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AI Detection
/ Open Source Code Review - Repository AnalysisAn AI score is a signal, not a verdict, a 0 to 100% probability from stylistic patterns common to ChatGPT, Claude & Copilot. Starter code, very clean code, and short files can read high, so treat a high score as a reason to open the submission and start a conversation, not as proof.
Open a flagged submission and read the highlighted regions before acting. Those lines are what the score is resting on. If they cover only imports, licence headers or boilerplate, the score is weak evidence however high it reads.
On mature library code the false-flag rate was 0 of 30, but polished library internals are the easiest possible case, so the 14% above is the figure to plan around. Both were measured at a 75% threshold; this page flags from 70%, which is lower, so expect at least that many false flags here.
Not yet validated on real student homework, and detection is weaker against some models than others. We cannot predict in advance which human files will read high.
Submissions
| Submission | AI Score | Range | Files | Status | |
|---|---|---|---|---|---|
|
LOLWANE, MZIVUKILE(4459390)
10 months ago
|
, | , | 0 | Not analyzed | |
|
LINDA, METHEMBE SINOTHO VUMANI(4...
10 months ago
|
32%
|
21%, 44% | 4 | Safe | |
|
LEVY, ZAAKIRAH(4493694)
10 months ago
|
25%
|
20%, 43% | 8 | Safe | |
|
LUBISI, SANDILE(4429192)
10 months ago
|
31%
|
21%, 45% | 4 | Safe | |
|
LESELE, REGINALD(4395901)
10 months ago
|
28%
|
20%, 39% | 4 | Safe |