CNET47%

Your Brain Is a Better AI Detector Than Any Tool Out There. Here's How to Use It 52%

By Rachel Kane58%

6/21/2026, 2:01:00 PM

BS Summary: This article contains 32 faulty reasoning types, including Biased Writer Voice, Indoctrination, and Hasty Generalization, with Negativity Bias as the most egregious example at 33.8% saturation with 252 hits. Analysis detected 2,741 faulty-reasoning hits from 745 analyzed words, generating a BS Score of 51% and a BS Rank of 52% (10,667 of 21,886 articles). This article is worse (more manipulative) than 51.30% of the article peer group.

AI detection tools promised a clean solution to the internet's growing slop problem. 
What they've delivered is a coin flip. 
Academic studies and independent tests have repeatedly shown that the most widely used detectors misidentify human writing as AI-generated at rates that make them actively counterproductive, which is a problem that gets worse as AI writing gets better. 
Meanwhile, the qualities that actually distinguish machine-generated prose from human writing are consistent enough that a trained reader can identify them reliably, without any software assistance. 
Here's what those qualities are and how to recognize them. 
AI is now seemingly the ultimate "work smarter, not harder" shortcut, and nowhere is that more obvious than in the classroom and in some workplaces. 
While tools such as ChatGPT are great for writing grocery lists or other kinds of brainstorming tasks, they're also responsible for creating full-on slop. 
As a professor, I'm seeing AI tools such as ChatGPT and Claude pop up in my inbox every single day, and frankly, they're getting easier to spot -- not because of "AI detectors," but because the writing is so painfully predictable. 
One of the biggest red flags is what I call the "Wikipedia Voice," or text that's grammatically perfect but completely soulless, relying on vague, over-the-top language that parrots the prompt back at me. 
If a student who usually writes in fragments suddenly hands in a "multifaceted analysis" that uses the word "tapestry" or "delve," I become suspicious. 
AI loves a cliché and can't resist wrapping every paragraph in a neat little summary bow that starts with "In conclusion." 
It's the written equivalent of a deepfake: It looks right at a glance, but once you start looking for the "human" imperfections, the whole thing falls apart. 
But can teachers use AI tools to catch students using AI tools? 
I devised some ways to be smarter in spotting artificial intelligence in papers. 
How to catch AI cheaters 
Here's how to use AI tools to catch cheaters in your class. 
Understand AI capabilities 
There are AI tools on the market that can scan an assignment and its grading criteria to provide a fully written, cited and complete piece of work in a matter of moments. 
Some of these tools include GPTZero and Smodin. 
Familiarizing yourself with tools like these is the first step in the war against AI-driven integrity violations. 
Do as the cheaters do 
Before the semester begins, copy and paste all your assignments into a tool like ChatGPT and ask it to do the work for you. 
When you have an example of the type of results it provides specifically in response to your assignments, you'll be better equipped to catch AI-written answers. 
You could also use a tool designed specifically to spot AI writing in papers. 
Get a real sample of writing 
At the beginning of the semester, require your students to submit a simple, fun and personal piece of writing to you. 
The prompt should be something like "200 words on what your favorite toy was as a child," or "Tell me a story about the most fun you ever had." 
Once you have a sample of the student's real writing style in hand, you can use it later to have an AI tool review that sample against what you suspect might be AI-written work. 
Ask for a rewrite 
If you suspect a student of using AI to cheat on their assignment, take the submitted work and ask an AI tool to rewrite the work for you. 
In most cases I've encountered, an AI tool will rewrite its own work in the laziest manner possible, substituting synonyms instead of changing any material elements of the "original" work. 
Can you always tell if AI wrote something? 
The most important part about catching cheaters who use AI to do their work is having a reasonable amount of evidence to show the student and the administration at your school, if it comes to that. 
Maintaining a skeptical mind when grading is vital, and your ability to demonstrate ease of use and understanding with these tools will make your case that much stronger. 
Good luck out there in the new AI frontier, fellow teachers. 
Try not to be offended when a student turns in work written by a robot collaborator. 
It's up to us to make the prospect of learning more alluring than the temptation to cheat. 
Article reasoning-pattern comparisonThis article: 22.3%Rachel Kane: 6.8%CNET: 1.7%Confirmation Bias22.3%This article: 3.2%Rachel Kane: 0.8%CNET: 2.3%Anchoring Bias3.2%This article: 22.6%Rachel Kane: 8.0%CNET: 2.9%Availability Heuristic22.6%This article: 3.5%Rachel Kane: 1.7%CNET: 0.9%Representativeness Heuristic3.5%This article: 0.0%Rachel Kane: 0.0%CNET: 0.2%Hindsight Bias0.0%This article: 6.8%Rachel Kane: 8.0%CNET: 2.5%Overconfidence Bias6.8%This article: 0.0%Rachel Kane: 3.0%CNET: 4.5%Framing Effect0.0%This article: 2.8%Rachel Kane: 0.7%CNET: 1.3%Loss Aversion2.8%This article: 5.6%Rachel Kane: 1.4%CNET: 0.5%Status Quo Bias5.6%This article: 0.0%Rachel Kane: 0.0%CNET: 0.2%Sunk Cost Effect0.0%This article: 10.2%Rachel Kane: 2.6%CNET: 3.8%Optimism Bias10.2%This article: 7.2%Rachel Kane: 1.8%CNET: 1.3%Pessimism Bias7.2%This article: 33.8%Rachel Kane: 8.5%CNET: 3.6%Negativity Bias33.8%This article: 7.2%Rachel Kane: 1.8%CNET: 1.7%Self-Serving Bias7.2%This article: 3.8%Rachel Kane: 1.7%CNET: 0.2%Fundamental Attribution Error3.8%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Actor-Observer Bias0.0%This article: 3.6%Rachel Kane: 1.5%CNET: 0.4%In-Group Bias3.6%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 4.3%Halo Effect0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Horn Effect0.0%This article: 1.6%Rachel Kane: 0.4%CNET: 0.0%Dunning-Kruger Effect1.6%This article: 10.6%Rachel Kane: 2.7%CNET: 1.4%Recency Bias10.6%This article: 0.0%Rachel Kane: 0.0%CNET: 0.4%Primacy Effect0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Blind-Spot Bias0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Ad Hominem0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.0%Straw Man0.0%This article: 17.9%Rachel Kane: 5.7%CNET: 4.2%Appeal to Authority17.9%This article: 14.4%Rachel Kane: 3.8%CNET: 1.2%False Dilemma14.4%This article: 3.6%Rachel Kane: 0.9%CNET: 0.5%Slippery Slope3.6%This article: 3.5%Rachel Kane: 2.0%CNET: 0.2%Circular Reasoning3.5%This article: 27.5%Rachel Kane: 14.7%CNET: 4.7%Hasty Generalization27.5%This article: 3.2%Rachel Kane: 0.8%CNET: 0.1%Red Herring3.2%This article: 0.7%Rachel Kane: 0.2%CNET: 0.6%Bandwagon0.7%This article: 17.6%Rachel Kane: 4.4%CNET: 2.3%Appeal to Emotion17.6%This article: 3.5%Rachel Kane: 0.9%CNET: 0.5%Begging the Question3.5%This article: 0.0%Rachel Kane: 0.0%CNET: 1.5%Post Hoc (False Cause)0.0%This article: 0.7%Rachel Kane: 0.2%CNET: 0.0%Tu Quoque0.7%This article: 11.5%Rachel Kane: 2.9%CNET: 0.4%Burden of Proof11.5%This article: 0.0%Rachel Kane: 0.0%CNET: 0.3%Appeal to Nature0.0%This article: 3.2%Rachel Kane: 0.8%CNET: 0.1%Composition/Division3.2%This article: 14.0%Rachel Kane: 8.3%CNET: 6.3%Anecdotal14.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%No True Scotsman0.0%This article: 11.3%Rachel Kane: 2.8%CNET: 3.0%Ambiguity (Equivocation)11.3%This article: 0.0%Rachel Kane: 0.0%CNET: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Middle Ground0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.0%Personal Incredulity0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Special Pleading0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.1%Genetic Fallacy0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 1.0%Unattributed Quote0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.3%Quote-first Misdirection0.0%This article: 33.6%Rachel Kane: 18.0%CNET: 6.2%Biased Writer Voice33.6%This article: 32.9%Rachel Kane: 12.2%CNET: 2.4%Indoctrination32.9%This article: 0.0%Rachel Kane: 0.0%CNET: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Rachel Kane: 0.0%CNET: 0.0%Politically Right Leaning Bias0.0%This article: 24.0%Rachel Kane: 6.0%CNET: 13.1%Attempt to Sell a Product or S…24.0%

745 words analyzed.

Speakers

No attributed speakers were identified in this analysis.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.