Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
11 min
Emily Watson
·
1 week ago
A practical walkthrough for CS instructors and TAs: wire Codequiry's peer similarity and AI detection into a GitHub Actions workflow, get CSV results on every commit, and triage suspicious submissions in under a minute each.
General
8 min
Alex Petrov
·
2 weeks ago
A logistics company needed to know whether a contractor's 14,000-line Python service was original before paying the final invoice. Token-based fingerprinting showed exactly how much had been lifted from an open source repo and rewritten just enough to hide. What the team learned about normalization, thresholds, and the limits of similarity scoring applies to any company that accepts outside code.
General
10 min
Alex Petrov
·
2 weeks ago
As a bootcamp instructor and open-source maintainer, I've closed hundreds of license-related pull requests. Most were copy-paste fixes where someone dropped a Stack Overflow function into a GPL project. This walks through what actually triggers license tickets, which scanners catch what, and where source similarity tools save you from silent compliance failures.
General
10 min
Alex Petrov
·
2 weeks ago
A direct walkthrough of a Python code plagiarism audit across three bootcamp cohorts. Learn which thresholds actually caught copied code, why starter-code exclusion matters, and how to stack web and AI detection into one honest review pass.
General
8 min
Rachel Foster
·
2 weeks ago
AI code detectors are producing false positives on the most ordinary submissions in CS1: code that looks the same because the assignment required it. This reported piece examines the data, the workflow changes instructors are making, and why combining AI detection with structural similarity reduces the error rate.
General
9 min
James Okafor
·
2 weeks ago
Line diffs collapse when students rename variables, reorder functions, and extract methods. Here's how token fingerprints and AST normalization catch refactored copies, and how to triage hundreds of similarity reports without drowning in false positives.
General
9 min
Alex Petrov
·
2 weeks ago
A bootcamp instructor explains how token-level perplexity works as an AI code signal, what thresholds we actually use in Java review, and why a single statistical score is never enough on its own.
General
6 min
Rachel Foster
·
3 weeks ago
Set up an automated code plagiarism detection pipeline in GitHub Actions using Codequiry's REST API. Follow precise steps to write a workflow YAML and a Python script that submits student code, receives similarity scores, flags suspicious pushes, and optionally detects AI-generated code. Includes threshold tuning, result interpretation, and false positive handling.
General
12 min
Priya Sharma
·
3 weeks ago
A three-semester analysis of 4,100 CS1 submissions shows why a 70% similarity score means different things in Python, Java, C++, and JavaScript. I break down percentile thresholds, boilerplate effects, false positives, and how to pair similarity checks with AI detection.
General
10 min
Priya Sharma
·
3 weeks ago
Most plagiarism checkers only compare submissions against each other, so a Stack Overflow snippet with renamed variables sails through. We break down how web source matching uses token and AST fingerprints to catch code copied from Stack Overflow, GitHub, and tutorials, and show a Java example where refactoring did not hide the source.
General
4 min
Marcus Rodriguez
·
3 weeks ago
A three-semester case study at Briarwood University tracked 14,000 Python assignments through four AI code detectors. The result: false positive rates from 4% to 9% overall, spiking to 23% on common algorithmic patterns. This article breaks down the data, the code patterns that trigger false flags, and a practical workflow for balancing detection with fairness.
General
7 min
James Okafor
·
3 weeks ago
Web code plagiarism hides in plain sight when students copy from Stack Overflow, GitHub, or tutorials and rename a few variables. This post shows how to teach detection as a skill, design assignments that surface copied web code, and use a source-aware checker like Codequiry to see the evidence.