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
James Okafor
·
2 days ago
Renaming variables and extracting helpers is the cheapest way to defeat a naive similarity checker, and the fix is not a better diff. This walks through the normalization stack that makes refactoring-resistant plagiarism detection work, from token streams to winnowing to tree edit distance, and is honest about the precision you pay for each step.
General
16 min
James Okafor
·
4 days ago
A student takes last year's Java solution and hands you Python. Same-language scanners see nothing, because the tokens barely overlap. Here is the normalization stack, the thresholds, and the review workflow we used to catch 61 translated pairs in a single term of 1,412 submissions.
General
10 min
Rachel Foster
·
5 days ago
Take-home exercises have become the most reliable place to catch AI-generated code in a hiring pipeline, but almost no team uses the three signals they already own. A staff engineer's screening routine, the code tells that survive a careful read, and the legal questions nobody has answered yet.
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
12 min
Priya Sharma
·
1 week ago
A working build guide to the similarity pipeline behind most academic code checks: tokenization, k-gram fingerprinting, winnowing, and AST shape comparison in roughly 300 lines of Python. Includes the threshold calibration table we used across six assignments and an honest account of where a homegrown checker stops being enough.
General
15 min
Marcus Rodriguez
·
1 week ago
A code similarity score is not a probability of cheating. It's a measurement of overlapping token fingerprints, and it can be high for honest reasons or low for dishonest ones. Here's what sits underneath the number, with worked Java examples and the thresholds that hold up when a student appeals.
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
Rachel Foster
·
2 weeks ago
A department ran a web-source scan on 412 intro Python submissions and got 61 hits, 23 of which were noise. This is the eight-step workflow they built afterward, from normalizing LMS exports and budgeting GitHub's code search rate limit to ranking matches by token count instead of percentage and assembling an evidence packet that survives an appeal.
General
13 min
Alex Petrov
·
3 weeks 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
·
3 weeks 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
17 min
Rachel Foster
·
4 weeks 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
15 min
Dr. Sarah Chen
·
1 month ago
Dependency scanners read manifests. They cannot see the 300 lines someone pasted into a file with the header deleted. This piece walks through what actually constitutes an open source license violation, why SBOM tooling goes blind at exactly the wrong moment, and how provenance checks catch copied code before counsel does.