Ranking Methodology
Eduindex evaluates how visible, and accurately represented universities are across leading AI platforms and large language models (LLMs). Unlike traditional rankings, Eduindex analyses how AI systems see, describe, and sometimes bias against institutions.
Our Approach
We take a repeatable, evidence‑led, fairness-by-design approach to measuring AI visibility keeping comparisons meaningful:
AI Signals: how often and in what context leading AI assistants mention or recommend each institution.
Content signals: whether institutional information (web pages, structured data and research outputs) is machine‑readable, trustworthy and easy for models to cite.
Perception: how assistants portray each institution across key themes, plus checks for over‑ or under‑representation.
Sector Benchmarking: safeguards account for the influence of outsized volumes, benchmark results relative to peers, and reward improvement and growth.
Cadence of updates: monthly market‑relative benchmarking, paired with quarterly content checks, keeps scores dynamic and responsive to change.
Key Performance Indicators
LLM Readiness
How easy it is for an AI tool to fetch, navigate, extract, and cite facts from an institution's website. We monitor 5 key criteria to assess this:
Perception
How positively or negatively AI tools perceive an institution based on 9 higher education specific signals:
Traffic
The volume of traffic referred to an institution from AI tools - measuring real-world student engagement driven by AI recommendations.
Diversity
How balanced your referred traffic is across different AI tools. A diverse AI referral base signals broader, more resilient visibility.
Score Calculation
How we compute the final rankings
Data Normalisation
All indicators are scaled between 0–100 to allow comparison.
Weighted Score Aggregation
Each indicator score is multiplied by its assigned weight to create a composite Eduindex score.
Final Ranking and QA
Universities are ranked according to their total Eduindex score; consistent settings with built‑in guardrails and fallbacks, deterministic checks and balances, and resilient data capture.
University not included?
We can fix that!
Eduindex is a blend of public data, AI-native telemetry and institution-provided inputs.
We continuously review sources for accuracy and reliability. Universities may correct inaccuracies or contribute new data for review.
We take accuracy and transparency seriously. The purpose of Eduindex is not simply to rank universities, but to help the sector improve its visibility and representation within AI systems.
Universities, students and policymakers should be able to trust that the data behind Eduindex is fair, evidence-based and open to scrutiny. Our aim is to reveal how AI platforms currently perceive higher education, not to reinforce but identify bias, correct it and create a more equitable foundation for the future of AI-driven discovery.
Institutions can request their AI visibility report at any time. If AI systems misrepresent an institution or show evidence of bias, these cases can be submitted to us for review and correction. Our methodology is reviewed regularly to reflect updates in AI technology, ethical standards and feedback from higher education experts, ensuring Eduindex remains credible, fair and genuinely valuable to the sector.