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
12 min
David Kim
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16 hours ago
A deep-dive comparison of MOSS, JPlag, Dolos, and hybrid detectors on deliberately obfuscated student Java code. Token-based algorithms catch most refactoring, but AI-generated obfuscation is changing the game — and combining similarity checks with AI detection is the only reliable way to stay ahead.
General
12 min
Dr. Sarah Chen
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3 days ago
When Midwestern State University’s CS department discovered that MOSS alone missed nearly a third of suspicious submissions—many generated by ChatGPT—they implemented a two-stage detection pipeline. This is what they learned about running plagiarism checks first, then AI detection, and why the combination caught more than either tool alone.
General
12 min
Priya Sharma
·
4 days ago
After years of relying on MOSS to spot peer-to-peer code plagiarism, the CS department at Purdue Northwest saw a new problem: students submitting AI-generated code that looked original to traditional checkers. By adding an AI code detector and web‑source matching to their pipeline, they flagged 34% more AI‑written assignments in a single semester — giving instructors the concrete evidence they needed to uphold academic integrity.
General
10 min
Priya Sharma
·
6 days ago
We ran both MOSS and an AI-generated code detector on all submissions across a semester of Data Structures. What we learned about catching ChatGPT-authored code — and where MOSS still shines — changed how our department thinks about academic integrity tooling.
General
11 min
Priya Sharma
·
1 week ago
GitHub Classroom automates assignment distribution, but grading still exposes copied code and AI-authored submissions late in the semester. By wiring Codequiry’s dual-purpose API into a GitHub Actions pipeline, instructors can flag both traditional plagiarism and LLM-generated solutions the moment a student pushes. This walkthrough shows the exact YAML and shell scripts you need, plus how to interpret the structured similarity and AI probability reports that come back.
General
7 min
Rachel Foster
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1 week ago
A two-semester experiment at a mid-sized CS department put Codequiry’s AI code detector to the test across 1,200 student submissions. The tool achieved 87% overall accuracy in identifying AI-generated Python code, with a manageable false-positive rate and strong recall. The study surfaced distinct patterns in where detection excels—and where manual judgment remains essential.
General
8 min
James Okafor
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1 week ago
From the early days of the Unix diff command to the rise of MOSS, JPlag, and AI-powered detectors, code similarity detection has undergone a quiet revolution. This retrospective traces the key technical milestones—tokenization, ASTs, fingerprinting, web-source matching, and the new frontier of AI-generated code—showing how each layer made plagiarism harder to hide. See how modern platforms like Codequiry unify these techniques into a single pipeline.
General
11 min
James Okafor
·
2 weeks ago
When a mid-sized software firm started scaling external contractor contributions, they needed a way to verify code originality at scale. By integrating Codequiry’s API into their CI/CD pipeline, they built an automated gate that flagged plagiarized open-source snippets and AI-generated code before it ever hit the main branch. Here’s a look at their process, the false-positive tradeoffs, and the integration that saved their legal review hours.
General
12 min
Marcus Rodriguez
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2 weeks ago
When a single CS1 assignment yields 300+ submissions, manual plagiarism checking simply doesn't scale. This hands-on guide walks through connecting Canvas to Codequiry's API, running similarity and AI-detection scans with a handful of Python scripts, and posting flagged results directly back into the SpeedGrader — so you catch the cases that matter without drowning in paperwork.
General
11 min
Dr. Sarah Chen
·
2 weeks ago
Copy-paste code and AI-generated solutions are flooding CS courses. Smart assignment design—parameterized prompts, incremental checkpoints, and open-ended specs—can make copying so cumbersome that students default to doing their own work. This deep dive builds a concrete, reusable playbook for instructors.
General
12 min
Marcus Rodriguez
·
2 weeks ago
A single run of an AI detector on a suspicious student submission is not enough. CS professors need a systematic triage protocol that stacks similarity analysis, AI code detection, web-source fingerprinting, and manual review into a defensible pipeline. This article outlines a concrete workflow you can implement this semester.
General
13 min
Priya Sharma
·
2 weeks ago
AI large language models can now generate passable code for many introductory CS assignments, leaving instructors scrambling. A systematic scanning framework—combining AI detection, plagiarism analysis, and human review gates—can reliably identify AI-written submissions while respecting due process. Here’s how to build one.