Decoding Your Originality Report

The SafeAssign AI Checker report is not a simple pass/fail grade. It provides a Combined Integrity Index. This metric synthesizes two distinct forensic layers:

Similarity Matching

Checks for direct text matches against the Global Reference Database (web pages, journals, and past student submissions).

Linguistic Forensics

Analyzes statistical probability (perplexity & burstiness) to detect synthetic content generated by AI models.

Risk Level Interpretation

0-15%

Low Risk (Green Zone)

Status: Original Work. The text exhibits natural human variation in sentence structure (“burstiness”). Common phrases or citations may trigger minor percentages, which is normal for academic writing.

Action: No revision typically required.
15-40%

Medium Risk (Yellow Zone)

Status: Hybrid Composition. Indicates a mix of original thought and potentially AI-assisted editing or generation. Often seen when students use tools like Grammarly GO or ChatGPT to rewrite paragraphs.

Action: Instructor review recommended. Check version history.
40%+

High Risk (Red Zone)

Status: AI Generated. Significant statistical patterns consistent with Large Language Models. The text lacks the semantic entropy (randomness) typical of human cognition.

Action: Formal academic integrity audit required.

Understanding Deep Metrics

Global Reference Match

This percentage represents text found verbatim in our database of over 10 million student submissions and academic journals.

  • Direct quotes should be excluded if cited.
  • Bibliographies often trigger false positives here.

AI Model Signature

Identifies specific “watermarks” of logic used by models like Claude 4 or GPT-5.

  • High consistnecy in sentence length often triggers this.
  • Overuse of transition words (“Furthermore,” “In conclusion”).

Perplexity & Burstiness

Perplexity

The measure of how “surprised” a model is by the text. AI maximizes probability, so it writes with Low Perplexity (predictable). Humans write with High Perplexity (unpredictable).

AI (Low) Human (High)

Burstiness

The variation in sentence structure and length. AI tends to be monotonous (flat line). Humans vary short and long sentences dynamically (spikes).

High Burstiness Pattern (Human)

Common False Positives

Certain writing styles or tools can inadvertently trigger higher AI scores without malicious intent. Be aware of these factors:

  • Grammarly & Spell Checkers

    Heavy use of “Rewrite for Clarity” features can strip natural human “burstiness,” making text appear machine-generated.

  • Technical & Legal Writing

    Formulaic documents (lab reports, legal briefs) naturally use repetitive structures and low-entropy language.

  • Non-Native English Speakers

    Writers with limited vocabulary may use simpler, more predictable sentence structures that algorithms can mistake for AI.

FAQ & Appeals

How can I appeal a high AI score?

If you believe your work was flagged incorrectly, present your instructor with Version History (from Google Docs or Word) showing the evolution of your document. Drafts, outlines, and research notes are strong evidence of original work.

Why is my bibliography flagged?

Bibliographies often list standard citations found in millions of other papers. This triggers “Similarity Matching” but usually not “AI Detection.” Instructors typically exclude references from the final score.

Does this detect paraphrasing tools like Quillbot?

Yes. Paraphrasers often leave specific syntax patterns that our linguistic model detects as “unnatural flow,” even if the words themselves are unique.

Can I resubmit my paper to lower the score?

This depends on your institution’s policy. If allowed, we recommend focusing on adding personal analysis and varying your sentence structure rather than simply swapping synonyms.

What to do next?

Improve Your Integrity Score

Academic Fairness Clause

This report is a screening tool, not a verdict. A high probability score does not prove misconduct. It indicates a need for further human review. Instructors must verify results using version history, oral defense, or comparative analysis of previous work.