Shortlists are formed in chat, not in a prospectus.
Programme fit, fees, placements, eligibility and comparisons — applicants ask an assistant first, and only a few institutions get named. CiteRank AI shows how the 8 major engines answer for your programmes and campuses, and what it takes to be included.
Vertical benches currently run 4 engines; full tracking covers all 8.
Aggregators answer the questions you should.
Course portals and ranking sites publish exactly the structured facts engines want — fees, eligibility, outcomes — while institution sites bury them in PDFs and brochures.
'Best data science masters for working professionals' returns a shortlist that rarely includes mid-tier institutions.
Engines quote outdated or invented fee figures because current ones are locked inside PDFs.
Admission criteria vary by intake and cohort, and engines flatten them into one wrong answer.
Placement and accreditation claims without a citable source are ignored or contradicted.
Visa, language and credential-recognition prompts are answered by forums rather than the institution.
Course portals take the citation for nearly every comparison prompt in the category.
Which institutions the engines name for programme prompts.
A sample of the education prompt graph replayed across engines, by programme and applicant intent.
| Category prompt | ChatGPT | Gemini | Perplexity | Claude | Answer concentration | You cited? |
|---|---|---|---|---|---|---|
| Best MBA for a mid-career switch | Portal, Institution A, Institution B | Portal, Institution A, Institution B | Portal, Institution A, Institution B | Portal, Institution A, Institution B | 3 of 30 | — |
| Affordable data science masters online | Portal, Institution C, Listicle | Portal, Institution C, Listicle | Portal, Institution C, Listicle | Portal, Institution C, Listicle | 4 of 26 | |
| Eligibility for an international intake | Forum, Institution A, Portal | Forum, Institution A, Portal | Forum, Institution A, Portal | Forum, Institution A, Portal | 5 of 22 | — |
| Which course has the best placements | Ranking site, Institution D, Portal | Ranking site, Institution D, Portal | Ranking site, Institution D, Portal | Ranking site, Institution D, Portal | 3 of 28 | — |
Illustrative modelled data — not live client results. Names anonymised.
A multi-campus institution, modelled end to end.
An institution was cited only through course portals, and two campuses never appeared. We published the facts engines needed — fees, eligibility, outcomes — as structured, dated pages per programme.
- EducationalOrganization, Course and CollegeOrUniversity schema published per programme and campus
- Fees, intakes and eligibility moved out of PDFs into structured, dated pages
- Faculty profiles normalised with credentials, research areas and identifiers
- Outcome and accreditation claims republished with sources and dates engines can quote
- Weekly replays across programme and campus prompts with change attribution
Illustrative modelled data — not live client results. Names anonymised.
Programme and campus visibility in one view.
Visibility trend and share of voice, loaded with an education-shaped dataset.
AI Visibility — Share of Voice over time
12-week trend, weighted across the tracked LLMs.
Tip: tab into the chart to focus a weekly data point, then use the left and right arrow keys to step through weeks. Press Escape to exit.
AI recommendation share — you vs competitors
Per-engine Share of Voice across the last 30 days of replays. Hover or focus a bar for the breakdown.
Illustrative modelled data — not live client results. Names anonymised.
Questions from education teams.
Can we track visibility per programme?
Yes. Prompt graphs are built per programme and campus, so each course gets its own visibility, share-of-voice and gap reporting instead of one institutional number.
How do we stop engines quoting wrong fees?
By publishing fees and eligibility as current, structured, dated pages rather than PDFs, and by correcting the aggregator records engines rely on. Replays track the correction.
Does this help with international applicants?
Yes. Prompt sets can include visa, language and credential-recognition intent by source country, so you can see where your institution is absent from those answers.
How many prompts does an institution need?
Most institutions start with 250 to 500 prompts covering flagship programmes, fees, eligibility and comparison intent, then expand each admission cycle.
What actually moves education citations?
Structured programme facts and sourced outcome claims move the most, because engines prefer institutions whose fees, eligibility and results can be quoted with attribution.
See which institutions AI names for your programmes.
Run a free audit and get the programme-by-programme breakdown of where you are cited and where you are absent.