Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
14 min
Emily Watson
·
1 month ago
Web code plagiarism is distinct from peer copying, and standard similarity tools miss it. This guide breaks down how to detect code lifted from Stack Overflow, GitHub, and tutorials, with a practical grading workflow that catches it without drowning TAs in false flags.
General
10 min
Alex Petrov
·
1 month ago
Quoted Google searches used to be the standard way to catch a Stack Overflow lift. I tracked the shift across tools, fingerprints, and live web corpora, and why modern checks need to pair source matching with AI detection. The short version: the web changed the question from "who copied whom" to "where did this code come from."
General
11 min
Emily Watson
·
1 month 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
10 min
Alex Petrov
·
1 month 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
9 min
Alex Petrov
·
1 month 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
·
1 month 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
·
1 month 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
·
1 month 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
·
1 month 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
·
1 month 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.
General
8 min
Marcus Rodriguez
·
1 month ago
Most AI-generated code in student submissions goes unnoticed when instructors rely on intuition or a single detector. This research-style guide explains how to layer statistical signals, peer similarity, web-source checks, and rubric design to reliably catch AI-assisted code without manufacturing false positives.
General
1 min
Emily Watson
·
1 month ago
Most ChatGPT-generated Python doesn't announce itself with a watermark. It announces itself in docstrings that restate the function name, broad exception blocks, and comments that narrate the obvious. A CS2 TA's 20-minute screen, combining Codequiry's AI detector and similarity checks, catches the majority of cases before the first grade is entered.