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
11 min
Marcus Rodriguez
·
1 week ago
A student turns in Dijkstra's algorithm in Python. Another turned in the same algorithm in Java three weeks earlier. JPlag scores the pair at 4 percent, and MOSS never compares them at all. Here's why language-to-language copying has become the blind spot in academic integrity tooling, and what actually catches it.
General
10 min
Priya Sharma
·
1 week ago
A take-home pipeline with 1,180 submissions and no AI screening is a coin flip dressed up as a hiring process. Here's what the scores actually measure, how to combine them with similarity checks, and where the whole method quietly falls apart.
General
17 min
James Okafor
·
2 weeks ago
Five generations of code similarity detection have each been beaten by a student with a text editor. This is the technical history of how detectors moved from byte comparison to tokens, fingerprints, syntax trees and learned vectors, and what that arc tells you about choosing a tool that can still find a match when the code has been renamed, refactored, or generated by a model.
General
12 min
Dr. Sarah Chen
·
2 weeks ago
A CS2 course with 412 students and eleven TAs ran peer similarity, web-source matching, and AI-generation scoring in one pass per assignment instead of three separate workflows. The interesting result wasn't the flag count. It was that the review meetings stopped arguing about whether a submission looked suspicious and started arguing about what the department's own AI policy actually permitted.
General
12 min
Alex Petrov
·
4 weeks ago
I seeded 48 known-copy pairs into 1,247 real bootcamp submissions and ran them through MOSS, JPlag, Dolos, and Codequiry. Token-based engines held up against renaming and reformatting, then collapsed once control flow changed. Here's the data, the methodology, and where every engine breaks.
General
12 min
David Kim
·
1 month ago
A DevSecOps lead's field notes on putting AI-generated code detection inside a real CI pipeline: what the detector actually measures, which files produce false positives on day one, and why the gate should warn on pull requests but only block on release. Includes the config we run and the numbers we got.
General
11 min
Rachel Foster
·
1 month ago
A public university ran MOSS, JPlag, and Codequiry against the same 214 Python submissions, plus 30 AI-generated files. The tools disagreed on nearly a quarter of flagged cases. One combined approach changed how instructors review code.
General
9 min
Marcus Rodriguez
·
2 months ago
A student renames every variable and converts for loops to while loops. MOSS still flags 94%. This guide builds a minimal winnowing detector in Python 3.11 so you can see exactly why code plagiarism detection algorithms survive refactoring. We then look at where the approach breaks and how AST matching fills the gap.
General
11 min
Dr. Sarah Chen
·
2 months ago
One CS department's switch from a single similarity tool to a layered detection workflow changed what they could see in student code. Peer copying, web sources, and AI-generated submissions each required different signals, and combining them revealed more than any one check alone.
General
8 min
Rachel Foster
·
2 months 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
11 min
Priya Sharma
·
3 months ago
What started as a textual diff in Unix is now a high-stakes algorithmic arms race. This article traces the thirty-year evolution of source code plagiarism detection—from simple token matching and AST comparison to fingerprinting that survives variable renaming, and finally to the fresh challenge of AI-generated code. We examine the real detection rates, the tools that led each era, and where Codequiry fits as the first hybrid platform to unify peer, web, and AI checks in a single reporting workflow.
General
11 min
Alex Petrov
·
3 months ago
A copied snippet might survive a human code review after a quick variable rename and loop inversion. A similarity checker that understands ASTs won’t be fooled. This guide walks through wiring Codequiry’s API into your Git pre‑commit workflow, step by step, so every commit is scanned for non‑original code before it hits the branch.