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
7 min
James Okafor
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10 hours 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 day 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
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2 days 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.
General
1 min
Marcus Rodriguez
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3 days ago
Greedy string tiling is the matching algorithm behind JPlag and several modern code similarity engines. This report explains how it tokenizes source, extracts maximal contiguous matches, and why it still needs help from AST and fingerprinting to catch refactored plagiarism.
General
8 min
James Okafor
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4 days ago
A head-to-head evaluation of AI code detection across ChatGPT-4o, GitHub Copilot, Claude 3.5 Sonnet, and Gemini 1.5 Pro. One pattern kept surfacing: text-only detectors miss refactored LLM code, while structural and multi-signal checks hold up.
General
7 min
Rachel Foster
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5 days ago
An engineering leader audit of 1,284 remote take-home coding submissions found 31.2% flagged as likely AI-generated at high confidence. Manual review confirmed 279 of 401 high-confidence flags, with a 4.5% false positive rate among high-confidence flags. Here is what the data showed and how hiring managers should handle AI detection scores.
General
11 min
Alex Petrov
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6 days ago
A 400-student Python course adopted Codequiry to check submissions for plagiarism and AI generation. The instructor found that 18% of assignments contained copy-pasted code from Chegg, and 12% showed strong signs of LLM authorship — a pattern that peer-only checks had missed entirely.
General
11 min
Dr. Sarah Chen
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6 days ago
Over 1,200 student submissions from a large public university’s introductory Python course were analyzed with Codequiry’s similarity engine and its AI code detector. The results show how traditional plagiarism tools miss a growing fraction of unauthorized work—and why layering AI detection changes what instructors actually see.
General
7 min
Dr. Sarah Chen
·
1 week ago
We cut similarity rates from 43% to 7% in a Data Structures course not by policing harder but by rewriting the assignments themselves. Here's what worked, what broke, and where detection tools like Codequiry still earn their keep.
General
11 min
Alex Petrov
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1 week ago
When Riverside University’s CS department ran an AI detector across 300 CS2 assignments alongside MOSS, they discovered a new layer of academic integrity challenges. The case study reveals what they found, how they calibrated thresholds, and why combining AI detection with source-code fingerprinting changed their grading workflow.
General
11 min
James Okafor
·
1 week ago
When students rename variables, extract methods, and reorder statements to hide copied code, which detection algorithm actually holds up? A controlled experiment pits winnowing, token-based matching, and AST structural hashing against a ladder of refactoring transformations — and reveals why single-technique checkers miss the cases that academic-integrity panels care about most.
General
11 min
Marcus Rodriguez
·
1 week ago
Most plagiarism detectors rely on token streams, which break down as soon as students rename variables, reorder statements, or extract methods. We map the precise failure points, walk through AST-based recovery techniques, and show how fingerprinting fills the gaps that tree comparators leave behind. A must-bookmark deep‑dive for any CS educator or engineering lead who has watched suspect code sail right through a token‑only scanner.