Priya studies AI-assisted coding in the classroom and how detection tools hold up against ChatGPT, Copilot, Claude and Gemini output.
General
9 min
Priya Sharma
•
1 day 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
9 min
Priya Sharma
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2 weeks 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.
General
12 min
Priya Sharma
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4 weeks ago
A three-semester analysis of 4,100 CS1 submissions shows why a 70% similarity score means different things in Python, Java, C++, and JavaScript. I break down percentile thresholds, boilerplate effects, false positives, and how to pair similarity checks with AI detection.
General
10 min
Priya Sharma
•
4 weeks ago
Most plagiarism checkers only compare submissions against each other, so a Stack Overflow snippet with renamed variables sails through. We break down how web source matching uses token and AST fingerprints to catch code copied from Stack Overflow, GitHub, and tutorials, and show a Java example where refactoring did not hide the source.
General
11 min
Priya Sharma
•
1 month ago
What started as a textual diff in Unix is now a high-stakes algorithmic arms race. This article traces the thirty-year evolution of source code plagiarism detection—from simple token matching and AST comparison to fingerprinting that survives variable renaming, and finally to the fresh challenge of AI-generated code. We examine the real detection rates, the tools that led each era, and where Codequiry fits as the first hybrid platform to unify peer, web, and AI checks in a single reporting workflow.
General
14 min
Priya Sharma
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1 month ago
When State University of Plains' CS department first faced GitHub Copilot-generated homework in 2022, their decades-old MOSS pipeline was useless. This retrospective traces their journey from manual suspicion to a layered detection stack that caught 31% of submissions as AI-generated last semester — and the hard lessons learned about false positives along the way.
General
12 min
Priya Sharma
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1 month 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
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1 month 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
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1 month 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
13 min
Priya Sharma
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2 months 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.
General
7 min
Priya Sharma
•
2 months ago
When the University of Texas at Austin’s CS 312 course saw a spike in suspicious submissions that evaded their existing checks, they turned to token-based similarity analysis to catch code that had been renamed, reorganized, and logic-swapped. This case study walks through the techniques, the results, and the lessons for any institution facing refactoring-resistant plagiarism.
General
10 min
Priya Sharma
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2 months ago
Code refactoring — renaming variables, reordering statements, extracting functions — has long been the easiest way for students to disguise copied code. This article traces the thirty-year arms race between obfuscation tactics and detection techniques, from simple string comparison to modern AST and graph-based analysis that can spot similarities even after heavy transformation. Understanding this history explains why no single method is perfect and how layered approaches like Codequiry’s hybrid engine achieve the highest accuracy.