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ai-code-detector · screening reportpage 1 / 4

Screening report · sample

assignment-04 — batch code screening

Job IDdemo_job_intro_cs
Submitted2026-09-08 14:22 UTC
Files32 files · 4,118 lines total · Python (32)
Engine versioncodebert-ft-0.1.0
RetentionSample report — not stored, nothing to delete
Note This score estimates the likelihood that code was AI-generated. It is a screening signal, not proof of AI use. False positives and false negatives occur. Always review flagged work with the author and follow your institution’s or company’s policies before taking any action.
ai-code-detector · screening report demo_job_intro_cs · screening signal, not proof — review flagged work with its author

Sheet 2 · Summary — per-file scores, sorted

summarypage 2 / 4

Per-file scores, highest first

Start your review at the top of this table. A high score means "review this first" — nothing more.

#FileLinesAI-likelihood scoreFlagged lines
1submission_22.py96
82
14–16, 19–24
2submission_07.py118
77
3–9, 41
3submission_15.py84
64
22–28
4submission_02.py143
45
51–52
5submission_30.py77
18
—
…27 more files—lower signal—
0–6 · lower signal
7–13 · mixed signal
14–100 · stronger signal
ai-code-detector · screening report demo_job_intro_cs · screening signal, not proof — review flagged work with its author

Sheet 3 · File detail — side-by-side evidence

file detail · submission_22.pypage 3 / 4

submission_22.py

82 AI-likelihood score
Flagged code
14def calculate_statistics(data):
15 # Calculate the mean
16 mean = sum(data) / len(data)
17 return mean
19def process_results(values, threshold=0.5):
20 # Filter values above the threshold
21 filtered = [v for v in values if v > threshold]
Why these segments were flagged
lines 14–16
Comment restates the code almost word for word — common in generated code, rare in student drafts.
lines 19–20
Same comment-echo pattern repeats. Two unrelated functions with identical comment style in one file.
overall
Uniform structure throughout: no dead ends, no unused variables, no stylistic drift.

Signal groups referenced: naming patterns · comment density · structural predictability · edge-case handling.

ai-code-detector · screening report demo_job_intro_cs · screening signal, not proof — review flagged work with its author

Sheet 4 · How to use this report + archival record

how to use this reportpage 4 / 4

How to use this report

This report is a record of a screening step. It is not a finding of misconduct, and it is not a basis for a grade penalty or a hiring decision on its own.

  1. Ask the author to explain the flagged snippets — people who wrote the code can usually walk through it.
  2. Ask them to extend or modify the flagged code on the spot, with a small change in requirements.
  3. Compare with the author's earlier work before drawing any conclusion.

Archival record

Report generated2026-09-08 14:22 UTC
Engine versioncodebert-ft-0.1.0
RetentionSample report — synthetic code, no personal data.
Methodology/accuracy
Note This score estimates the likelihood that code was AI-generated. It is a screening signal, not proof of AI use. False positives and false negatives occur. Always review flagged work with the author and follow your institution’s or company’s policies before taking any action.
ai-code-detector · screening report demo_job_intro_cs · screening signal, not proof — review flagged work with its author

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