University of Leeds selects Student Voice AI
Published Sep 22, 2025 · Updated Sep 22, 2025
22/09/2025 — Glasgow, United Kingdom — Student Voice AI today announces a new partnership with the University of Leeds to support institution‑wide analysis of open‑text student feedback, with reporting designed for use by faculties, schools and programme teams.
Universities collect thousands of open comments through surveys such as the NSS, module evaluation and internal feedback. Turning that qualitative detail into evidence that can be tracked over time — and used in action planning — requires consistent categorisation and clear reporting. Student Voice AI will help Leeds bring these sources together and convert free text into comparable themes and sentiment, year‑on‑year and across disciplines.
Alongside sector benchmarking, the service is built around outputs that are straightforward to use in faculty, school and programme discussions. Reporting is written in plain language, and automatic redaction removes names and other identifiers so insight can be shared appropriately.
What Leeds will receive:
- Theme and sentiment analysis across surveys and internal feedback
- Benchmarks to show how patterns compare with the wider sector
- Segmentation by level of study, year group, mode of study, campus and discipline
- Summary reporting for faculties, schools and programmes, including positives and pain points by theme
- Automated redaction to remove personal identifiers before sharing
Student Voice AI runs on controlled infrastructure without third‑party model providers, supporting UK GDPR and the original purpose for which student feedback was collected.
About the University of Leeds:
The University of Leeds is a research‑intensive Russell Group university, established by Royal Charter in 1904. Based in the city of Leeds, it is one of the UK’s largest universities, with over 38,000 students from more than 170 countries across seven faculties.
About Student Voice AI:
Student Voice AI is the UK’s leading provider of text‑analytics for education providers. Using machine‑learning models run on controlled infrastructure, it analyses open‑ended student comments to provide a clear, comprehensive view of the student experience. Institutions use these insights to inform teaching, learning and quality enhancement while aligning with UK GDPR and the original purpose for which survey data was collected.
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See all-comment coverage, sector benchmarks, and governance packs designed for OfS quality and NSS requirements.
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All-comment coverage with HE-tuned taxonomy and sentiment.
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Versioned outputs with TEF-ready governance packs.
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Benchmarks and BI-ready exports for boards and Senate.
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