The Hidden Pattern That Catches AI-Generated Code

You've read the submission. The logic is correct. The variable names are fine. It passes all the test cases. But something feels profoundly off. It's like reading an essay written by a competent but emotionally vacant alien. Your gut says it's AI-generated, but MOSS shows no similarity to anything in your repository. What now?

The old paradigm of plagiarism detection—finding copied blocks—is obsolete for this new problem. AI-generated code is, by definition, novel. The detection signal has shifted from similarity to stylometric anomaly. You're not looking for copying; you're looking for the machine's unconscious tics.

"The most reliable indicator of AI-generated code isn't a single error, but a pervasive, statistically improbable uniformity." – Dr. Elena Rodriguez, CS Department Chair, Stanford University

The Five Fingerprints of Machine-Written Code

Based on analysis of thousands of submissions flagged by advanced detectors, these patterns emerge consistently across models like ChatGPT, Copilot, and Claude. Train yourself to spot them.

1. The Comment Anomaly

AI models are trained to be helpful and "explain" their code, but they do it in a bizarrely formulaic way.

  • Over-commenting trivial operations: You'll see a comment for every single line, even for i++ in a loop.
  • Narrative-style comments that don't aid understanding: They read like a textbook description, not a programmer's note.
  • Perfectly grammatical, complete sentences: Human comments are fragments, inside jokes, or TODOs. AI comments are sterile prose.
# Calculate the sum of the list by iterating through each element
total_sum = 0  # Initialize the total sum to zero
for number in number_list:  # Iterate over each number in the provided list
    total_sum = total_sum + number  # Add the current number to the running total
# The total sum has now been computed and stored in the variable total_sum
return total_sum  # Return the computed total sum to the caller

No human who understands a for loop writes that. Ever.

2. Syntactic Over-Optimization and Consistency

Humans are messy. We mix snake_case and camelCase. We forget const. AI models often pick one style and apply it with robotic precision across the entire file, even in contexts where it's unnatural.

  • Unwavering adherence to a single style guide (e.g., always using let in JavaScript, never var or const, even when inappropriate).
  • Perfect, yet contextually odd, error handling: Every function has a try-catch. Every input is validated. In a simple 50-line homework script, this is a huge red flag.
  • Library import overkill: Using numpy for a task solvable with a list comprehension, or importing math just to use math.sqrt instead of **0.5.

3. The "Average Solution" Problem

LLMs generate the statistical mean of all solutions they've seen. The result is code that avoids cleverness, brevity, and idiomatic shortcuts—the very things good students pride themselves on.

Look for the absence of:

  • List comprehensions in Python (AI will write a verbose for-loop).
  • Ternary operators or arrow functions in JavaScript.
  • Built-in functions like map, filter, or reduce.
  • Any solution that makes you think, "Huh, that's a neat trick."

Compare these two solutions to "find even numbers":

# Human (often)
evens = [x for x in numbers if x % 2 == 0]

# AI (frequently)
even_numbers = []
for index in range(len(numbers)):
    current_number = numbers[index]
    if current_number % 2 == 0:
        even_numbers.append(current_number)

4. Structural Repetition and Token-Level Predictability

This is where automated detectors like Codequiry's AI analysis engine excel. They perform a statistical analysis on the token stream of the code (keywords, operators, identifiers).

  • Lower entropy in token choice: Human code has more surprising, less predictable sequences. AI code is more "linear" in its statistical model.
  • Repetitive block structures: If every function has the exact same skeleton (docstring, try-catch, same return pattern), it's suspect.
  • This is a quantitative measure, not a visual one. It's the digital equivalent of measuring the "randomness" of the writing.

5. The Context Disconnect

The code solves the abstract problem but ignores the specific assignment context.

  • Your assignment sheet says "Implement function `foo`". The submission has `foo`, but also has a full `main()` method with argument parsing and a pretty print function you never asked for.
  • The code uses concepts not yet covered in class (e.g., recursion in Week 2, decorators in an intro course).
  • Variable names are generic (data, result, value) instead of context-specific names a student would naturally choose (student_grades, temp_fahrenheit).

Your Actionable Investigation Workflow

When your gut tingles, don't just stare. Systematically investigate.

  1. Run a traditional similarity check first. Rule out old-fashioned copying. A clean MOSS report now means the problem is more subtle.
  2. Conduct a manual scan for the five fingerprints above. Focus on comments and structure. Does it feel like a student's work?
  3. Use a dedicated AI-detection tool. Manual spotting is for suspicion. You need quantitative evidence for a confrontation. Platforms like Codequiry don't just check similarity; they run the code through a classifier trained on millions of human and AI-written examples, looking for those statistical fingerprints.
  4. Compare against the student's prior work. This is the most powerful technique. A student's coding style is like a fingerprint. Does this submission radically diverge in commenting style, error handling, or structure from their Week 1 lab? If so, you have your talking point.
  5. Prepare for the conversation. Your evidence shouldn't be "the tool said so." It should be: "Your solution uses a try-catch block on every function, which you've never done before, and you've added narrative comments in perfect English, which contrasts sharply with your previous submissions. Can you walk me through your thought process for this specific block?"

The goal isn't to become a paranoid enforcer. It's to uphold a standard of authentic learning. By understanding the signature of the machine, you can better appreciate—and assess—the work of the human mind.

The bottom line: AI-generated code is a new species of academic dishonesty. Detecting it requires new tools and a trained eye for statistical sterility, not just duplicated lines.