By industry · Education & EdTech

Learners ask AI what to study and where. We are measuring who gets named.

Students and parents ask AI to compare courses, schools and platforms. MentionX tracks who the engines recommend and which sources they use.

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example scene · buyer → AI engine
A buyer types
“”
Waiting for the question
1. Provider ARecommended
2. Provider BRecommended
3. Provider CMentioned
Your institutionNot in the answer
Example scene with placeholder names. Your scan shows the real answers.
What the research says · education

Outside evidence, before your own scan exists.

Honest gap
0
independent studies found on how AI recommends courses or schools
MentionX desk research, Oct 2026
The number that matters
How often AI picks you
We never guess it. Your first scan measures it, and it shows up here instead of the research.
Run your first scan →
These are third-party studies, many from vendors with something to sell. They are hypotheses to test, not MentionX results. Your first scan replaces them with your own numbers.
Chapter 1 · The shortlist forms

One sentence. Three or four names. Then the buyer stops looking.

Nobody scrolls ten blue links any more. The answer is the shortlist, and the shortlist is the market. In education, a name that is not in it is not in the room.

Most tools stop here and tell you whether you appeared. That is the easy question.

Share of the answer, per brand · placeholder
Provider ARecommended
Provider BRecommended
Provider CMentioned
Your institutionNot in the answer
Chapter 2 · Where the answer came from

No credible study yet. That is the opening.

We found no independent research on how AI picks schools or courses, only vendor blog posts. Our first scans will measure it, and this page will show real findings once they exist.

THEANSWER1Course guideplaceholdercourse-guide.example2Ranking siteplaceholderranking.example?Your pagerarely citedyour-school.example
Chapter 3 · The gap

Not “you are missing”. Which page, which question, which answer.

Your share of answers
Read from your scan, never estimated. Ring shown with placeholder fill.
01
Unknown: which sources shape course recommendations
Hypothesis: course guides and ranking sites. Not yet measured.
02
Unknown: how engines treat accreditation and outcomes
Hypothesis: they quote what is easy to verify. Not yet measured.
03
Unknown: gaps between engines
To be filled from scans.
Chapter 4 · What happens next

Seeing the gap is not the product. Closing it is.

Every gap becomes an ordered fix. Do them in order, rescan, and watch your name climb the shortlist.

The shortlist · example
Today
1Provider ARecommended
2Provider BRecommended
3Provider CMentioned
4Your institutionNot in the answer
1
Run a baseline scan on the prompts learners use
Start with course, school and career-outcome questions.
2
Read which sources the engines cite
Do not act on blog advice before you see your own data.
3
Fix the biggest verified gap first
Then rescan and compare.
Chapter 5 · What it is worth

A mention is not the goal. Pipeline is.

The question leadership asks is never “are we mentioned”. It is what the fix is worth. Each gap is tied to the revenue it touches, so the order of work has a number next to it.

1
In the answer
Your brand makes the shortlist.
2
Buyers act
They visit, compare and ask for a demo.
3
Pipeline
Opportunities tied to the fix.
[YOUR PIPELINE NUMBER]
4
Revenue
The number leadership signs off on.
[YOUR REVENUE NUMBER]
education FAQ

What teams in education ask first.

Why is there no statistic here?

We will not invent one. This page fills with measured findings after our scans.

Does it work for universities and platforms?

Yes. Prompts are discovered from your URL.

Can I help shape the research?

Run a scan and your category becomes part of it.

Other industries
B2B SaaS & TechnologyE-commerce & RetailFinancial Services & FintechHealthcare & ClinicsTravel & HospitalityProfessional ServicesAll industries →

Find your gap before your buyer finds your rival.

Stored engine answers before scores. No fabricated query volumes.

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