Expert insights on AI code detection and academic integrity
Stay ahead with expert analysis and practical guides
General
9 min
Priya Sharma
·
2 weeks ago
Similarity scores are ranking signals, not verdicts. I'll walk through the distributions, thresholds, and triage rules I use when reviewing code similarity reports for 400-student courses, plus where AI-generated code fits in the same queue.
General
10 min
Alex Petrov
·
2 weeks ago
As a bootcamp instructor, I've graded hundreds of take-home coding challenges. The AI-resistant ones share a pattern: they ask for process artifacts, not just final code. Here's how to design assignments that hold up.
General
8 min
Rachel Foster
·
3 weeks ago
A University of Missouri-St. Louis instructor spent two decades watching code similarity tools change shape. The gaps between each generation of detection explain why instructors now run peer, web, and AI checks side by side.
General
6 min
Alex Petrov
·
3 weeks ago
A hands-on retrospective on how code similarity detection grew from naive line diffs to tokenization, ASTs, and fingerprinting. Follow a step-by-step Python prototype and a production workflow with Codequiry to catch refactored plagiarism in CS courses.
General
8 min
Dr. Sarah Chen
·
3 weeks ago
A CS professor traces how AI-generated code detection grew out of MOSS-era token fingerprints, code stylometry, and a broken similarity assumption. The piece explains how modern detectors work, where they still stumble, and why stacked peer, web, and AI signals make the most defensible academic workflow.
General
11 min
Rachel Foster
·
3 weeks ago
A public university ran MOSS, JPlag, and Codequiry against the same 214 Python submissions, plus 30 AI-generated files. The tools disagreed on nearly a quarter of flagged cases. One combined approach changed how instructors review code.
General
11 min
David Kim
·
3 weeks ago
A DevSecOps lead's field notes on running AI-generated code detection as a merge request gate in GitLab CI, with the exact YAML, thresholds, and audit trail decisions that survived a fintech rollout.
General
11 min
Dr. Sarah Chen
·
4 weeks ago
One CS department's switch from a single similarity tool to a layered detection workflow changed what they could see in student code. Peer copying, web sources, and AI-generated submissions each required different signals, and combining them revealed more than any one check alone.
General
8 min
David Kim
·
1 month ago
Most vendor code passes unit tests and static analysis. It fails provenance checks. This is the exact merge gate I run in a fintech CI pipeline: Codequiry's plagiarism and AI scans wired into GitHub Actions, with thresholds, evidence review, and a failure policy.
General
10 min
James Okafor
·
1 month ago
A practical, code-level guide to batch-scanning Python files for web-sourced code. We walk through token normalization, fingerprinting, uploading to Codequiry, interpreting web match URLs, and stacking an AI check on flagged files. Built for CS professors auditing assignments and engineering managers verifying contractor code.
General
10 min
Dr. Sarah Chen
·
1 month ago
A CS professor ran 1,200 Java submissions through three AI code detectors. Codequiry caught 94% of known AI files and flagged only 3.5% of pre-LLM human code, while the other tools posted two to three times that false positive rate. The full numbers and methods are below.
General
9 min
Priya Sharma
·
1 month ago
Across 41,000 student submissions at a large public university, an 85% token-level similarity score between two students predicted confirmed misconduct 92% of the time in introductory courses. This guide walks through the exact calibration workflow, score distributions by language, and tiered review thresholds that worked for my assessment team.