01
Review
Start with stored ratings, courses, class formats, reported grades, and AI responses.
How the analysis works
How Luni uses AI to make professor reviews easier to understand.
Luni finds useful patterns in student reviews while keeping the courses, class formats, sample size, and confidence in view.
ReviewsPatternsContextDecision
How it works
AI finds patterns without flattening the details.
Each step keeps the original context close, so you can understand why an insight matters for your class choice.
02
Compare
Look for patterns in teaching, workload, grading, attendance, and exams.
03
Keep context
Analyze online and in-person feedback separately when the experience differs.
04
Explain
Show the takeaway, the supporting sample, and anything the data cannot confirm.
How evidence is handled
A repeated pattern, a format difference, and a missing detail are not the same.
The page shows each one differently so you can tell what is supported and what still needs checking.
Repeated pattern
Students mention it again and again
Recurring themes from the analyzed reviews are highlighted in the summary.
Different by format
Online and in-person reviews stay separate
When experiences differ by class format, each format keeps its own sample and summary.
Not enough information
Unknown stays unknown
If the reviews do not confirm a policy or detail, Luni does not fill it in.
Understanding confidence
Confidence shows how much evidence supports an insight.
More evidence
Show the sample size
See how many reviews support the AI summary.
Different formats
Compare each format separately
Online and in-person review counts stay with their own summaries.
Not enough evidence
Say what is missing
Unconfirmed details stay visible instead of becoming assumptions.
What you can check
The evidence stays close to the conclusion.
Student reviews
Read the student language that supports the summary.
Course context
See the courses and class formats connected to each pattern.
Reported outcomes
Treat grades and ratings as student-reported context, not promises.
Evidence strength
Check the sample size and confidence before deciding.
Common questions
What to know before using Luni professor insights.
What information appears on a professor page?
You can see public ratings, courses, online and in-person context, reported grades when available, and stored AI insights. The page also shows how many reviews were analyzed and which details are missing.
How does Luni use AI to understand student reviews?
Luni uses stored AI analysis to surface repeated patterns in teaching, workload, grading, attendance, and exams. Online and in-person reviews stay separate when the experience differs by format.
What does the confidence level tell me?
Confidence describes the strength and size of the available evidence. It helps you judge how much support an insight has, but it does not predict your grade, course policy, or class experience.
Can Luni predict my grade or class experience?
No. Ratings, reported grades, and AI insights describe past public records. Luni does not guess unverified policies or promise what will happen in a future class.