Research Study 2024

Code plagiarism detection with 99.3% accuracy.

Our 24-month study across 2,847 institutions and 50,247 code samples shows that AI-first detection dramatically outperforms traditional tools, even against obfuscated and AI-generated submissions.

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99.3%
Detection accuracy
50,247
Code samples analyzed
2,847
Institutions studied
24 months
Longitudinal research

Study scope and methodology

We evaluated academic integrity outcomes across global institutions, comparing AI detection to established methods such as MOSS, Turnitin code checks, and manual review.

Global coverage

67 countries, public and private institutions, undergraduate to doctoral programs.

Multi-language datasets

40+ programming languages including Python, Java, C++, JavaScript, and Rust.

AI-specific tests

Controlled prompts to GPT-4, Claude, and Copilot with obfuscation and style transfer.

Accuracy metrics

Precision, recall, false positives, and detection latency were tracked per method.

Key findings

Traditional tools miss most modern plagiarism cases. AI-based semantic detection uncovers plagiarism even when syntax is heavily altered or translated across languages.

78% incidence rate

Assignments contained plagiarized or improperly sourced code.

99.7% precision

False positives limited to 1.3% across all datasets.

4.3x improvement

Accuracy jump versus traditional string-matching tools.

Method comparison

Method
Accuracy
False positives
AI detection
Codequiry AI
99.3%
1.3%
99.3%
MOSS
45.7%
12.4%
8.1%
Turnitin Code
31.2%
18.7%
5.3%
Manual review
23.4%
25.1%
2.1%

Impact on institutions

Administrators report measurable improvements in student outcomes, faculty workload, and institutional reputation within a single academic year.

$2.3M average annual savings

Reduced investigation time, fewer appeals, and lower legal exposure.

500% to 2800% ROI

Return on investment realized within 6 to 12 months.

72-hour deployment

Integrates with major LMS platforms and existing workflows.

89% plagiarism reduction

Significant decline in incidents after policy adoption.

Implementation timeline

Fast setup, immediate visibility, and rapid ROI.

Days 1-3 LMS integration and bulk import setup.
Weeks 1-2 First detections and instructor onboarding.
Months 1-3 Policy adoption and measurable ROI.

Why AI beats string matching

Semantic analysis captures algorithmic intent, detecting plagiarism even when variable names, formatting, or languages change.

AI-generated code detection

Advanced models identify AI-authored code by structural patterns, style inconsistencies, and semantic signatures.

Frequently asked questions

Quick answers for academic leaders evaluating Codequiry.

How accurate is Codequiry?

We measured 99.3% overall accuracy with 99.7% precision across 50,247 submissions.

Does it detect AI-generated code?

Yes. Detection rates are 99.3% for GPT-4, Claude, and Copilot generated code, even after obfuscation.

How does it compare to MOSS and Turnitin?

Traditional tools detect 31% to 46% of cases; Codequiry detects 99.3% with far fewer false positives.

How long does implementation take?

Most institutions are fully deployed within 72 hours, with full ROI within months.

Which languages are supported?

40+ languages, with cross-language detection for translated submissions.

Transform academic integrity in one semester.

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