University of Edinburgh selects Student Voice AI
Updated Mar 16, 2026
Glasgow, United Kingdom. The University of Edinburgh has selected Student Voice AI to analyse institution‑wide open‑text student comment data at scale. That gives university teams a faster, more consistent way to [turn student comments into evidence for teaching and learning decisions](/resources/nss-open-text-analysis-methodology/).
The University of Edinburgh, known for its research strength and breadth of teaching, will use Student Voice AI's text-analysis platform for education, run on controlled infrastructure, to classify and analyse student comments. The machine‑learning models will provide structured reporting that helps teams spot patterns quickly, benchmark results, and support evidence-based decisions on teaching and learning with less manual effort.
We are honoured to have been chosen by the University of Edinburgh for this work. The partnership reflects our focus on delivering meaningful insights from student comments and helping higher education providers act on evidence with confidence.
said Dr Stuart Grey, Founder of Student Voice AI.
Student Voice AI's models are trained on data from more than 100 UK higher education institutions. This enables the University of Edinburgh to analyse open‑text feedback and benchmark theme frequency and sentiment against the wider sector, rather than relying on isolated internal snapshots.
About the University of Edinburgh:
The University of Edinburgh is one of the world's leading research-intensive universities and ranks fourth in the UK for research power. Founded in 1583, it is globally recognised for its research, development, and teaching, and is committed to diversity, inclusion, and a supportive learning environment for students.
About Student Voice AI:
Student Voice AI provides text‑analytics for education. Using machine‑learning models trained exclusively on UK higher‑education data and run on controlled infrastructure, it analyses open‑text student comments to provide institutions with a consistent view of the student experience. Teams use these insights to inform teaching, learning, and quality enhancement while supporting UK GDPR requirements and the original purpose for which survey data was collected.
Want to see how institution-wide comment analysis works in practice? Explore Student Voice Analytics to see how universities classify open‑text feedback, benchmark results, and share structured reporting across teams.
Contact
Dr Stuart Grey
Founder and CEO
stuart@studentvoice.ai
Request a walkthrough
Book a free Student Voice Analytics demo
See all-comment coverage, sector benchmarks, and reporting designed for OfS quality and NSS requirements.
- All-comment coverage with HE-tuned taxonomy and sentiment.
- Versioned outputs with TEF-ready reporting.
- Benchmarks and BI-ready exports for boards and Senate.
UK-hosted · No public LLM APIs · Same-day turnaround
Related Entries
- Student Voice AI selected to analyse AdvanceHE data
- UCL selects Student Voice AI for comment analysis
- NSS open-text analysis methodology for UK HE
- Sentiment analysis for UK universities: a practical guide
- Student comment analysis governance checklist for UK HE