Mark Antoniou receives funding from the Australian Research Council.
Mark Antoniou 获得澳大利亚研究理事会的资助。
