Will using AI tools always result in high AIGC detection?

Not necessarily. The scope and method of AI assistance matters. If AI was only used for topic selection, outlining, and data processing, with final writing done independently, AIGC rate can be very low.

Can AIGC detection identify which specific AI tool was used?

Currently no. AIGC detection provides probability and feature scores but does not identify specific tools.

Which should I handle first when addressing both plagiarism and AIGC?

It is recommended to address plagiarism first, then AIGC. Plagiarism reduction sometimes requires paragraph restructuring, while AIGC reduction needs subjective expression injection.

Detection Comparison Guide

What Is the Difference Between AIGC Detection and Similarity Checks?

This guide explains the core differences between AIGC detection and plagiarism checks, what each system evaluates, and strategies for passing both.

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What this page helps you do first

Plagiarism and AIGC checks operate on completely different principles
Each evaluates different aspects and has different passing thresholds
Strategies for passing both checks simultaneously

Plagiarism and AIGC checks are fundamentally two separate systems

Plagiarism checks (similarity detection) compare your paper against existing literature databases to detect copying or reproduction of existing content.

AIGC detection calculates whether your text exhibits AI writing patterns by analyzing statistical characteristics of the writing style.

They operate independently. A fully original paper written with AI assistance may have low similarity but high AIGC rate.

Core differences between the two systems

Plagiarism checks "content source": whether you copied existing content
AIGC checks "writing style": whether text exhibits AI writing statistical patterns
Plagiarism has national standards (typically 10%-20%)
AIGC has no national standard, thresholds vary by institution
Plagiarism databases contain published papers and literature
AIGC detection relies on model training sets and text feature databases

Why passing similarity check does not guarantee passing AIGC

If you heavily used AI assistance, the content may be original but the writing style is highly AI-like, plagiarism will pass (no copying), but AIGC detection will fail (obvious AI patterns).

Strategies for passing both checks

First stage: pass similarity check - ensure no large sections copied or improper citations
Revision stage: reduce AIGC - transform high AI-feature paragraphs in sentence structure and expression
Finalize only after passing both checks to avoid rework
Self-test on dual platforms before formal submission when possible

Frequently asked questions

Will using AI tools always result in high AIGC detection?: Not necessarily. The scope and method of AI assistance matters. If AI was only used for topic selection, outlining, and data processing, with final writing done independently, AIGC rate can be very low.
Can AIGC detection identify which specific AI tool was used?: Currently no. AIGC detection provides probability and feature scores but does not identify specific tools.
Which should I handle first when addressing both plagiarism and AIGC?: It is recommended to address plagiarism first, then AIGC. Plagiarism reduction sometimes requires paragraph restructuring, while AIGC reduction needs subjective expression injection.

Visit plagiarism reduction page Visit AIGC reduction page Understand AIGC detection standards Return to help center