You’ve run the submissions through the detector. The similarity report came back clean, or the AI confidence score was a low, reassuring 15%. You’re ready to move on. This is the exact moment you’ve been fooled.
Modern academic dishonesty—fueled by LLMs like ChatGPT and Claude, and sophisticated plagiarism networks—no longer lives in verbatim copying. It lives in the stylistic uncanny valley of code. The tools that catch direct duplication are blind to the architectural and syntactical fingerprints left behind by AI or by a student trying to obfuscate stolen work. Your detection process has a gaping blind spot, and it’s filled with these patterns.
Based on analyzing tens of thousands of submissions across universities from Stanford to community colleges, and reviewing internal code at tech firms, here are the eight code patterns your current system is almost certainly missing.
1. The Over-Engineered Hello World
AI models are trained on vast corpora of production and tutorial code. They often default to robust, generalized solutions even for trivial problems. A student writing a simple function to read a file will use `open()`. An LLM, aiming for "completeness," will frequently introduce unnecessary layers: exception handling with specific error types, context managers where a simple call suffices, or verbose logging setups.
This creates a jarring mismatch between the complexity of the problem and the complexity of the solution. It’s the coding equivalent of using a satellite navigation system to walk to your neighbor’s house.
"When a beginner's assignment contains a fully-fledged factory pattern or a custom error hierarchy for a task that needs three lines of code, the first question shouldn't be 'Is this student a prodigy?' It should be 'What generated this?'" – Dr. Elena Rodriguez, CS Department Chair, UC Irvine.
Example: Reading a config file.
Human-written (novice):
import json
with open('config.json') as f:
config = json.load(f)
print(config['user'])
AI-generated (common pattern):
import json
import logging
import sys
from typing import Any, Dict
CONFIG_PATH = 'config.json'
logger = logging.getLogger(__name__)
def load_configuration(file_path: str) -> Dict[str, Any]:
"""Loads and validates configuration from a JSON file."""
try:
with open(file_path, 'r', encoding='utf-8') as config_file:
config_data = json.load(config_file)
logger.info("Configuration loaded successfully from %s", file_path)
return config_data
except FileNotFoundError:
logger.error("Configuration file not found at %s", file_path)
sys.exit(1)
except json.JSONDecodeError as e:
logger.error("Invalid JSON in configuration file: %s", e)
sys.exit(1)
if __name__ == "__main__":
config = load_configuration(CONFIG_PATH)
print(config.get('user', 'default_user'))
The second snippet isn't wrong; it's inappropriately correct for a first-year programming assignment. Detectors looking for similar code blocks won't flag this. You need to analyze the complexity delta between the prompt and the submission.
2. The Comment-to-Code Ratio Anomaly
Students under time pressure comment sparingly, if at all. When they plagiarize from online sources like GitHub or Stack Overflow, they often copy the comments verbatim as part of the block. AI models, conversely, are notoriously over-zealous commenters. They generate docstrings for every function, explain obvious logic, and use a very specific, formal tone.
Look for submissions where the commenting style is internally consistent but externally anomalous. Does a student who submitted barely-commented code for weeks 1-5 suddenly deliver a perfectly documented Java class with param/return tags? Do the comments use phrasing or terminology not covered in your course? This is a strong signal of borrowed or generated code.
Example: A simple Python function.
Typical student comment:
# checks if n is prime
def is_prime(n):
if n < 2:
return False
for i in range(2, int(n**0.5)+1):
if n % i == 0:
return False
return True
AI-typical comment style:
def is_prime(number: int) -> bool:
"""
Determines whether a given integer is a prime number.
A prime number is a natural number greater than 1 that has no positive
divisors other than 1 and itself. This function implements a basic
trial division algorithm for efficiency.
Args:
number (int): The integer to be checked for primality.
Returns:
bool: True if the number is prime, False otherwise.
Example:
>>> is_prime(7)
True
>>> is_prime(10)
False
"""
if number < 2:
return False
for divisor in range(2, int(number ** 0.5) + 1):
if number % divisor == 0:
return False
return True
The logic is identical. A structural similarity tool like MOSS would see them as very different due to the comment text. A smart detector needs to strip comments and analyze the underlying logic, while also flagging the sudden presence of professional-grade documentation as an integrity metric.
3. The Import Statement Graveyard
This is a classic in obfuscated plagiarism and a common AI artifact. A student will copy a solution that uses specific libraries, then try to modify the core logic to make it look original. However, they often forget to remove the now-unused import statements. You find a Python file importing `numpy`, `pandas`, and `matplotlib` for a task that only requires list comprehensions.
Similarly, AI models will frequently generate import blocks that are overly broad or include libraries not strictly necessary for the solved problem, reflecting the statistical likelihood of those imports appearing together in its training data.
Practical Check: Run a simple static analysis on submissions to find unused imports. A single stray import might be a mistake. Three or more in a beginner's assignment are a red flare. In one case at the University of Michigan, this pattern, cross-referenced with a logic similarity check in Codequiry, identified 17 students who had copied from the same obscure GitHub repository.
4. The Inconsistent Formatting Fingerprint
Humans have a consistent, if sometimes messy, formatting style: their tab size, brace placement, spacing around operators. When code is assembled from multiple AI prompts or spliced together from different plagiarized sources, these formatting rules break down.
Look for a single file where:
- Indentation switches between 2 spaces and 4 spaces.
- Some functions use `snake_case` and others use `camelCase` for local variables.
- String formatting alternates between f-strings, `.format()`, and `%` operators without reason.
This is different from mere sloppiness. It's a stylistic polyglot indicating multiple origins. Advanced detection platforms don't just compare code trees; they build a stylistic profile for each student and flag drastic deviations within a single artifact.
5. The Hallucinated API or Parameter
LLMs are prone to "hallucination"—generating plausible but non-existent information. In code, this manifests as the use of a perfectly logical-sounding library function or method parameter that doesn't exist in the specified version of the language or framework.
A student working honestly with the official Java 17 documentation is unlikely to invent a method. An AI model, synthesizing from various sources, might.
Example: An assignment for a web scraping class using Python's `requests` and `BeautifulSoup`.
AI might generate:
response = requests.get(url, verify_ssl=True, timeout=30)
soup = BeautifulSoup(response.content, 'html.parser')
# The AI hallucinates a 'prettify_to_file' method
soup.find('div', class_='content').prettify_to_file('output.html')
`prettify_to_file` is a logical extension of the `prettify()` method, but it doesn't exist. A student who genuinely wrote this would have a runtime error. The fact that it's present in a submitted, presumably "tested" file suggests it was copied without execution from a generative source.
6. The Algorithmic Mismatch
You teach a specific algorithm—say, Dijkstra's algorithm for shortest paths with an adjacency matrix. A student submits a working solution. However, upon inspection, the core loop uses a priority queue (heap) implementation optimized for sparse graphs, and the data structure is an adjacency list.
This is a deep, logical disconnect. The student demonstrated knowledge of an efficient shortest-path algorithm, but not the one you taught. This often occurs when a student finds a solution online or gets AI to "solve this graph problem." The detector must understand the semantic intent of the assignment, not just functional correctness. Is the submitted algorithm the one that was instructed?
7. The Variable Name Echo Chamber
AI-generated code has a distinctive lexicon for variable names. It heavily favors descriptive, verbose, and often compound names (`input_data_stream`, `calculation_result_final`). It avoids short, arbitrary names (`x`, `tmp`, `foo`) that humans use when sketching. Conversely, plagiarized code often contains "echoes" of its original context: variable names referencing domains, projects, or authors unrelated to your assignment.
Search for variable names that are semantically odd for the problem. A task to calculate student grades shouldn't have variables named `client_payload`, `transaction_id`, or `aws_bucket_name`. These are ghosts of the code's original source.
8. The Structural Template Remnant
This is the most technical and damning pattern. Many code generation tools and tutorial sites use boilerplate templates. AI models trained on this data reproduce the template structure, including placeholder comments or specific setup/teardown patterns.
Classic Example: The `if __name__ == "__main__":` guard is standard. But look at what's inside. Does the main block include a `# TODO: Add command-line argument parsing` comment that’s irrelevant to the assignment? Does a C++ program include `// For more details, see my blog at...`? These are untouched remnants of a copied block.
In one enterprise audit, this pattern uncovered that a contractor had submitted, as "original work," an entire module that was a slightly modified version of a popular open-source library, complete with the original author's configuration template and build instructions in the comments.
Moving Beyond Simple Similarity
Catching these patterns requires moving beyond line-by-line comparison. It demands a multi-layered analysis:
- Stylometric Analysis: Building a writing-style profile for the coder.
- Semantic Code Comparison: Comparing logic and algorithm choice, not just text.
- Anomaly Detection: Flagging inconsistencies in formatting, imports, and complexity within a submission history.
- Context-Aware Checking: Understanding the assignment's constraints and required techniques.
Tools like MOSS, which revolutionized plagiarism detection decades ago, primarily tackle pattern #1 (direct similarity). They are largely blind to patterns 2 through 8. Modern platforms have evolved to fill this gap. For instance, Codequiry's analysis engine doesn't just run a similarity score; it generates a integrity profile that highlights these stylistic and structural anomalies, presenting them alongside traditional similarity matches.
The goal isn't to create an inescapable police state. It's to restore the fundamental contract of education and professional development. When a student or developer submits code, they are making a statement: "This represents my understanding." Our job is to ensure that statement is true. By learning to spot these eight hidden patterns, you're not just catching cheaters; you're identifying who needs genuine help and ensuring your evaluations actually measure what you intend to teach.
Your next step? Run your last batch of submissions through this mental checklist. Then, find a tool that automates it.