Education GEO & AI SEO. Get named when students ask AI where to study.
Prospective students now ask an assistant to shortlist courses, compare entry requirements and judge whether a degree is worth it. Those answers are built from league tables and third-party sources - rarely from your prospectus.
How applicants actually research now
The old funnel started with a search and a prospectus download. It increasingly starts with a conversation that produces a shortlist of five institutions - and you are either on it or you never enter consideration.
Best universities in the UK for computer scienceA ranking prompt answered almost entirely from third-party league tables and outcome data.
Which universities should I apply to with BBB predicted grades?Entry-requirement matching. Models need current, structured, course-level data to include you accurately.
Is a masters at [institution] worth it for career prospects?A value judgement built from graduate outcomes, employment data and student sentiment.
Cheapest online MSc in data science for international studentsAttribute-led filtering where fees, mode of study and international eligibility decide inclusion.
We build your prompt set from your own category and run it against live models - these are illustrative.
Your prospectus is not the source
Education is unusual: the institution publishes enormous amounts of content, yet models answer from rankings, outcome datasets and student forums. Closing that gap means making your own data machine-readable and improving how third parties describe you.
League tables dominate
Models treat rankings and outcome data as authoritative. Your marketing copy rarely enters the answer at all.
The unit is the course
Institution-level visibility is not enough. Applicants ask about specific courses, grades and modes of study.
Stale data costs applications
Outdated fees, entry requirements or course titles get repeated confidently by models for a long time.
What education GEO involves
Applicant prompt audit
We map the prompts applicants use across your priority courses and markets, and test which institutions models currently name.
Course data structuring
Making course, fee, entry-requirement and outcome data complete, current and machine-readable at course level.
Outcome & evidence content
Graduate outcomes, employability and research evidence presented in the form models quote.
International & recruitment markets
Prompt testing in your key recruitment markets, where model answers and source weighting differ significantly.
Where accuracy is a regulatory matter
Higher education marketing is bound by consumer protection expectations on clear, accurate and current information. In AI search this stops being a compliance box and becomes a visibility issue too, since inaccurate data propagates.
Course, fee and entry-requirement claims need to be accurate and current wherever they are published - including in structured data models read.
Employability and salary claims should be evidenced and sourced. Models reward traceable evidence and discount unsupported marketing language.
Course changes need to propagate everywhere at once, or models will keep repeating the old version for months.
How we measure education GEO
- Course-level citation share against competitor institutions
- Inclusion rate in AI-generated applicant shortlists
- Accuracy of model-stated fees, entry requirements and course titles
- Visibility in priority international recruitment markets
- Third-party ranking and outcome-source coverage
- AI referral traffic to course pages in GA4
- Before/after prompt testing across the applicant journey
- Time for course data changes to reach model answers
We work across regulated and high-consideration categories
Education GEO questions
What is education GEO and AI SEO?
Why do AI assistants cite league tables instead of our website?
Can you fix inaccurate information an AI gives about our courses?
Does this work for international student recruitment?
Is this relevant for edtech and online course providers?
See which institutions AI is shortlisting
We'll test your priority courses against live models, show where competitors are named instead, and fix the data behind it.