AI Writing vs Human Writing β Side-by-Side Examples
The best way to understand AI writing detection is to see real examples. Below are three side-by-side comparisons across essay, narrative, and email writing β annotated with exactly which signals make each sample identifiable.
Why Compare AI and Human Writing?
Reading about AI writing patterns in the abstract is useful β but seeing them in real text is far more instructive. The differences between AI-generated and human-generated writing are not obvious to a casual reader; they're statistical and structural, not simply grammatical errors or factual mistakes. AI writing is often grammatically perfect. That's what makes it hard to detect by reading alone.
The examples below were constructed to illustrate the most reliable detection signals: vocabulary, burstiness, sentence-subject monotony, and structural predictability. Each pair covers the same topic, written in two different ways β one as an LLM would typically generate it, one as a human writer would typically produce it. After each pair, we annotate precisely which signals would fire in our detector.
Example 1: The Academic Essay Paragraph
Topic: "The impact of social media on mental health in young people."
The pervasive influence of social media on the mental health of young people has become a topic of paramount importance in contemporary discourse. It is worth noting that the nuanced relationship between social media usage and psychological well-being is multifaceted and requires meticulous examination. Furthermore, research has consistently demonstrated that excessive exposure to curated online content can foster unrealistic comparisons, thereby contributing to elevated levels of anxiety and diminished self-esteem among adolescents. Moreover, the addictive design of these platforms β engineered to maximize engagement β plays a pivotal role in perpetuating compulsive usage patterns. In conclusion, addressing this complex issue necessitates a comprehensive, multidisciplinary approach that considers both individual and systemic factors.
- π© AI vocabulary: paramount, nuanced, meticulous, multifaceted, pivotal, comprehensive β 6 flagged words in 105 words
- π© Significance phrases: "it is worth noting," "of paramount importance"
- π© Transitions: Furthermore, Moreover in consecutive sentences
- π© Closing ritual: "In conclusion"
- π© Uniform sentence length: 18, 22, 34, 25, 28 words β low CV
Social media has been rough on teenagers β and the data backs that up. My sister spent three years glued to Instagram before she realized how much time she was wasting comparing herself to people she'd never meet. That's not an unusual story. Studies from the early 2020s kept finding the same thing: the more time kids spent scrolling, the worse they felt about themselves. The platforms aren't neutral β the likes, the infinite scroll, the algorithm that learns what keeps you hooked β it's all designed to keep you on there as long as possible. There's no mystery about why it's a problem. The real question is what anyone's going to do about it.
- β No flagged AI vocabulary
- β Personal anecdote (sister/Instagram)
- β Sentence lengths: 8, 21, 5, 27, 33, 10, 14 words β high CV
- β Informal register appropriate to the topic
- β No closing ritual β ends with a genuine question
Example 2: The Narrative / Story Paragraph
Prompt: "She arrived at the empty house and realized something was wrong."
She walked up to the front door. She noticed it was slightly ajar. She felt a chill run down her spine. She couldn't help but hesitate on the threshold. She heard a sound from inside β something scraping across the floor. She felt her heart race in her chest. She took a deep breath and tried to calm herself. She pushed the door open slowly. She felt a wave of unease wash over her. She stepped inside and looked around the darkened hallway. She couldn't help but feel that something was very wrong.
- π© Subject monotony: 11 of 11 sentences begin with "She"
- π© AI fiction clichΓ©s: "couldn't help but," "felt her heart race," "wave of unease wash over her"
- π© Phrase loops: "couldn't help but" repeated
- π© Uniform sentence length: 6β14 words, every sentence
- π© No sensory specificity β no smell, no visual detail
The front door was open. Not swung wide β just an inch, the way it was when Dad forgot to pull it fully shut β but Dad was in Phoenix and wouldn't be back until Thursday. Maya stood on the step, keys still in her hand, looking at that inch-wide gap like it owed her an explanation. The house smelled wrong too, once she got close enough. Paint? Bleach? Something underneath both of those. She should call someone. She didn't move. The gap stayed there, patient, waiting, a dark seam in the beige of the door frame, and the street behind her had gone very quiet, the way streets did when something was about to happen.
- β Varied sentence openings β 7 different subjects/structures
- β Specific sensory details: smell, visual specificity, temperature absent but implied
- β Named character (Maya) with backstory (Dad, Phoenix, Thursday)
- β Highly varied sentence lengths: 4, 25, 28, 8, 4, 4, 3, 47 words
- β No AI fiction clichΓ©s
Example 3: The Professional Email
Context: Following up on a delayed project report.
I hope this email finds you well. I wanted to reach out regarding our upcoming project report, which I understand may be experiencing some delays. Furthermore, I wanted to note that this report plays a pivotal role in our end-of-quarter deliverables, and it is important to ensure that we are aligned on the timeline. Additionally, if there are any obstacles or challenges that are impeding progress, I would be more than happy to provide assistance or resources as needed. In conclusion, I look forward to your timely response and to working collaboratively toward a successful resolution of this matter. Please do not hesitate to reach out if you have any questions or concerns.
- π© AI vocabulary: pivotal, impeding
- π© Filler opener: "I hope this email finds you well"
- π© Transitions: Furthermore, Additionally in an email
- π© Closing ritual: "In conclusion" in an email
- π© Significance phrases: "it is important to ensure," "pivotal role"
- π© No specific information β no names, no actual deadline mentioned
Hey Tom β quick check-in on the Henderson report. We're supposed to have it ready for the board deck by the 18th and I realized I haven't seen a draft come through yet. No alarm bells yet, just want to make sure we're on track. Is there anything stuck on your end? I can loop in Priya if you need an extra pair of hands on the financials section β she did the Q2 version and knows the format. Just let me know where things are. Cheers, Marcus
- β No AI vocabulary words
- β Specific names (Tom, Priya, Henderson report), specific date (the 18th)
- β Informal, natural register appropriate to colleague email
- β No formal transitions or closing ritual
- β Varied sentence lengths: 10, 24, 9, 7, 27, 9, 3 words
The 5 Biggest Differences Between AI and Human Writing
| Feature | π€ AI Writing | βοΈ Human Writing |
|---|---|---|
| Sentence length | Uniform β clusters in 15β25 words with low variation | Highly varied β mixes fragments with long complex sentences |
| Vocabulary register | Formal and elevated β overuses "delve," "meticulous," "pivotal" | Natural and mixed β register matches context, not always formal |
| Personal voice | Generic and balanced β avoids specific opinions or anecdotes | Specific β names real people, places, experiences, and opinions |
| Conclusion style | Almost always ends with "In conclusion" or "In summary" | Often casual, abrupt, or absent β matching real-world writing norms |
| Transitions | Frequent formal transitions β Furthermore, Moreover, Additionally | Occasional and varied β transitions emerge from content, not habit |
What This Means for Detection
Looking at the examples above, it becomes clear that detecting AI writing isn't about finding obvious errors β AI rarely makes grammatical mistakes. It's about recognizing statistical patterns that differ from what humans naturally produce.
The Academic essay example is detectable because of vocabulary density and structural formulas. The Narrative example is detectable because of subject monotony and phrase loops. The Email example is detectable because of register mismatch β a human doesn't write "In conclusion" in a project follow-up email.
Each of these patterns is independently weak. But when 4 or more fire on the same text, the combined signal becomes reliable. That's the core logic behind our detection algorithm β it's not one rule, it's a weighted combination of 12 independent linguistic signals, each calibrated to human writing baselines.
Want to see how any text you have scores? Use the detector below.
Try It Yourself
Paste any text into the detector below β or try copying one of the AI examples above and running it through to see exactly which signals fire.
Signal Breakdown (click each signal to expand)
Note: This tool uses linguistic pattern analysis β not an AI language model. Browser-based detectors achieve ~70-80% accuracy. Use as a screening tool, not sole evidence. How it works β
Frequently Asked Questions
Can humans write like AI?
Yes β and this is a real source of false positives in AI detection. ESL (English as a Second Language) writers often use formal vocabulary, uniform sentence structures, and heavy transitions because they've learned English through formal instruction rather than natural immersion. Very formal academic writers, legal writers, and certain non-fiction genres also share characteristics with AI writing. This is why our detector caps at 97% certainty and why scores in the 40β65% range should always be interpreted with context about the author's known writing style.
Does AI writing have a consistent style?
Yes, within model families. ChatGPT has a distinctive formal-vocabulary fingerprint identified by Kobak et al. (2025). Claude tends toward slightly more varied sentence structures but retains structural predictability. Gemini often produces notably consistent paragraph lengths. All major LLMs share the core signals β burstiness, transitions, closing rituals β because these emerge from transformer architecture and RLHF training processes common to all models, not from model-specific differences.
Can experienced editors always tell the difference?
Research suggests experienced editors detect AI writing at rates around 55β65% in blind tests β barely above random chance for high-quality AI outputs. Human readers are good at detecting obviously bad AI writing, but the statistical signals that reliably separate AI from human text are not intuitive to perception. This is why computational detection β even imperfect β adds genuine value beyond human editorial judgment alone.
What about ESL writers β do they write like AI?
This is one of the most important limitations of all AI detectors. ESL writers trained in formal English instruction often produce writing that shares characteristics with AI: elevated formal vocabulary, uniform sentence structure, heavy use of transitions, and sometimes closing ritual phrases learned from formal essay templates. Any AI detector score for an ESL writer should be weighted against their writing history and known style. A high score from a student who has consistently produced formal-register writing across multiple assignments is far less meaningful than a high score from someone who previously wrote in a clearly informal, varied style.
Explore Further
- What Is AI Writing? β How AI text is generated and why it matters
- How Our Detector Works β Full explanation of all 12 detection signals
- ChatGPT Detector β Specialized detection for ChatGPT and GPT-4 writing
- Humanized AI Detector β Detect AI text that's been rewritten to avoid detection