文章总结： 本文探讨AI检测工具对高校写作教学的负面影响，指出其迫使学生追求平庸文风甚至使用AI自保。文章呼吁教育者放弃检测工具，将AI视为教育问题，并提供了避免AI写作套路的提示词以绕过检测。 综合评分： 85 文章分类： AI安全,安全意识,解决方案

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避开英文AI写作套路：别让AI检测工具成了写作的枷锁

原创

黑鸟 黑鸟

黑鸟

2026年3月8日 23:10 广东

在当下的高校英文写作（论文）教学场景中，AI 代写是师生共同面对的核心焦虑。

为了甄别 AI 生成的文本，各类 AI 检测工具被大规模引入课堂。

无数学生被反复传授规避 AI 写作套路的技巧。

这些技巧包括不能使用过于复杂的词汇，不能采用过于规整的句式，甚至要刻意制造轻微的表达瑕疵，才能证明文本出自真人之手。

但很少有人意识到，这套看似对抗 AI 套路的操作，正在催生出一个更加荒诞的恶性循环。

早在一年半前，科技作者迈克・马斯尼克就记录下了极具讽刺性的一幕。

他的孩子在完成一篇关于库尔特・冯内古特《哈里森・伯杰隆》的英文读后感作业时，只因使用了 devoid 这个稍显书面的词汇，就被学校预装的 AI 检测工具标记为 18% 由 AI 撰写。

而当孩子把这个词替换成最基础的 without 之后，AI 检测占比瞬间降到了 0%。

这篇小说本就讲述了反乌托邦社会为了强行推行平等，给所有出类拔萃的人套上枷锁的故事。而现实中的这一幕，恰好完成了对小说内核最精准的复刻。

人们为了规避 AI 写作的套路，正在亲手把学生的英文写作，逼向平庸刻板、毫无灵气的固定范式里。甚至原本完全不使用 AI 的学生，也被这套规则推向了 AI 工具的怀抱。

这篇小说本就是在警示世人，强行压制卓越会带来怎样的恶果。

而现实中，人们却逼着孩子为了通过检测，把这篇读后感写得越来越平庸。其中的讽刺意味，迈克・马斯尼克明白，他的孩子也明白。

那个下午，孩子满心挫败，一个词一个词地删改，一句话一句话地测试，只想搞清楚算法到底设下了怎样的隐形红线。而孩子从这件事里学到的道理无比清晰。

写作不能有创意，要用最简单的词汇，绝对不能写得太好。因为写得好，在当下就等于有嫌疑。

当时迈克・马斯尼克就担心，这件事会演变成一个更严重的问题。对 AI 作弊的恐慌，会催生出一种主动惩罚优秀写作、把学生推向平庸的风气。他多希望自己的预判是错的。

结果证明，他的预判完全准确。

曾在多所高校任教的写作讲师戴德兰・梅耶，在《高等教育纪事报》上发表了一篇文章，完整记录了这一现象在他的课堂上一步步失控的全过程。

现实情况甚至比迈克・马斯尼克之前描述的还要糟糕。

这套 AI 检测体系，不仅逼着学生把文章写得更差，还让那些原本从来不用 AI 的学生，也开始主动使用 AI。

今年秋季学期，有一名学生向戴德兰・梅耶坦言，她之所以开始使用生成式 AI，完全是因为听说，特殊的文体格式都有可能触发 AI 检测器的警报。为了避免被标记，她开始把自己写的内容放进 AI 工具里，检测内容会不会被判定为 AI 生成。

一名原本全程自主创作、用自己的文字完成写作的学生，开始抱着防御的心态使用 AI 工具。她使用工具的目的不是作弊，只是为了确保自己的原创内容，不会被冤枉成作弊。这款本用来杜绝 AI 使用的工具，反倒成了她开始使用 AI 的直接原因。

这就是最纯粹的眼镜蛇效应。英国殖民印度时期，政府为了减少眼镜蛇数量，推出了悬赏死眼镜蛇的政策。结果民众反而开始人工养殖眼镜蛇，来赚取赏金。当政府叫停这个项目后，养殖户把手里毫无价值的眼镜蛇全部放生，最终蛇患比政策实施前更加严重。而 AI 检测工具，就是当下的眼镜蛇赏金。它本应减少 AI 的使用，结果反倒成了人们使用 AI 的核心诱因。

这种现象绝非个例。戴德兰・梅耶描述了一种正在他的课堂上蔓延的普遍情况。

有一名母语为英语的学生，长久以来都因为写作水平远超同年级而备受称赞。这个学期，她转学到了一所新大学，新的烦恼也随之而来。不熟悉她的教授们，根本无从得知她这种笃定自信的文风，是长年累月练习积累的成果。

于是她向谷歌 Gemini 发起了一个针对性的提问，询问大学老师眼里，哪些内容会触发 AI 作弊的警报。而这个提问，打开了潘多拉魔盒。

她学会了提示词对生成内容的影响，哪些句式会引来审查，笃定的文风又会如何引发质疑。

这款工具，最终成了她补充课程内容、厘清难点知识的辅助工具。但即便如此，她还是觉得这件事存在问题。

她向戴德兰・梅耶坦言，自己总觉得在作弊，尽管让她走到这一步的，只是自我保护的念头。

一名多年来因写作出众而备受赞誉的学生，如今却觉得自己像个作弊者。她产生这种想法，只因为不得不去搞懂 AI 检测的规则，才能避免被冤枉。这套监控体系，把写作天赋变成了一种原罪。

还有更令人错愕的情况。

另一名学生，在另一门课程中被指控使用 AI 写作后，找到了戴德兰・梅耶。

这项指控毫无根据，可他的论文还是被搁置，不予评分。而接下来发生的事，让戴德兰・梅耶深感不安。这名学生表示，他觉得必须摸透这套把自己拖入困境的技术，这样才能保护自己。

而他所谓的保护，就是彻底沉浸在这项技术中。他订阅了多个 AI 工具的会员，认真研究检测系统的运作原理，熟练掌握了那些原本从未打算使用的工具。这段经历的最终结果，是他做出了一个决定。

他绝不会把这件事告诉其他教授，因为他不觉得教授们会对自己有好印象。

一次莫须有的指控，让这名学生订阅了多款 AI 服务，深入研究检测系统的运作逻辑。他这么做不是因为想作弊，而是因为觉得，这是自我保护的唯一办法。

而最后，他还选择对此守口如瓶。因为让教授知道他了解 AI 相关技术，只会招来更多的怀疑。

迈克・马斯尼克坦言，他很清楚确实有学生在用 AI 作弊，这也是教育工作者必须面对的现实问题。

但这种检测至上的做法，催生出了一套完全本末倒置的激励机制。不用 AI 的学生，会因为写得太好而受到惩罚。

被冤枉的学生，会明白唯一的自保方式，就是熟练掌握那套他们被指控使用的工具。而那些真正会作弊、懂门道的学生，恰恰是最有能力骗过检测工具的人。这些工具根本抓不到作弊者，反倒把诚实的孩子逼得改变了立场。

正如戴德兰・梅耶所说，这种局面，在纽约市立大学这类开放式入学的院校里，显得格外残酷。这里的学生，本就背负着巨大的压力。

在纽约市立大学，很多学生每周要工作 20 到 40 个小时，很多人掌握多种语言。几乎每一门课程，对 AI 的使用规定都不相同。

一名教授完全禁用 AI，另一名教授却鼓励使用，学生们只能学会闭口不谈，生怕一步踏错。

这种规则不统一的代价，最终全落在了学生身上，而且是实实在在的代价。这些代价包括耗费的时间、反复的修改，还有持续的自我审查。

有一名学生向戴德兰・梅耶坦言，哪怕文章里每一个词都是自己原创的，只要被检测器标记为 AI 生成，他就要花好几个小时改写句子。他说自己改了一遍又一遍，整个过程实在太耗时间了。

就像迈克・马斯尼克的孩子当年遇到的那款学校预装的 AI 检测器一样，戴德兰・梅耶的学生，把大量的时间浪费在了为规避标记而修改文章上。

学生们要花好几个小时，重写自己完完全全原创的作品。他们这么做，只因为一个算法判定文章读起来太像机器写的了。而这些时间，本可以用来学习、工作、照顾家人，或是用来真正提升自己的写作能力。

学习修改，本是学习写作的核心环节。但修改的目的，应该是为了更好地表达写作的初衷，而不是为了讨好一款不靠谱的机器检测工具。

戴德兰・梅耶把这件事的本质说得无比透彻。这件事的危害，远不止误判和浪费时间。更深层的问题，是这些工具给学生灌输了完全错误的写作理念。

即便老师没有明说，检测工具也在向学生传递一个信息。写作是一场需要小心翼翼应付的表演，而不是一项需要打磨精进的能力。学生们学会了，文风会给自己招来麻烦，文笔流畅反而会引来怀疑。

这套体系正在教整整一代学生，写作的终极目标，就是写得足够平庸、足够不起眼。写作的意义不再是表达原创的想法，不再是构建完整的论证，不再是找到自己的文风，更不是清晰有力地传递信息。而是写出足够平淡乏味的文本，让一个统计模型不会给它打上 AI 生成的标签。

使用书面化的高阶词汇，风险太高。使用特殊的标点格式，会引来怀疑。笃定流畅的散文，直接就是高危警报。

现在回头看，迈克・马斯尼克的孩子当年写《哈里森・伯杰隆》的那段经历，完美预示了如今的一切。

冯内古特早已警示过，一个社会，会通过给有能力的人套上枷锁，把所有人都拉到最低的平庸水平。而如今，人们正身处这样的现实。AI 检测工具，成了学生写作界的平等总管，惩罚流畅的文笔，苛责丰富的词汇，训练学生们要写得尽可能平庸，只为了不触发那个连走心的原创文章和 ChatGPT 生成的内容都分不清的算法。

戴德兰・梅耶最终做了一件唯一清醒的事。他不再陪这场闹剧继续下去。

学期过半，戴德兰・梅耶不再要求学生披露自己的 AI 使用情况。

他的教学大纲里，原本要求学生对此保持透明，可如今这个要求已经变得逻辑不通。

使用 AI 和上网查资料的边界，已经模糊到无法分辨。要求学生记录下每一次和 AI 的接触，只会把写作变成一场记账式的任务。他调整了自己的教学方式。他告诉学生，他们可以用 AI 做调研和列大纲，但初稿必须由自己完成。他教学生如何负责任地撰写提示词，如何分辨工具什么时候开始取代了自己的思考。

他没有采用那种有罪推定的管理方式，而是直面现实，把重心放在了真正对学习环境有益的事上。教学生合理地使用工具，而不是把工具当成作弊的捷径，更不是从一开始就抱着怀疑的态度对待学生。

戴德兰・梅耶的课堂氛围彻底发生了改变。课后会有学生主动来找他，询问怎么才能用好这些工具。有学生想知道，怎么用 AI 辅助调研，又不会直接照搬生成的内容。还有学生问，怎么判断 AI 撰写的摘要偏离了原文的本意。这些对话，完完全全回归了教学的本质。而只有当 AI 使用不再是一个披露问题，而是变成一个教学主题的时候，这些对话才有可能发生。

当这套监控体系被撤下，学生们才真正开始学习。他们会发自内心地提问，询问如何高效、合规地使用这些工具。他们把这项技术当成一个值得去了解的对象，而不是一片需要小心翼翼趟过的雷区。师生之间的关系，从对立的警匪关系，回归到了原本的教学关系。这才是学校存在的根本意义。

戴德兰・梅耶说的那句，这些对话完完全全回归了教学的本质，一直在迈克・马斯尼克的脑海里挥之不去。对 AI 会颠覆教学的恐惧，让教育者连正常的教学都做不到了。而放下这份恐惧，教育者才重新找回了教学的本质。其中的讽刺意味不言而喻。

每一个觉得抓住使用 AI 的学生是头等大事的教育工作者，都该好好读读这篇文章。

正如戴德兰・梅耶在一次次惨痛的经历中明白的那样，解决问题的答案，是不要再把 AI 当成一个治安问题，而是要把它当成一个教育问题。教学生怎么写作，教学生怎么批判性地看待 AI 工具，教他们分辨这些工具什么时候有用、什么时候有害、什么时候会变成让人丧失独立思考能力的拐杖。迈克・马斯尼克在文末呼吁，教育者别再使用这些检测工具。这些工具只会惩罚优秀的写作者，把所有人都推向算法定义的、平淡乏味的平庸标准。

毫不夸张地说，当下的教育体系正在限制学生的写作能力，只为了讨好一台连真人写作和 AI 生成分不清的机器。

然而，黑鸟要给大家提供一个包含AI写作套路的提示词，将其添加到你的AI系统中，以帮助它避免这些模式。

可以有效的绕过一些AI检测是否AI生成的工具。

AI Writing Tropes to Avoid

Add this file to your AI assistant’s system prompt or context to help it avoid

common AI writing patterns.

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Word Choice

“Quietly” and Other Magic Adverbs

Overuse of “quietly” and similar adverbs to convey subtle importance or understated power. AI reaches for these adverbs to make mundane descriptions feel significant. Also includes: “deeply”, “fundamentally”, “remarkably”, “arguably”.

**Avoid patterns like:**

• “quietly orchestrating workflows, decisions, and interactions”

• “the one that quietly suffocates everything else”

• “a quiet intelligence behind it”

“Delve” and Friends

Used to be the most infamous AI tell. “Delve” went from an uncommon English word to appearing in a staggering percentage of AI-generated text. Part of a family of overused AI vocabulary including “certainly”, “utilize”, “leverage” (as a verb), “robust”, “streamline”, and “harness”.

**Avoid patterns like:**

• “Let’s delve into the details…”

• “Delving deeper into this topic…”

• “We certainly need to leverage these robust frameworks…”

“Tapestry” and “Landscape”

Overuse of ornate or grandiose nouns where simpler words would do. “Tapestry” is used to describe anything interconnected. “Landscape” is used to describe any field or domain. Other offenders: “paradigm”, “synergy”, “ecosystem”, “framework”.

**Avoid patterns like:**

• “The rich tapestry of human experience…”

• “Navigating the complex landscape of modern AI…”

• “The ever-evolving landscape of technology…”

The “Serves As” Dodge

Replacing simple “is” or “are” with pompous alternatives like “serves as”, “stands as”, “marks”, or “represents”. AI avoids basic copulas because its repetition penalty pushes it toward fancier constructions (I’ve studied this!).

**Avoid patterns like:**

• “The building serves as a reminder of the city’s heritage.”

• “Gallery 825 serves as LAAA’s exhibition space for contemporary art.”

• “The station marks a pivotal moment in the evolution of regional transit.”

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Sentence Structure

Negative Parallelism

The “It’s not X — it’s Y” pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale. Includes the causal variant “not because X, but because Y” where every explanation is framed as a surprise reveal.

**Avoid patterns like:**

• “It’s not bold. It’s backwards.”

• “Feeding isn’t nutrition. It’s dialysis.”

• “Half the bugs you chase aren’t in your code. They’re in your head.”

“Not X. Not Y. Just Z.”

The dramatic countdown pattern. AI builds tension by negating two or more things before revealing the actual point. Creates a false sense of narrowing down to the truth.

**Avoid patterns like:**

• “Not a bug. Not a feature. A fundamental design flaw.”

• “Not ten. Not fifty. Five hundred and twenty-three lint violations across 67 files.”

• “not recklessly, not completely, but enough”

“The X? A Y.”

Self-posed rhetorical questions answered immediately in the next sentence or clause. The model asks a question nobody was asking, then answers it for dramatic effect. Thinks this is the epitome of great writing.

**Avoid patterns like:**

• “The result? Devastating.”

• “The worst part? Nobody saw it coming.”

• “The scary part? This attack vector is perfect for developers.”

Anaphora Abuse

Repeating the same sentence opening multiple times in quick succession.

**Avoid patterns like:**

• “They assume that users will pay… They assume that developers will build… They assume that ecosystems will emerge… They assume that…”

• “They could expose… They could offer… They could provide… They could create… They could let… They could unlock…”

• “They have built engines, but not vehicles. They have built power, but not leverage. They have built walls, but not doors.”

Tricolon Abuse

Overuse of the rule-of-three pattern, often extended to four or five. A single tricolon is elegant; three back-to-back tricolons are a pattern recognition failure.

**Avoid patterns like:**

• “Products impress people; platforms empower them. Products solve problems; platforms create worlds. Products scale linearly; platforms scale exponentially.”

• “identity, payments, compute, distribution”

• “workflows, decisions, and interactions”

“It’s Worth Noting”

Filler transitions that signal nothing. AI uses these phrases to introduce new points without actually connecting them to the previous argument. Also includes: “It bears mentioning”, “Importantly”, “Interestingly”, “Notably”.

**Avoid patterns like:**

• “It’s worth noting that this approach has limitations.”

• “Importantly, we must consider the broader implications.”

• “Interestingly, this pattern repeats across industries.”

Superficial Analyses

Tacking a present participle (“-ing”) phrase onto the end of a sentence to inject shallow analysis that says nothing. The model attaches significance, legacy, or broader meaning to mundane facts using phrases like “highlighting its importance”, “reflecting broader trends”, or “contributing to the development of…”.

**Avoid patterns like:**

• “contributing to the region’s rich cultural heritage”

• “This etymology highlights the enduring legacy of the community’s resistance and the transformative power of unity in shaping its identity.”

• “underscoring its role as a dynamic hub of activity and culture”

False Ranges

Using “from X to Y” constructions where X and Y aren’t on any real scale. In legitimate use, “from X to Y” implies a spectrum with a meaningful middle. AI uses it as a fancy way to list two loosely related things. “From innovation to cultural transformation” — what’s in between???? Nothing!

**Avoid patterns like:**

• “From innovation to implementation to cultural transformation.”

• “From the singularity of the Big Bang to the grand cosmic web.”

• “From problem-solving and tool-making to scientific discovery, artistic expression, and technological innovation.”

Gerund Fragment Litany

After making a claim, AI illustrates it with a stream of verbless gerund fragments — standalone sentences with no grammatical subject. “Fixing small bugs. Writing straightforward features. Implementing well-defined tickets.” The first sentence already said everything. The fragments add nothing except word count and that familiar AI cadence. Humans don’t write first drafts this way. It’s a pure structural tic.

**Avoid patterns like:**

• “Fixing small bugs. Writing straightforward features. Implementing well-defined tickets.”

• “Reviewing pull requests. Debugging edge cases. Attending architecture meetings.”

• “Shipping faster. Moving quicker. Delivering more.”

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Paragraph Structure

Short Punchy Fragments

Excessive use of very short sentences or sentence fragments as standalone paragraphs for manufactured emphasis. RLHF training has pushed models toward “writing for readability” aimed at the lowest common denominator: one thought per sentence, no mental state-keeping required. It’s an inhuman style. No real person writes first drafts this way because it doesn’t match how humans think or speak.

**Avoid patterns like:**

• “He published this. Openly. In a book. As a priest.”

• “These weren’t just products. And the software side matched. Then it professionalised. But I adapted.”

• “Platforms do.”

Listicle in a Trench Coat

Numbered or labeled points dressed up as continuous prose. The model writes what is essentially a listicle but wraps each point in a paragraph that starts with “The first… The second… The third…” to disguise the format. Perhaps you told it to stop generating lists and it decided to do this instead… still very common.

**Avoid patterns like:**

• “The first wall is the absence of a free, scoped API… The second wall is the lack of delegated access… The third wall is the absence of scoped permissions…”

• “The second takeaway is that… The third takeaway is that… The fourth takeaway is that…”

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Tone

“Here’s the Kicker”

False suspense transitions that promise a revelation but deliver a point that did NOT need the buildup. The model uses these phrases to manufacture drama before an otherwise unremarkable observation LOL. Also includes: “Here’s the thing”, “Here’s where it gets interesting”, “Here’s what most people miss”.

**Avoid patterns like:**

• “Here’s the kicker.”

• “Here’s the thing about AI adoption.”

• “Here’s where it gets interesting.”

“Think of It As…”

The patronizing analogy. AI constantly reaches for “Think of it as…” or “It’s like a…” to simplify concepts. The model defaults to teacher mode and assumes the reader needs a metaphor to understand anything. Often produces analogies that are less clear than the original concept.

**Avoid patterns like:**

• “Think of it like a highway system for data.”

• “Think of it as a Swiss Army knife for your workflow.”

• “It’s like asking someone to buy a car they’re only allowed to sit in while it’s parked.”

“Imagine a World Where…”

The classic AI invitation to futurism. To sell the argument usually begins with “Imagine” followed by a list of wonderful things that will happen if the reader agrees with the premise.

**Avoid patterns like:**

• “Imagine a world where every tool you use — your calendar, your inbox, your documents, your CRM, your code editor — has a quiet intelligence behind it…”

• “In that world, workflows stop being collections of manual steps and start becoming orchestrations.”

False Vulnerability

Simulated self-awareness or honesty that reads as performative. The model pretends to break the fourth wall or admit a bias, creating a false sense of authenticity. Real vulnerability is specific and uncomfortable; AI vulnerability is polished and risk-free!!!!

**Avoid patterns like:**

• “And yes, I’m openly in love with the platform model”

• “And yes, since we’re being honest: I’m looking at you, OpenAI, Google, Anthropic, Meta”

• “This is not a rant; it’s a diagnosis”

“The Truth Is Simple”

Asserting that something is obvious, clear or simple instead of actually proving it. If you have to tell the reader your point is clear, it very likely isn’t.

**Avoid patterns like:**

• “The reality is simpler and less flattering”

• “History is unambiguous on this point”

• “History is clear, the metrics are clear, the examples are clear”

Grandiose Stakes Inflation

Everything is the most important thing ever. AI inflates the stakes of every argument to world-historical significance. A blog post about API pricing becomes a meditation on the fate of civilization.

**Avoid patterns like:**

• “This will fundamentally reshape how we think about everything.”

• “will define the next era of computing”

• “something entirely new”

“Let’s Break This Down”

The pedagogical voice that assumes the reader needs hand-holding. AI defaults to a teacher-student dynamic even when writing for expert audiences. Also includes: “Let’s unpack this”, “Let’s explore”, “Let’s dive in”.

**Avoid patterns like:**

• “Let’s break this down step by step.”

• “Let’s unpack what this really means.”

• “Let’s explore this idea further.”

Vague Attributions

Attributing claims to unnamed authorities instead of being specific. AI loves to invoke “experts”, “observers”, “industry reports”, and “several publications” without naming anyone. It also inflates the quantity of sources — presenting what one person said as a widely held view, or writing “several publications have cited” when it means two. If you can’t name the expert, you don’t have a source.

**Avoid patterns like:**

• “Experts argue that this approach has significant drawbacks.”

• “Industry reports suggest that adoption is accelerating.”

• “Observers have cited the initiative as a turning point.”

Invented Concept Labels

AI clusters invented compound labels that sound analytical without being grounded. It appends abstract problem-nouns (paradox, trap, creep, divide, vacuum, inversion) to domain words — “supervision paradox”, “acceleration trap”, “workload creep” — and uses them as if they’re established, rigorously defined terms. They function as rhetorical shorthand: name a thing, skip the argument. Multiple such labels in the same piece is a strong signal of AI slop.

**Avoid patterns like:**

• “the supervision paradox”

• “the acceleration trap”

• “workload creep”

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Formatting

Em-Dash Addiction

Compulsive overuse of em dashes for dramatic pauses, parenthetical asides and pivot points. A human writer might use 2-3 per piece (and naturally); AI will use 20+.

**Avoid patterns like:**

• “The problem — and this is the part nobody talks about — is systemic.”

• “The tinkerer spirit didn’t die of natural causes — it was bought out.”

• “Not recklessly, not completely — but enough — enough to matter.”

Bold-First Bullets

Every bullet point or list item starts with a bolded phrase or sentence. Extremely common in Claude and ChatGPT markdown output. Almost nobody formats lists this way when writing by hand. It’s a telltale sign of AI-generated documentation and blog posts AND README files (especially with emojis).

**Avoid patterns like:**

• “Every single bullet point begins with a bold keyword.”

• “**Security**: Environment-based configuration with…”

• “**Performance**: Lazy loading of expensive resources…”

Unicode Decoration

Use of unicode arrows (->), smart/curly quotes, and other special characters that can’t be easily typed on a standard keyboard. Real writers typing in a text editor produce straight quotes and -> or =>. Claude in particular loves the -> arrow.

**Avoid patterns like:**

• “Input → Processing → Output”

• “This leads to better outcomes → which means higher engagement”

• ““Smart quotes” instead of straight “quotes” that you’d actually type”

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Composition

Fractal Summaries

“What I’m going to tell you; what I’m telling you; what I just told you” — applied at every level of the document. Every subsection gets a summary. Every section gets a summary. The document itself gets a summary.

**Avoid patterns like:**

• “In this section, we’ll explore… [3000 words later] …as we’ve seen in this section.”

• “A conclusion that restates every point already made in the previous 3000 words”

• “And so we return to where we began.”

The Dead Metaphor

Latching onto a single metaphor and beating it into the ground across the entire thing. A human writer would introduce a metaphor, use it then move on. AI will repeat the same metaphor 5-10 times.

**Avoid patterns like:**

• “The ecosystem needs ecosystems to build ecosystem value.”

• “Walls and doors used 30+ times in the same article”

• “Every paragraph finds a way to say “primitives” again”

Historical Analogy Stacking

ESPECIALLY COMMON IN TECHNICAL WRITING: Rapid-fire listing of historical companies or tech revolutions to build false authority.

**Avoid patterns like:**

• “Apple didn’t build Uber. Facebook didn’t build Spotify. Stripe didn’t build Shopify. AWS didn’t build Airbnb.”

• “Every major technological shift — the web, mobile, social, cloud — followed the same pattern.”

• “Take Spotify… Or consider Uber… Airbnb followed a similar path… Shopify is another example… Even Discord…”

One-Point Dilution

Making a single argument and restating it in 10 different ways across thousands of words. The model pads a simple thesis to feel “comprehensive” by rephrasing the same idea with different metaphors, examples, and framings. An 800-word argument becomes 4000 words of circular repetition.

**Avoid patterns like:**

• “The same point, restated eight ways across 4000 words.”

• “Each section rephrases the thesis with a different metaphor but adds nothing new”

Content Duplication

Repeating entire sections or paragraphs verbatim within the same piece. This happens when the model loses track of what it has already written, especially in longer pieces. A dead giveaway of unedited AI output. Less common nowadays.

**Avoid patterns like:**

• “The same section appeared twice, word-for-word identical.”

• “Paragraph 3 and paragraph 17 are the same sentence reworded”

The Signposted Conclusion

Explicitly announcing the conclusion with “In conclusion”, “To sum up”, or “In summary”. Competent writing doesn’t need to tell you it’s concluding. The reader can feel it. AI signals its structural moves because it’s following a template, not writing organically.

**Avoid patterns like:**

• “In conclusion, the future of AI depends on…”

• “To sum up, we’ve explored three key themes…”

• “In summary, the evidence suggests…”

“Despite Its Challenges…”

The rigid formula where AI acknowledges problems only to immediately dismiss them. Always follows the same beat: “Despite its [positive words], [subject] faces challenges…” then ends with “Despite these challenges, [optimistic conclusion].”.

**Avoid patterns like:**

• “Despite these challenges, the initiative continues to thrive.”

• “Despite its industrial and residential prosperity, Korattur faces challenges typical of urban areas.”

• “Despite their promising applications, pyroelectric materials face several challenges that must be addressed for broader adoption.”

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Remember: any of these patterns used once might be fine. The problem is when

multiple tropes appear together or when a single trope is used repeatedly.

Write like a human: varied, imperfect, specific.

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本文转载自：黑鸟 黑鸟 黑鸟《避开英文AI写作套路：别让AI检测工具成了写作的枷锁》