Ranking Methodology
EduIndex evaluates how visible, accurately represented and fairly treated 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, because AI is rapidly becoming the new discovery layer for higher education.
Our Approach
We take a repeatable, evidence‑led, fairness-by-design approach to measuring AI visibility keeping comparisons meaningful:
Assistant 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 and fairness: how assistants portray each institution across key themes, plus checks for over‑ or under‑representation.
Meaningful peer comparison: safeguards account for the influence of outsized volumes, benchmark results relative to peers, and reward improvement and growth.
Cadence that avoids jitter: monthly market‑relative benchmarking, paired with quarterly content checks, keeps scores dynamic and responsive to change.
Key Performance Indicators
- Crawlability
- Content Chunking
- Structured Data
- Trust And Provenance
- Verifiability
- Teaching
- Research
- Employability
- Student Life
- Facilities
- Location And Cost
- Support And Inclusion
- International Outlook
- Sustainability
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.
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.