If your code is flagged as AI-generated and you wrote it yourself, treat the result as a claim to review—not proof of authorship.
This guide explains how to preserve evidence, raise the issue clearly, and distinguish AI-authorship classification from code similarity or plagiarism checks.
What does an AI flag on code actually mean?
An AI detector estimates whether submitted material resembles patterns associated with machine-generated content.
A score or label is an output of that system, not a record of who wrote the code.
It cannot, by itself, establish that a person used AI, copied a program, or violated a rule.
Code also presents a special challenge for authorship classification.
Programming languages have strict syntax, conventional structures, repeated keywords, and common ways to solve standard problems.
Short snippets may contain little distinctive prose or stylistic variation for a detector to assess.
Similar-looking code can result from shared requirements, standard library usage, tutorials, or conventional implementations.
That is separate from code similarity analysis.
Similarity tools compare a submission with other code or known sources and report overlap.
A similarity result may prompt review, but overlap alone does not establish plagiarism: common boilerplate, required starter code, and standard algorithms can produce legitimate matches.
Conversely, an AI-authorship score does not show that code matches a copied source.
What should you do first?
Preserve the materials that show how the work developed before editing or resubmitting anything.
Keep the original files and gather records from your normal workflow.
Useful evidence may include:
- Version-control history, such as Git commits, branches, and diffs.
- Editor or cloud revision history, where available.
- Drafts, notes, pseudocode, diagrams, and test cases.
- Build logs, terminal output, and debugging records that you already have.
- Assignment instructions, starter files, and any permitted reference code.
Do not recreate timestamps, alter logs, or make claims about evidence you cannot support.
A clear, ordinary development record is more useful than a large collection of unrelated screenshots.
If you need to make changes, save a copy first and note what changed and when.
How can you ask for a fair review?
Follow the applicable course, workplace, or platform process.
Policies vary, and the right contact may be an instructor, supervisor, academic-integrity office, or review team.
Ask what material was flagged, which tool or process was used, and how you can respond.
Keep your message calm, specific, and focused on the work.
You can explain your process by pointing to concrete evidence: when you planned the solution, how you tested it, what errors you corrected, and why you chose particular structures.
If the code contains common patterns or required starter material, identify those parts.
Offer to walk through the relevant functions or explain how a test case moves through the program.
A concise request could say: “I wrote this submission myself and would like to understand the basis for the AI flag.
I can provide my version history and explain the implementation.
Please let me know what evidence was considered and the process for requesting a review.” Adapt it to your situation and avoid overstating what a detector can establish.
How should you explain your coding process?
Prepare to discuss the substance of your program rather than trying to guess what a detector expects.
Be ready to describe the problem, the approach you chose, important functions or classes, and how you tested edge cases.
If you used documentation, examples, autocomplete, or AI assistance, describe that use accurately and check it against the rules that applied to the assignment or project.
When appropriate, show how your code changed over time.
A sequence of small revisions, tests, and fixes can help a reviewer understand your work.
But no single artifact guarantees a particular outcome; reviewers may consider the context, the tool’s limitations, the applicable policy, and other evidence.
What should you avoid doing?
- Do not delete or rewrite history to make the work appear more human.
- Do not rely on paraphrasing or adding deliberate mistakes to change a detector score.
- Do not treat a score from another detector as definitive proof either.
- Do not accuse a reviewer of misconduct based only on a flag.
- Do not assume that a similarity percentage and an AI score measure the same thing.
These tactics can obscure the facts and distract from the evidence a reviewer needs.
A better response is to preserve the original work, explain your process honestly, and ask for the specific review procedure.
Does Turnitin detect AI-generated code?
Turnitin distinguishes its AI writing model from source-code similarity checking.
The company says its AI writing model does not reliably detect code, so its writing report should not be treated as a source-code authorship test.
A similarity tool, where used, addresses overlap with other material rather than proving whether AI wrote a program.
Check the tool’s documentation and the policy that governs your case, because institutions and organizations may use different systems and procedures.
Where does a source-code detector fit?
Codespy is an external source-code detector.
If you consider any detector, including Codespy, treat its output as one possible input to review, not an independent verdict about authorship or misconduct.
This article does not report a test of Codespy or endorse its results.
Before relying on a product, review its stated purpose, supported languages, data-handling terms, and limitations; do not assume features or accuracy that have not been verified.
If you are the person reviewing a flagged submission, give the author a meaningful opportunity to explain the work and provide context.
Consider whether a score reflects authorship classification or source overlap, whether shared starter code affected the result, and what the relevant policy requires.
A careful review uses the tool as a signal to investigate, not as a substitute for evidence and judgment.