Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
12 min
Dr. Sarah Chen
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1 month ago
A mid-size CS department got 41 similarity flags from a single assignment and no written policy for what any of them meant. This is the calibration exercise they ran, the AI cluster that confused everyone, and the starter-file mistake that produced 61 false 100% matches.
General
9 min
Alex Petrov
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1 month ago
A week-by-week account of the three-signal sweep one bootcamp runs at week 7 of every cohort: peer similarity, web matching, and AI detection in one batch. Includes the ignore-list mistake that cost us two evenings, what LLM-shaped student code actually looks like, and how to turn a flag into a conversation instead of a verdict.
General
10 min
Alex Petrov
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1 month ago
387 files, three scores, one hiring round. Here's what a 12-week bootcamp learned after moving take-home review from three exhausted instructors to an automated pass that checks peer similarity, public web sources, and AI generation, including the false positives we cleared and the two assignment changes that mattered more than any detector.
General
9 min
Rachel Foster
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1 month ago
Renaming a variable, extracting a helper, and swapping a for loop for a while loop are the three moves students reach for when they want a copied submission to look original. Some similarity engines shrug them off, and some lose the match entirely. This piece walks through how token hashing, AST subtree matching, and fingerprinting each behave against a deliberately refactored Python pair, then compares what MOSS, JPlag, Dolos, and Codequiry actually reported on the same cohort.
General
12 min
David Kim
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1 month ago
An AI detection score is a signal, not a verdict. This is the four-stage triage I borrowed from a fintech incident pipeline to decide which alerts deserve a conversation, which deserve a case file, and which deserve to be closed.
General
11 min
Rachel Foster
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1 month ago
MOSS and JPlag compare Java to Java and Python to Python, which means a translated submission can score in single digits while the logic stays identical. This is how one lecturer, a TA, and a department chair handle ports, and what they've learned about the tooling that catches them.
General
15 min
Alex Petrov
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1 month ago
Two Python submissions scored 4% against each other and in the 70s against a Java gist from 2017. Cross-language plagiarism is the fastest-growing blind spot in academic integrity because translation destroys the text while preserving everything that matters. Here's what survives a translation, what detectors actually see, and where the false positives come from.
General
11 min
Marcus Rodriguez
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1 month ago
Ottenstein's 1976 detector hashed student Fortran token streams, and most of what we run today is a refined version of the same idea. This is the fifty-year arc from line diffs to winnowing, AST matching, web crawling, and statistical AI detection, plus the failure mode that still bites: a 0% similarity score that tells you nothing about authorship.
General
11 min
Marcus Rodriguez
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1 month ago
Most statements of work say "original work" and never define it, which is how GPL code ends up in your settlement service. Here is the four-question intake review I run on every contractor deliverable, with the thresholds and tooling that hold up under scrutiny.
General
10 min
David Kim
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1 month ago
Line diffing under-reports copied code and over-reports similar-looking code. Here's what token normalization and AST fingerprinting actually compare, where each one breaks, and how to wire both into a CI pipeline or an academic submission workflow.
General
12 min
Rachel Foster
·
1 month ago
Renaming variables and swapping a for loop for a while loop defeats simple text matching, but it rarely defeats structural comparison. This report walks through the obfuscation ladder, the algorithms that climb it, and the published detection rates behind the claims, including the cases where every engine still misses.
General
9 min
Priya Sharma
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1 month ago
A single AI detection score is a ranking, not a verdict, and most of the damage we've seen comes from reading it as one. This is the five-step triage we settled on after two years of grading CS 1 and CS 2 cohorts of roughly 400 submissions, including the score bands, the script, and the two cases where the whole thing fell apart.