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Use AI Detection to focus attention, then make decisions through informed human review.

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Interpretation principles

  1. Confirm the reporting population and active filters.
  2. Review the full submission, not only highlighted sentences.
  3. Treat classification and confidence as model signals.
  4. Consider assignment rules, writing context, and other evidence.
  5. Follow your institution’s documented process.
  6. Give students the review and response opportunities required by policy.
Do not create a local misconduct threshold from an AI percentage unless your institution has adopted a validated policy that explicitly requires it.

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Frequently asked questions

No. AI Only is the detector’s classification for the processed text. It can justify closer review, but it does not establish authorship or intent.
A submission confidence or probability score describes the detector’s assessment of that submission. Dashboard percentages can summarize a different population or metric. Read the displayed metric description before comparing them; a confidence score does not measure the exact share of text generated by AI.
Sentence-level results use separate evaluations from the overall submission. Individual sentences may not be marked even when the complete text receives a nonzero overall score.
Each sentence is evaluated individually, while the overall score summarizes the full submission. Many sentence signals can coexist with an overall result below 100%.
Confirm the course, term, date, and classification filters. The submission also must be available to the configured integration and eligible for processing. Record the course and submission details if it remains absent.
The reporting dataset must contain eligible processed results for that term. Clear other filters and verify that relevant course submissions exist.
Compare filters, date boundaries, course scope, and whether the course report displays all submissions. The reports may summarize different populations.
K16 documentation does not recommend that. Use the result as one input within your institution’s human review and evidence process.
Access depends on the workspace’s AI Detection configuration and reporting permissions. Ask your K16 administrator to review access when a person can or cannot open the dashboard.

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A practical review record

When your policy permits it, record:
  • course and assignment
  • submission and report date
  • filters used to locate the result
  • classification and overall score as displayed
  • relevant highlighted passages
  • contextual evidence reviewed
  • reviewer and next step
Keep that record in your institution’s approved system.

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When to ask for help

Contact your K16 administrator or support when:
  • the workbook repeatedly fails to load
  • expected processed submissions remain absent after you clear filters
  • course and institution reports cannot be reconciled
  • your access does not match your role
Include the workspace, course, submission identifier, time, active filters, and exact message. Do not send student work through an unapproved support channel.
Last modified on September 16, 2026