Signs of AI Writing and How to Remove Them

Learning objectives

• Name at least eight words on the "AI vocabulary" watchlist and explain why each reads as a tell.
• Explain the Rule of Three and negative parallelism, and rewrite an example of each.
• State the em-dash heuristic and why Wikipedia treats it as a signal, not proof.
• Explain why AI-detection tools are unreliable and what to teach instead.

Prerequisites: Stage 1 in full, especially Lesson 1.3.3 (the ethics line); Stage 2 Lesson 2.4.3 (citation ethics).

Core concepts

Start with the primary source: Wikipedia's "Signs of AI writing" names a specific AI-vocabulary watchlist to purge on sight, delve, tapestry, testament, underscore, pivotal, boasts, showcase, intricate, vibrant, crucial, garner, foster, robust, seamless, and "stands as/serves as" constructions that replace a plain "is." If a draft comment for r/SaaS contains three of these, it did not survive a human editor, and Reddit readers can tell.

Structural tells matter more, because they survive a thesaurus pass. The Rule of Three is the most reliable: models default to triplets, and when every list has exactly three items the rhythm itself reads as machine-generated (Wikipedia; Forbes, Sep 2025). Break it deliberately, two items sometimes, five sometimes. Negative parallelism ("It's not just a scheduling tool, it's a trust-building tool") mimics insight while saying little; strip it and state the claim. Tailing -ing clauses ("...further cementing its role in the workflow") are superficial add-ons; delete them or replace with a real consequence.

Vague attribution is the clearest tell to a skeptical reader. "A recent study shows," "researchers found", none of that survives a comment section, because someone asks "which study?" within the hour. Replace every vague attribution with the actual source, number, and tool name.

Sentence rhythm is subtler: AI text clusters in a narrow 15-25 word band, producing a "metronomic" rhythm humans perceive as artificial (GPTZero; Writing Cooperative). Fix it with deliberate variance, a fragment, then a long run, then something medium. Read drafts aloud; anywhere you stumble is where a reader's attention snags too.

On em dashes: treat the "ChatGPT dash" as a heuristic, not gospel, humans use them too, and the models learned the habit from us. A workable rule (vrid.ai): more than roughly three per 500 words is worth investigating, not a verdict. Wikipedia's guide and TechCrunch's November 2025 coverage of it both warn against over-relying on any single detector, even heavy LLM users correctly identify AI text only about 90% of the time by ear. The actual skill is human pattern recognition applied before posting, not a checker's clean bill of health.

Video lessons

Supporting reading

• Wikipedia:Signs of AI writing (https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing), the definitive primary source; read it in full.
• The best guide to spotting AI writing comes from Wikipedia, TechCrunch (https://techcrunch.com/2025/11/20/the-best-guide-to-spotting-ai-writing-comes-from-wikipedia/), why detector tools are unreliable.
• How to Avoid AI Detection (the Right Way): Top Writing Strategies, Grammarly blog (https://www.grammarly.com/blog/ai/how-to-avoid-ai-detection/), the sentence-length clustering data.

Exercise

Take an AI-drafted paragraph from the last month. Circle every AI-vocabulary word, triplet, negative-parallelism sentence, and vague attribution. Rewrite it by hand, aloud, with deliberate sentence-length variance.

Assignment

Build a personal "never use these words" list of 15+ terms and run your next three Reddit drafts against it. Log violations caught per draft.

Claude workflow

• Skill idea: an "AI-tell scanner" that flags AI-vocabulary words, triplets, negative parallelism, and em-dash density per 500 words in any draft.
• Automation: none, this stays a human judgment call; a scheduled reminder to run the scanner before each post is the most that should be automated.

Expected outcomes

• Can name at least eight AI-vocabulary words from memory.
• Can identify the Rule of Three, negative parallelism, and tailing -ing clauses on sight.
• Personal watchlist built and applied to three real drafts with a logged violation count.

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