AI-Generated Code Detection: The New Frontier in Academic Integrity
As AI coding assistants become ubiquitous, learn how institutions are adapting to detect AI-generated code and maintain educational standards.
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.
Stay ahead with expert analysis and practical guides
A practical walkthrough for CS instructors who want to wire code similarity checks directly into their grading workflow. Covers tooling choices, LMS integration, and how to layer in web-source and AI-generated code detection for a complete academic integrity pipeline.
By aggregating similarity scores across 4,200 student Python submissions over three semesters, we uncovered distinct copy-paste behaviors tied to assignment type, submission deadline, and language features. This practical guide walks through the exact process of running a large-scale code reuse audit using Codequiry’s output and Python data analysis, then shows how to turn those numbers into actionable course design decisions.
K-gram fingerprinting is the backbone of modern code plagiarism detection. This step-by-step guide walks through tokenization, k-gram generation, hashing, winnowing, and comparison — the exact pipeline used by MOSS and Codequiry. Includes Python code examples, algorithmic tradeoffs, and real-world scaling numbers.
Setting up automated code plagiarism and similarity checks inside a CI pipeline cuts manual grading time and catches copying that individual reviewers miss. This practical guide walks through the architecture, tooling choices, and honest tradeoffs of running MOSS, JPlag, or Codequiry’s API on every lab push.
When CareerDevs Academy scaled from 30 to 200 students per cohort, their manual code review process couldn't keep up with plagiarism and improper code reuse. Here's how they built a tiered originality pipeline combining static analysis, similarity detection, and educational intervention — and what other programs can learn from their approach.
A retrospective on automatic grading in computer science education—from shell scripts comparing output strings to modern platforms combining unit tests, static analysis, and code similarity detection. What we gained, what we lost, and why integrity pipelines matter more than ever.
When contractors deliver source code, verifying originality and license compliance is critical. This guide walks through building an automated provenance pipeline that checks for code similarity, license violations, and proper attribution before accepting deliverables into your codebase.
Not all code similarity is plagiarism, and not all plagiarism is caught by string matching. This article breaks down the three major detection techniques—AST comparison, token-based analysis, and algorithmic fingerprinting—and explains what each one actually reveals about student submissions.
A step-by-step guide to building a source code similarity detection pipeline from scratch. Covers tokenization, AST comparison, Winnowing fingerprinting, and heuristic scoring. Includes working Python code and configuration strategies used by universities and enterprises.
Pair programming and plagiarism can look identical to automated detectors. This article explains the technical signals that distinguish collaborative work from unauthorized code sharing, and how educators can design assignments and detection workflows that respect both academic integrity and modern development practices.
Attribution comments are a simple but powerful tool for teaching code integrity in collaborative programming projects. This article explains how to implement them effectively, what to include, and how they transform group work from a plagiarism minefield into a learning opportunity.
The history of code similarity detection is a story of escalating arms races. What started with professors reading printouts has evolved through Unix diffs, token-based fingerprinting, and into modern abstract syntax tree analysis. This retrospective traces the key technical shifts that shaped how we detect code plagiarism in programming courses today.