AI Detection reports are embedded workbooks. Their tabs, filters, and summary metrics can vary by
workspace and report version. Follow the labels and metric descriptions in your report; the
examples below may not all appear.
Open a course report
- From K16
- From Canvas
Open AI Detection Dashboard, open the course summary provided by your workbook, and select
the course you want to review.
The Canvas entry appears only where your institution has installed and enabled the LTI. Students
do not receive reporting access through this faculty workflow.
Read the submission list
A course report can include:- submission title
- submission type, such as assignment, discussion, or quiz
- AI classification
- AI probability or confidence
- student’s name
- submission date
- read status
Review a course consistently
1
Confirm the course
Check the course title, code, and term before you review results.
2
Confirm the visible population
If your report offers Display All Submissions, check whether it is on. Note any other active
filters.
3
Sort your review queue
Use classification, date, submission type, or read status to organize work. Do not use the
probability alone as a disciplinary cutoff.
4
Open the submission
Select a row to inspect the overall result and sentence-level highlighting.
5
Record your review state
Use the available read status and your institution’s case process so another reviewer can
understand what remains.
Avoid common comparison errors
- Compare submissions from the same course and assignment context when possible.
- Do not compare percentages across different filters without confirming both populations.
- Treat short, formulaic, heavily quoted, translated, or accessibility-assisted writing with extra care.
- Check whether the assignment allowed generative tools and how students were asked to disclose them.
- Keep the original submission and other evidence central to the review.