Feature

Use AI on student feedback without losing control of the data

Analyse student feedback with controlled data handling, deterministic methods, and institution-safe workflows.

Student Voice Analytics gives institutions a governed route for AI-assisted student feedback analysis. It avoids public LLM APIs for classification and keeps analysis inside a controlled workflow.

Discuss your survey

Discuss the source data, reporting question, outputs and delivery window.

Who this is for

Data protection teams, IT leaders, survey teams, and student experience leaders.

Why it matters

Student comments can contain sensitive personal data. Informal AI use may be fast, but it can create data protection, reproducibility, and governance risks if comments are pasted into public tools.

What teams get

Keep student feedback in a governed workflow

Approved data handling, controlled processing, and review points help teams use AI methods without informal workarounds.

Use deterministic classification for official evidence

Classification uses reproducible methods, which makes outputs better suited to governance than ad hoc prompt-based analysis.

Support IT and data protection review

Clear method and processing information helps technical stakeholders assess the workflow before institutional use.

How it works

Agree data transfer, processing, and access requirements.
Run feedback through controlled analysis rather than public AI tools.
Review outputs for sensitivity, categories, and interpretation.
Deliver governed reports, exports, and evidence packs.

Outputs

Private student feedback analysis workflow.
Governance-ready method notes.
Reviewed reports and exports.
Evidence packs suitable for institutional use.

Governance and evidence quality

Agree the survey, questions, reporting populations and intended audience before analysis.
Keep category definitions, analysis versions and denominators with the output.
Review relevant comments and ambiguous cases alongside the summary.
Agree data handling, access and the level of detail appropriate for each audience.

FAQs

Does Student Voice Analytics use public LLM APIs for classification?

No. The product context is built around deterministic ML classification rather than public LLM classification workflows.

Why is private analysis important for student feedback?

Student comments may include sensitive or identifying details, so institutions need controlled handling, review, and clear processing terms.

Can IT and data protection teams review the workflow?

Yes. Student Voice Analytics can provide method, processing, and governance information to support institutional review.

See the workflow with your team

Discuss the source data, reporting question and outputs your team needs. Explore the worked example before agreeing the scope and delivery window.