This week, Dr Stuart Grey steps away from the usual research and sector-news format to talk directly about data security and provenance.

Prompted by Student Voice's recent move to serve Irish institutions from an EU data centre, Stuart explains how regional residency and model provenance work together. UK customer data remains entirely in the UK, while Irish customer data is processed in the EU. Categorisation and sentiment analysis use deterministic machine-learning models; locally run LLMs are used only for summarisation.

Pick one student comment and map its full journey from upload to analysis, reporting, retention, and deletion. Any point that cannot be clearly explained deserves attention before real student data is processed.

This episode was recorded by Dr Stuart Grey. The transcript was prepared from the final recording and lightly corrected for names, technical terminology, and readability.

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This week, Dr Stuart Grey asks who gets to decide what universities ask students in the first place.

Drawing on papers he read this week, his own teaching, and Student Voice's national NSS comment analysis, Stuart explores what changes when students help frame the question, produce the evidence, and interpret what it means.

Bring students into the evidence process before the questions and proposed solutions are fixed. Use categories and CAH comparisons to locate patterns, then read the comments in context and turn the shared interpretation into routine practice.

This episode was recorded by Dr Stuart Grey. The transcript was prepared from the edited recording and lightly corrected for names, terminology, and readability.

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This week, Dr Stuart Grey looks at how universities can design student voice around usable space, meaningful expression, a real audience, and visible influence.

The episode considers a Manchester Metropolitan University case study applying Lundy's participation model, a scoping review of how higher education research defines belonging, Leicester's NSS 2026 results, and the consistency questions raised by Swansea University's Quality Enhancement Review.

Take one existing student voice route and identify where students can contribute, how they can express a view, who is expected to respond, and what evidence will show that their contribution had influence.

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This week, Dr Stuart Grey looks at the difference between students participating in teaching and feeling able to influence it.

The episode examines a study of 1,713 final-year students in Colombia, where expressive voice accounted for a modest part of the relationship between collaborative learning and academic achievement. It then considers research on care and engagement, Sheffield Hallam's move away from a university-wide module evaluation process, and the evidence standards universities still need when student voice becomes more local.

If module evaluation becomes more local, retain a small shared evidence framework. Record what was asked, who could respond, how the evidence was interpreted, who owns the response, and what students were told afterwards.

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This week, Dr Stuart Grey focuses on QAA's response to NSS 2026 and what universities need to do once the results-day headlines have passed.

The episode looks at internal enhancement, weaker experiences among part-time, apprenticeship, and disabled students, the cost of taking part in student engagement, and the support student representatives need to contribute meaningfully to quality assurance. It also considers why shorter provision needs faster feedback loops.

Use NSS as the beginning of an internal quality conversation. Bring scores, subgroup patterns, and open comments together, then connect that evidence to supported student representatives and named institutional owners.

When analysing comments, distinguish the student experience itself from barriers to participation and evidence about follow-through. These patterns require different actions and often belong to different teams.

Bring NSS scores and comments together, check who is missing or having a weaker experience, support representatives to interpret the evidence, and record who owns the response and when students will see what changed.

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This week, Dr Stuart Grey focuses on NSS 2026 results day: the national headlines, the disabled student gaps that still need action, and what it takes to turn large volumes of student comments into usable evidence quickly.

The episode also looks at what Student Voice AI produced for NSS customers on results day, why sentence-level labels matter, and how recent research should shape the way universities use machine-learning analysis and high-stakes teaching evaluation evidence.

NSS results become more useful when teams read national context, local score movement, and student comments together. Comment analysis should preserve enough detail for staff to see what students actually meant, while keeping outputs reviewable and proportionate.

Sentence-level labelling helps separate praise, concerns, and mixed experiences within the same comment. That makes the evidence easier to route to the right people and reduces the risk that one average score carries too much of the story.

Treat NSS results day as the start of evidence work, not the end of reporting. Use the national headlines, but read them alongside subgroup gaps and the comments students wrote in their own words.

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This week, Dr Stuart Grey starts with Daniel Robson and Helena Lim's Wonkhe piece on King's College London, where the NSS comment analysis work was delivered in collaboration with Student Voice AI.

The episode discusses why faster comment analysis matters only when it preserves enough detail for human review, academic judgement, and visible action while universities still have time to respond.

Feedback analysis should preserve enough context for dialogue. A useful approach distinguishes whether comments are about process, timing, interpretation, learning design, or trust, then routes the evidence to people who can act while action is still possible.

The King's example matters because it shows the practical value of keeping a traceable line between what students wrote, how comments were categorised, and the judgement course teams then make.

Treat student feedback as the beginning of a structured conversation. Keep enough detail in the comments to show what students meant, who needs to respond, and whether action can still happen in time.

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This week, Dr Stuart Grey discusses why universities need sharper AI-related student feedback questions, especially as guidance moves from broad policy statements into modules, assessments, and local teaching practice.

The episode covers AI attitude survey design, Jisc's latest AI guidance and governance work, student mini-publics, and how universities can separate AI comments by usefulness, clarity, confidence, fairness, and support need.

AI feedback should not be treated as one broad theme. A useful analysis should distinguish whether students are talking about practical usefulness, unclear rules, confidence using tools appropriately, fairness between students, detection and false positives, staff inconsistency, or the need for better support.

Dig into what students mean when they talk about AI. Separate usefulness from confidence, confidence from clarity, and clarity from concern, then tag comments in a way that shows whether the problem is unclear rules, low trust, or support needs.

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This week, Dr Stuart Grey discusses why students judge staff AI use through care, trust, fairness, and visible human judgement, not only through technical competence or speed.

The episode covers new research on student perceptions of AI-using teachers, evidence on AI detector false positives, Bath's survey architecture, QAA's assessment and feedback roadshow, and a practical way to separate student comments about care, clarity, fairness, and accountability.

AI comments should not be grouped under one broad theme. Some are about academic care, some are about unclear rules, some are about fairness in detection or marking, and some are about accountability when something goes wrong. The useful move is to code the action required, not just the presence of the word "AI".

When students comment on AI, separate the comments by the kind of trust problem they reveal: care, clarity, fairness, or accountability. Each one needs a different institutional response.

https://www.studentvoice.ai/podcast/episodes/017-students-judge-ai-by-care-not-just-competence/

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This week, Dr Stuart Grey discusses AI legitimacy and student voice evidence: why students judge AI through trust, anxiety, fairness, and the visibility of human judgement, not only through speed or technical performance.

The episode covers student feelings about generative AI, feedback dialogue, Cambridge evidence on AI marking, Jisc's formative feedback pilot, and practical ways to separate comments about policy, assessment, belonging, and academic care.

AI comments are rarely only about a tool. They are evidence about what students think is safe, fair, useful, and human. A comment about uncertainty may belong with academic integrity policy, assessment design, confidence, belonging, and support at the same time. The useful move is to code the practical concern beneath the word "AI", then decide which team needs to respond.

Before expanding an AI feedback or marking pilot, ask students four questions: what do they think the AI is doing, where do they think human judgement sits, who can they ask when something feels wrong, and what evidence would make the process feel fair? Separate useful AI from legitimate AI before scaling it.

https://www.studentvoice.ai/podcast/episodes/016-ai-legitimacy-students-want-to-see-the-human-judgement/

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