Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
11 min
Alex Petrov
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3 weeks 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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3 weeks 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
·
3 weeks 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
·
3 weeks 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
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4 weeks 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
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4 weeks 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.
General
11 min
Priya Sharma
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4 weeks 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
10 min
Marcus Rodriguez
·
1 month ago
Code similarity tools all promise to catch cheaters, but their underlying algorithms differ dramatically. We ran a large-scale experiment—15,000 real CS1 Java submissions, 500 manually verified suspicious pairs—to compare winnowing (MOSS), AST hashing (JPlag-style), and fingerprinting side by side. The results expose which techniques survive renaming, refactoring, and template reuse, and why a layered approach matters for low false‑positive rates in production academic workflows.
General
14 min
Priya Sharma
·
1 month ago
When State University of Plains' CS department first faced GitHub Copilot-generated homework in 2022, their decades-old MOSS pipeline was useless. This retrospective traces their journey from manual suspicion to a layered detection stack that caught 31% of submissions as AI-generated last semester — and the hard lessons learned about false positives along the way.
General
10 min
Rachel Foster
·
1 month ago
A single function with renamed variables, reordered statements, and changed whitespace can still look structurally identical under the hood. This step-by-step guide builds a minimal AST clone detector in Python, explains where it breaks, and shows how production tools like Codequiry stack structural, token‑level, and web‑origin checks to catch the copying that student‑grade normalizers miss.
General
11 min
Alex Petrov
·
1 month 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.
General
7 min
Rachel Foster
·
1 month ago
When 800 students enroll in an introductory Python course, detecting AI-generated code by hand is impossible. UMass Amherst integrated an AI code detector trained on student-level patterns alongside traditional similarity checks—and uncovered a 14% AI flag rate, a 2% false positive rate, and a sustainable workflow that kept TAs focused on teaching rather than policing.