Expert insights on AI code detection and academic integrity
As AI coding assistants become ubiquitous, learn how institutions are adapting to detect AI-generated code and maintain educational standards.
Codequiry Editorial Team
·
Jan 5, 2026
Stay ahead with expert analysis and practical guides
General
12 min
Marcus Rodriguez
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13 hours ago
When a single CS1 assignment yields 300+ submissions, manual plagiarism checking simply doesn't scale. This hands-on guide walks through connecting Canvas to Codequiry's API, running similarity and AI-detection scans with a handful of Python scripts, and posting flagged results directly back into the SpeedGrader — so you catch the cases that matter without drowning in paperwork.
General
12 min
Marcus Rodriguez
·
2 days ago
A single run of an AI detector on a suspicious student submission is not enough. CS professors need a systematic triage protocol that stacks similarity analysis, AI code detection, web-source fingerprinting, and manual review into a defensible pipeline. This article outlines a concrete workflow you can implement this semester.
General
13 min
Priya Sharma
·
3 days ago
AI large language models can now generate passable code for many introductory CS assignments, leaving instructors scrambling. A systematic scanning framework—combining AI detection, plagiarism analysis, and human review gates—can reliably identify AI-written submissions while respecting due process. Here’s how to build one.
General
8 min
James Okafor
·
5 days ago
Modern CS courses often span multiple programming languages, but traditional plagiarism tools like MOSS only compare files within the same language. This case study walks through a practical detection pipeline that catches students rewriting Java logic in Python, using token-normalised AST comparisons and Codequiry’s cross-language API.
General
10 min
Alex Petrov
·
1 week ago
Traditional similarity tools like MOSS and JPlag compare student submissions against each other but leave a massive blind spot: code copied directly from Stack Overflow, GitHub repositories, and online tutorials. This article examines how web source detection works, what it catches that peer comparison misses, and why both approaches together give you the real picture of code originality.
AI Detection
9 min
Priya Sharma
·
2 weeks ago
Burstiness and perplexity aren't just linguistic curiosities—they're the primary statistical signals that distinguish human-written source code from LLM output. This article explains exactly how these measures work under the hood, with worked examples, real-world detection rates, and honest limitations.
Academic Integrity
10 min
Priya Sharma
·
3 weeks ago
Instead of fighting plagiarism after submissions arrive, you can design assignments that are inherently resistant to copying. By embedding unique, student-specific context into problem statements, you make it obvious when code has been copied and also harder for AI tools to produce a correct answer. This article covers concrete techniques—parameterized test cases, local data imports, and narrative hooks—that real universities have used to cut similarity rates by over 40%.
Tutorials
8 min
Emily Watson
·
3 weeks ago
A practical walkthrough for CS instructors who want to wire code similarity checks directly into their grading workflow. Covers tooling choices, LMS integration, and how to layer in web-source and AI-generated code detection for a complete academic integrity pipeline.
Academic Integrity
9 min
Alex Petrov
·
3 weeks ago
Simple changes to assignment design—unique interfaces, randomized test harnesses, and automated similarity checks—drastically reduce code plagiarism. This guide walks through six concrete tactics with real code examples and grading workflows.
General
8 min
Dr. Sarah Chen
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4 weeks ago
Riverdale State University’s computer science department spent years relying on Moss to catch plagiarised assignments. But as student work grew more sophisticated — combining copied web code, heavy refactoring, and AI-generated fragments — the department realised token-based similarity alone was no longer sufficient. This case study covers how they transitioned to a multi-tool detection pipeline.
General
11 min
Emily Watson
·
1 month ago
Plagiarism isn't just a classroom problem. When code from Stack Overflow, GitHub repos, or contractor deliverables enters your production codebase without proper attribution, you risk license violations, IP disputes, and technical debt. This guide shows how static analysis tools detect copied code before it ships, using token matching, AST comparison, and dependency scanning.
General
8 min
David Kim
·
2 months ago
Winnowing fingerprinting is a powerful technique for detecting code plagiarism that survives variable renaming, refactoring, and cosmetic changes. This case study examines how the algorithm works, where it succeeds, and where it falls short compared to AST-based approaches.