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
15 min
Dr. Sarah Chen
·
12 hours ago
Running one scan and hoping it covers everything is how courses end up with both missed cases and false accusations. This walkthrough separates peer similarity, web provenance, and machine generation into three pipelines, then shows how to merge them into a ranked queue a TA can work through in an afternoon.
General
12 min
Marcus Rodriguez
·
1 day ago
I audited 1,847 programming submissions across three institutions over two terms. 3.3% were translations from another language, and automated similarity scoring caught exactly zero of them. Here is the method, the numbers, the tool comparison, and what actually surfaced the cases.
General
10 min
Priya Sharma
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2 days ago
A lead TA at a mid-sized CS department gets 412 similarity pairs back from a single algorithms assignment and has six days to decide which ones matter. This is the triage method she uses: score stacking, cohort-normalized thresholds, and a written rubric that has survived two grade appeals.
General
13 min
Alex Petrov
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3 days ago
We ran 412 bootcamp take-home submissions through four independent detection signals and followed 63 flagged candidates to a live code interview. This is what we found about which signals actually predict AI-generated code, which ones just predict "this person read a tutorial," and how to build an audit you can defend when a candidate pushes back.
General
10 min
Marcus Rodriguez
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4 days ago
Meridian, a 40-person benefits SaaS, had 340,000 lines of contracted code and no way to answer question 34 on a diligence questionnaire. Here's the five-gate provenance audit that followed, what the tools actually found, and the clause in the MSA that would have made all of it cheap.
General
10 min
Alex Petrov
·
5 days ago
I ran 4,000 bootcamp submissions through web-source matching and 22% matched something public. Most of those matches were harmless. Here's what a real Stack Overflow diff looks like, why GitHub matches almost always come with a receipt, and how I stopped flagging half my class with false positives.
General
12 min
Alex Petrov
·
6 days 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
17 min
Rachel Foster
·
1 week ago
Most CS departments teach students to cite what they copy but never to read the license attached to it. This reported guide walks through the licenses that actually show up in student repositories, the four failure modes that recur in grading, and a provenance rubric that separates academic honesty from legal obligation.
General
10 min
James Okafor
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1 week ago
Take 30 hand-written Java solutions, push them through five levels of mechanical refactoring, and you get a controlled ladder for asking a simple question: how much does each similarity engine actually see? The answers differ by nearly a factor of four, and the differences map cleanly onto how we should be designing assignments.
General
11 min
James Okafor
·
1 week ago
Renaming variables and restructuring control flow used to be the cheat code for copying a classmate's submission. It hasn't been for years, and the techniques that closed that door are worth understanding before LLM rewrites move the goalposts again. A practical look at what each family of code similarity detection actually compares, and where all of them still fail.
General
12 min
David Kim
·
1 week 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
9 min
Emily Watson
·
1 week ago
When a whole class prompts the same model, peer similarity scores climb even though nobody copied anybody. Here is how to read an AI detection score, a clustering pattern, and a genuine copy pair as three different things, and how I triage 200 submissions in under two hours without accusing the wrong students.