Dr Stuart Grey explores what our NSS comment data says about feedback: its prominence, changes in tone and differences between disciplines. He connects those findings with an institution-wide review of timing, clarity and opportunities to use advice, alongside a brief research finding about perceived fairness.

In this episode:

• Feedback appears in 13.8% of classified comments in 2026, almost unchanged from 2025.
• A longer view: mentions rose from 10.5% in 2023 while sentiment became less negative.
• Similar frequency and different sentiment in arts and physical sciences, with a higher mention rate in psychology.
• Keeping appreciation for advice and difficulties using it visible together.
• Reviewing assessment sequences, clarity and priorities through existing survey comments.
• Finding more detailed feedback results by discipline on the website.

Chapters

• 00:00 - What makes feedback useful?
• 00:44 - Feedback trends in the NSS comments
• 02:09 - Differences between disciplines
• 03:38 - Explore feedback findings on the website
• 04:00 - Timing and the assessment sequence
• 05:11 - Clarity and priorities for improvement
• 05:57 - Institution-wide review and positive accounts
• 06:44 - Research on usable feedback and fairness
• 07:13 - Connecting comments with process evidence
• 07:50 - Takeaways and further reading

Detailed findings and research

• Explore feedback findings across the disciplines we study.
• NSS open-text analysis methodology.
• Students judge feedback comments as fairer when they are usable. David Playfoot, Ruth Horry and Aimee E. Pink, Fairly useful feedback.

Mention rates describe classified comments within the relevant year or subject group. The sentiment index summarises language on a −100 to +100 scale. The proposed institutional review and imagined assessment examples are Stuart's practical applications.

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Dr Stuart Grey explores how institution-wide analysis of existing survey comments can show the work students do to get support. This week's research on disabled graduates and participatory PhDs leads into practical questions about service data, engagement measures and how universities review a change.

In this episode

• Keeping appreciation for helpful staff and difficulty accessing support visible together.
• Comparing themes across schools, student groups and survey cycles where the evidence supports comparison.
• Reading the comments behind a pattern to understand what students mean.
• Connecting doctoral-support concerns with the teams able to respond.
• Using student feedback evidence in decisions and checking what happens after a change.

Chapters

• 00:00 What getting support asks of students
• 01:07 Disabled graduates and the route into support
• 02:15 Reading survey comments across the institution
• 03:55 Participatory PhDs and doctoral support
• 05:19 Interpreting student engagement
• 06:31 OfS registration and feedback evidence
• 07:17 Reviewing an institutional change
• 08:02 Sources and newsletter

Research and sector sources

• Disabled graduates describe the work of securing support. Patricia Sanz-Martínez and Beatriz Morgado Camacho, From classroom to career: Self-efficacy and employment success of graduates with disabilities, published online 13 January 2026. Interviews with fifteen employed disabled graduates in Spain.
• Participatory PhD accounts reveal gaps in doctoral support. Cathy Beresford and colleagues, Supporting participatory PhDs: a call to action, published 22 September 2026. Reflections from eight Participatory PhD Network members.
• Jisc's RAISE reflections question student engagement measures. James Hodgkin's Jisc conference reflection, 25 September 2026, and Jisc's code of practice for learning analytics.
• OfS registration guidance makes student feedback evidence more explicit. Regulatory advice 3 and linked Annex G, updated 16 September 2026, for registration applications in England.

The support journey is an imagined example. The proposed institutional review and measures are Stuart's practical suggestions, not interventions evaluated by the cited research. The transcript is lightly edited for readability and correct name spelling.

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Dr Stuart Grey explores how student partners experience disagreement and uncertainty, and why the scope of an invitation to contribute deserves attention. Two qualitative studies lead into practical questions about decisions, responsibilities and whose experience appears in a partnership review.

In this episode:

• Following a student suggestion that was declined, including how the decision was explained.
• Exploring different reasons students may feel uncertain about partnership.
• Keeping valued relationships and concerns about decisions visible in the same account.
• Explaining the scope of a listening exercise and how its themes will be considered.

Research and sector sources

• Student partnerships can leave dissent unheard, based on Lutendo Nendauni's Student-staff partnerships as counterforces to toxic leadership in higher education: insights from a South African university. Nendauni's complete author-uploaded paper. Published online 27 March 2026. Interviews with ten students and ten academics at one institution, selected for relevant experiences, provide situated accounts rather than a prevalence estimate.
• Student partnership can bring uncertainty as well as trust, based on Gerald L. Decelles III, Catherine Bovill and Anders Mattias Lundmark's The Same, But Different: Teacher and student experiences of partnership, published 6 November 2025. Complete Decelles, Bovill and Lundmark paper. Twenty questionnaire respondents included nine interviewees; these are not separate samples totalling 29. The authors identify limited representation of critical experiences in the analysis.
• UWE community listening exercise sets out feedback boundaries. The complete UWE announcement and participation information, published 17 August 2026, explains the remit and planned reporting. This is an ongoing exercise, not an evaluation of its outcomes.

The learning-activity example, proposed review questions and suggestions for interpreting comments are Stuart's practical applications, not interventions evaluated by these sources.

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Dr Stuart Grey explores what students mean when they say they feel supported. Research on international students and doctoral expectations provides a starting point for distinguishing practical help, emotional support and the experiences people hoped for. Jisc's account of support conversations leads into practical questions about how universities can examine what happened after help was offered.

In this episode:

• Why practical assistance and feeling understood deserve separate attention in feedback.
• How doctoral expectations can open a conversation about the help a researcher needs.
• What to check during and after a support contact or referral.
• How to preserve mixed experiences when interpreting comments.

Research and sector sources

• International student support: help, care and belonging. Based on Liu, Liao and English's study of social support, belonging and psychological distress, published online on 2 October 2025. Its cross-sectional, non-random survey concerns international students in China; findings are associations and are not a UK university benchmark.
• PhD expectations relate to later satisfaction. Based on Steinhauser and colleagues' study of person-environment fit and doctoral success, published online on 2 May 2026. The German studies use self-reported outcomes and do not measure completion. The recorded phrase "increased satisfaction" refers to an association with later satisfaction; the study does not establish an increase over time or a causal effect.
• Jisc learning analytics account puts student conversations at the centre. The official Jisc account of Leeds Trinity practice, published on 7 September 2026, describes existing practice rather than a new rollout or a causal evaluation.

The imagined support scenarios and suggestions for reviewing support are Stuart's practical proposals, not findings from the studies.

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In this episode

• Matching the evidence to the decision a course team needs to make.
• Keeping students' circumstances visible when comments move between teams.
• Making preparation, dates, expenses and support clear before inviting students onto a committee.
• Understanding what different participation routes can contribute.
• Considering whose experiences a busy student committee may still be missing.
• The OfS quality regulation survey for staff in England, closing on 12 October 2026.

Sources and further reading

• Student ratings and achievement: a modest link, with the research record at TED University.
• Edinburgh students link housing insecurity to disrupted study, based on Hannah Awcock's published paper.
• QAA report shows how student engagement costs narrow student voice, with the official QAA announcement and Student Perspectives on: Costs of Engagement.
• OfS quality regulation survey opens ahead of wider reform, with the official OfS survey announcement.

Reading the evidence

The Edinburgh interviews concern experience at one institution in 2022/23. Stuart's observation about continuing housing difficulties is his own experience, rather than a current prevalence estimate from that study. QAA's report draws on 89 comments and committee members' experience; that is not a count of individual research participants. The practical examples and suggested responses are Stuart's interpretation. The OfS exercise is an evaluation in progress, with findings expected in spring 2027.

Practical takeaway

Ask what the feedback can tell you and who had a workable opportunity to give it. Keep those questions together when deciding what needs to change.

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The episode explains why Student Voice AI separates deterministic machine-learning categorisation and sentiment analysis from locally run LLM summarisation, and why that distinction matters for comparisons across cohorts, faculties and years.

In This Episode

• What Swansea University's GenAI-assisted comment-analysis pilot involved.
• Why QAA recommends consent checks, anonymisation, controlled infrastructure and human validation.
• What the Swansea example does and does not establish about effectiveness.
• The difference between exploratory thematic work and repeatable institutional analytics.
• Why Student Voice AI uses deterministic models for categorisation and sentiment.
• Where locally run LLM summarisation fits after the analysis.
• Practical stability and provenance checks for university pilots.

About This Recording

Recorded by Dr Stuart Grey for Episode 28 of Student Voice Weekly. The transcript has been lightly edited for clarity while preserving the substance and natural delivery of the recording.

Sources

• Faster thematic analysis of student survey comments, by Sophie Leslie of Swansea University
• QAA sets safeguards for GenAI student feedback analysis
• Student Voice Analytics versus generic LLMs
• Student Voice Analytics

Practical Takeaway

Decide whether the task is exploratory or repeatable. For an exploratory first pass, test GenAI with QAA's safeguards. For reporting, benchmarking or longitudinal comparison, use a method that can reproduce its categories and sentiment, preserve a route back to the evidence, and maintain appropriate data residency.

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Drawing on new research into student-centred culture and longitudinal evidence about student engagement, Stuart explains why feedback needs a clear route to the people who can act-and why comments provide context that scores alone cannot.

In This Episode

• Why collecting student feedback is only the beginning.
• How evidence can become trapped at a different level from the decision it needs.
• Why closing the feedback loop does not mean agreeing to every request.
• What longitudinal research tells us about engagement scores and student change.
• How open comments help teams distinguish between different barriers to participation.
• Why bot protection improves one part of survey integrity, but does not establish representativeness.
• Why LLM-generated survey comments make response-quality checks increasingly important.

Research

• Student-centred culture needs student voice at every level
• Engagement scores can mask student change

Across the Sector

• Jisc survey bot protection helps safeguard response quality
• Pre-arrival feedback should identify strengths as well as risks

Practical Takeaway

Take one recurring issue from last year's feedback and trace it from collection to decision and response. Give it a named route to the person or group that can act, carry enough context with the evidence, and show students what happened afterwards.

About This Recording

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

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Drawing on a national study of more than 19,000 undergraduates and research into how early-career academics use student evaluations, Stuart explores how universities can distinguish worthwhile academic challenge from avoidable pressure created by course design.

In This Episode

• Why academic stress cannot sit with counselling and wellbeing services alone.
• What a large Australian study suggests about the relationship between course experience and student distress.
• How workload bunching, unclear criteria and weak explanations of purpose create avoidable friction.
• Why evaluation scores show where to look while comments help explain what happened.
• How developmental conversations make student feedback more useful and safer for staff.
• Why repeated evidence and traceable comments matter when feedback concerns individual staff.

Research

• Academic stress is the strongest university-related distress signal
• Student evaluations become useful when comments explain the scores

Further Reading

• Three new QAA Subject Benchmark Statements open to student voice
• QAA Subject Benchmark Statements

Practical Takeaway

Choose one point in a programme where students report pressure and trace it back to the design decision behind it. Test whether the challenge is necessary, its purpose is clear, the workload is manageable, and the comments reveal avoidable friction that a course team can remove.

About This Recording

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

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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.

In This Episode

• Why student comments need to be treated as sensitive evidence rather than ordinary spreadsheet text.
• Why UK customer data remains entirely resident in the UK.
• What EU data residency provides for Irish institutions-and what it does not answer by itself.
• Why categorisation and sentiment analysis use deterministic machine-learning models.
• Why LLMs are used only for summarisation and run locally on controlled hardware.
• What data provenance means in practical terms.
• Why model, taxonomy, prompt, and processing versions matter for credible trends.
• Five questions to ask before uploading real student data to an analysis tool.

Practical Resources

• Student comment analysis governance checklist
• Student Voice Analytics governance and reproducibility
• Student Voice Analytics and generic LLMs compared
• ICO guidance on UK GDPR
• ICO guidance on international data transfers

Practical Takeaway

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.

About This Recording

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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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.

In This Episode

• Why student-led consultation can change the quality of wellbeing evidence.
• What Bangor University's student consultants added by working across the whole evidence pipeline.
• What Student Voice's Feedback and Student Support categories reveal beyond the headline average.
• What a study of more than 1,200 students reveals about self-doubt and feedback-seeking.
• How CAH codes support like-for-like subject comparisons without becoming a league table.
• Why a category is only a starting point when one comment can cover several experiences.
• How earlier student involvement can carry through into curriculum review and routine practice.

Research

• Student-led wellbeing strategy works best when students lead the consultation
• Self-doubt makes students watch for feedback, but hesitate to ask for it

Across the Sector

• Advance HE says student voice should start before consultation
• Northampton's Inclusive Curriculum Toolkit shows how student listening can reshape assessment and feedback

Student Voice Evidence

• NSS open-text analysis methodology for UK higher education
• What UK students say about feedback
• What UK students say about student support

Practical Implication

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.

About This Recording

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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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.

In This Episode

• Why adding a feedback channel does not necessarily widen participation.
• How space, voice, audience, and influence provide a practical audit for student voice.
• What a reflective departmental case study can and cannot demonstrate.
• Why belonging measures need definitions grounded in student experience.
• How open comments can explain what students recognise as visible action.
• Why strong partnership structures still need consistent assessment and support rules.

Research

• Student voice gets stronger when participation is designed, not assumed
• Belonging surveys are stronger when students define what belonging means

Across the Sector

• Leicester's NSS 2026 results show why student voice gains need visible follow-through
• Swansea's QER says strong student partnership still needs more consistent assessment and support rules

Practical Implication

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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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.

In This Episode

• Why participation and influence are different dimensions of student engagement.
• What the Colombian study can and cannot tell us about expressive voice and achievement.
• How care, clarity, interaction, and assessment design shape engagement.
• Why changing the unit of a survey may not resolve weak participation.
• What institutional governance is needed when Schools use different feedback methods.
• How open comments can distinguish participation, influence, response, care, and clarity.

Research

• Student voice improves outcomes when students can shape teaching
• Student engagement depends on visible care, not just teaching technique

Across the Sector

• Sheffield Hallam's module evaluation reset shows why local student voice still needs governance
• OfS NSS student characteristics data adds provider typologies, but key equity splits are missing

Practical Implication

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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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.

In This Episode

• Why improving NSS averages should not close down the conversation about subgroup gaps.
• What QAA's post-results message means for internal enhancement work.
• How time, money, accessibility, and recognition shape who can participate in student voice.
• Why student representatives need clear papers, briefings, mentoring, and wider survey evidence.
• How short-cycle provision changes the timing of student feedback.

Student Voice Practice

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.

Research Spotlight

• Student representation in quality assurance needs training, trust, and usable evidence

Across the Sector

• QAA says NSS 2026 should drive internal student voice action, not just league tables
• QAA's short-cycle course guidance says student feedback must move faster for the LLE

Practical Takeaway

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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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.

In This Episode

• Why stronger NSS national headlines do not remove the need for subgroup analysis.
• What results day looked like across Student Voice AI's NSS customer work.
• Why sentence-level comment analysis helps teams avoid flattening mixed student feedback.
• How Valeriya Minakova, Ryan Patterson and colleagues frame machine learning as a complement to academic judgement.
• Why Shaylen Stone, Gerard Jefferies, Megan Lee and Cindy Davis's wellbeing research is a warning about high-stakes evaluation use.
• Why disabled student gaps in organisation, management, and student voice need comment-level evidence alongside the scores.

Student Voice Practice

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.

Research Spotlight

• Can Machine Learning Help Instructors Make Sense of Student Evaluation Comments?
• High-stakes teaching evaluations can damage academic wellbeing

Across the Sector

• NSS 2026 results rise on student voice, but disabled student gaps still need action
• OfS updates modular outcomes for the LLE, and why continuous student feedback matters

Practical Takeaway

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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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.

In This Episode

• What the King's College London example shows about turning NSS comments into usable evidence quickly.
• Why student feedback should not be treated as a direct verdict on teaching quality.
• Why NSS timing makes local, earlier feedback systems more important.
• How VLE feedback and reattempt loops change what universities should ask about digital learning.
• Why faster comment analysis matters only when it leads to earlier human review and visible action.
• How to separate comments about process, timing, interpretation, digital learning design, and trust.

Student Voice Practice

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.

Research Spotlight

• Student evaluations should inform dialogue, not replace academic judgement
• VLEs scale better when feedback and reattempt are built in

Across the Sector

• Wonkhe's AI feedback analysis case shows how universities can act on NSS comments sooner
• Wonkhe's sector data warning shows why NSS and student feedback still arrive too late

From the Archive

• NSS open-text analysis methodology for UK HE
• The current understanding of student voice in assessment and feedback
• How to enhance student voice in university governance

Practical Takeaway

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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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.

In This Episode

• Why broad "do you use AI" or "are you concerned about AI" questions no longer give universities enough evidence to act on.
• Why student AI attitudes need to be separated into usefulness, identity and confidence, and concern.
• How module-level and assessment-level AI guidance changes what universities should ask students.
• Why human-in-the-loop governance matters when AI is used to summarise or interpret student comments.
• How to code AI-related comments so teams can distinguish unclear rules, low trust, fairness concerns, and support needs.
• Why feedback loops matter when students are asked for views on fast-moving institutional decisions.

Student Voice Practice

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.

Research Spotlight

• AI attitude surveys need to separate usefulness, self-expression, and concern
• Student mini-publics only matter if universities can show what changed

Across the Sector

• Jisc's June HE AI meetup says universities need sharper student feedback on AI guidance
• Jisc's 'human in the loop' pilot sharpens AI governance for student feedback evidence

From the Archive

• What learning resources do business management students say they need most?
• Do extracurricular activities enhance history students' academic success?
• What assessment methods work in therapy education?

Practical Takeaway

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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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.

In This Episode

• A brief Student Voice update on the build-up to NSS results day, new output formats, Newcastle University returning, and the University of Greenwich joining as a new customer.
• Why students can perceive teachers who use AI as less caring.
• Why visible human judgement matters when AI supports teaching, assessment, feedback, or academic integrity.
• What AI detector false positives mean for student trust and misconduct processes.
• How Bath's student feedback model shows the value of collecting evidence at the right level.
• Why QAA's assessment and feedback work reinforces the need to treat AI as part of assessment design, not a separate policy island.
• Why comments about AI should be separated by care, clarity, fairness, and accountability.

Student Voice Practice

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".

Research Spotlight

• Students judge AI-using teachers by care, not just technical competence
• AI detectors catch many LLM-assisted essays, but privacy and false positives remain major risks

Across the Sector

• Bath's 2026 student feedback system shows how to collect the right survey at the right level
• QAA launches Assessment & Feedback Roadshow, what it means for student feedback on assessment

From the Archive

• Do history degrees build personal development?
• Does module choice shape history students' engagement and success?
• Do peer opportunities improve learning for literature students?

Practical Takeaway

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.

Full Episode Page

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

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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.

In This Episode

• Why student feelings about AI are mixed, and why that matters for belonging and trust.
• How feedback dialogue helps students use assessment comments rather than decode them alone.
• What Cambridge's AI marking study shows about classification agreement, bias, and the need for human judgement.
• Why Jisc's pilot points towards formative feedback as the right place to start.
• How older student voice work on AI, co-creation, and assessment still helps frame the current debate.
• A practical way to test whether students can see where human judgement sits in an AI-supported process.

Student Voice Practice

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.

Research Spotlight

• Students' feelings about AI reveal trust and belonging risks universities miss
• Students use assessment feedback better when universities create space for questions

Across the Sector

• Cambridge study shows why AI marking in higher education still needs human judgement
• Jisc's AI marking and feedback pilot says formative feedback is the right place to start

From the Archive

• AI and Education - Equity Challenges and Opportunities
• Respect is key for successful student voice as co-creation practices
• Which assessment methods work best in physics?

Practical Takeaway

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.

Full Episode Page

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

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The episode covers time poverty as widening participation evidence, fair process in student evaluation systems, Cardiff's QER recommendation on student voice mechanisms, Advance HE's TEF analysis, and practical ways to read comments about workload, organisation, and trust as evidence about system design.

In This Episode

• Why time functions as a classed resource for low-income students.
• How timetable design, attendance requirements, deadline bunching, travel, and payment schedules can reproduce inequality.
• Why student evaluation systems earn trust through procedural justice, not just fair-looking scores.
• What Cardiff's QER recommendation says about representation, support structures, and wider student engagement.
• How TEF evidence can miss the technicians, demonstrators, studio staff, and lab teams students actually experience.
• A practical way to split mixed comments before turning them into action plans.

Student Voice Practice

Time poverty comments should not be filed only as individual resilience or study skills issues. They are often evidence about scheduling, workload, communications, finance, placement design, and whether the course leaves students enough room to participate. The useful question is not simply whether students are working hard, but whether the system is spending time they do not have.

Research Spotlight

• Time poverty creates hidden inequality for low-income students
• Student evaluation systems earn trust through fair process, not just fair scores

Across the Sector

• Cardiff's QER review says student voice mechanisms need clearer purpose and wider reach
• Advance HE's TEF analysis shows student voice evidence still misses part of teaching excellence

From the Archive

• Key elements of team teaching
• What are media studies students telling us about course organisation?
• Are medical students' workloads manageable?

Practical Takeaway

Take one programme where work, travel, care, or placement pressure is already visible in the comments. Map the first four teaching weeks against contact hours, gaps between sessions, deadlines, attendance rules, and administrative pinch points. If the map shows the course needs spare time students do not have, that is widening participation evidence.

Full Episode Page

https://www.studentvoice.ai/podcast/episodes/015-time-poverty-is-the-new-hidden-barrier/

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The episode covers everyday community engagement, friendship and collaborative study, DfE franchise arrangements guidance, Jisc's new "None of the above" option, and practical ways to read comments about belonging as evidence about curriculum design, wellbeing, and confidence.

In This Episode

• Why belonging often depends on small, repeated points of contact in teaching.
• How international student wellbeing is shaped by routine engagement with staff, peers, and the wider community.
• Why friendship formation and collaborative study put belonging inside course design.
• What DfE franchise arrangements guidance means for comparable student feedback evidence across delivery partners.
• How a "None of the above" option can improve survey data quality when answer sets do not fit.
• A practical way to sort belonging comments by the kind of action they require.

Student Voice Practice

Belonging evidence becomes more useful when teams read comments by what they reveal, not only by the words they mention. A seminar comment may be evidence about teaching delivery, confidence, peer connection, and wellbeing at the same time. The practical task is to separate provision problems, participation problems, and relationship problems so the right team can act.

Research Spotlight

• Everyday community contact shapes international student wellbeing
• Friendships and collaborative study shape belonging more than extracurricular activity

Across the Sector

• DfE franchise arrangements guidance raises the stakes for student feedback evidence
• Jisc Online Surveys adds a "None of the above" option, and why it matters for student feedback data quality

From the Archive

• Does nursing course content deliver breadth and relevance?
• Architecture students on personal development
• Student voice in the development of assessment practices

Practical Takeaway

Map belonging comments by action route before deciding the intervention. Some comments point to seminar design, some to group work structure, some to staff contact, and some to timetable or cohort stability. Treat belonging as a design parameter, then take the map into the programme team meeting.

Full Episode Page

https://www.studentvoice.ai/podcast/episodes/014-belonging-is-built-in-seminars-not-slogans/

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The episode covers AI feedback design, paid student voice roles, QAA Scotland's review of awarding evidence, HEPI's latest findings on student value and belonging, and practical ways to test whether feedback is specific, trusted and usable.

In This Episode

• Why students tend to value AI feedback most when teacher judgement stays visible.
• How paid student voice roles can make representation more accountable when the route from input to action is clear.
• What QAA Scotland's national review of awarding arrangements means for student voice evidence around assessment.
• Why student value, belonging and attendance need local comment analysis rather than headline numbers alone.
• A practical way to review AI feedback pilots before scaling them.

Student Voice Practice

Student voice work is most useful when it turns a general sector discussion into an institutional question that teams can test locally. For AI feedback, that means asking students whether the feedback was specific, whether they trusted it, and whether they could use it in their next piece of work.

Research Spotlight

• Students value AI feedback most when teacher judgement stays in the loop
• Paid student voice roles can make representation more accountable

Across the Sector

• QAA national review: awarding arrangements and student voice evidence
• Student Academic Experience Survey: student value, belonging and attendance

From the Archive

• Computer science students' views on Covid-19 challenges
• Navigating university life: insights from education students
• English literature students' perspectives on Covid-19

Practical Takeaway

Before scaling an AI feedback pilot, ask students three direct questions: was it specific, did they trust it, and could they use it in the next piece of work? Then compare those answers with teacher judgement rather than treating the tool output as the answer.

Full Episode Page

https://www.studentvoice.ai/podcast/episodes/013-ai-feedback-needs-teacher-judgement-and-better-design/

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The episode covers feedback literacy, OfS scrutiny of assessment feedback, NSS 2026 preparation, and why student feedback comments need to be grouped by the kind of action they require.

In This Episode

• Why assessment and feedback scores can hide several different problems.
• How feedback literacy can develop even when survey scores stay flat.
• What the OfS Northampton case signals about evidence, clarity and student voice.
• Why NSS 2026 preparation should connect scores with comments, module evaluations, rep feedback and other student voice routes.
• A practical way to split feedback comments into timing, clarity and usability.

Student Voice Practice

This week's Student Voice update looks at the first trials of the comment review pipeline, including full-text search across student comments. The aim is to let teams move from a dashboard theme to the exact student comments behind it, then review coding decisions in context.

Research Spotlight

Feedback literacy can develop in ways a standard survey scale misses

PhD competency confidence varies by research style: evidence from 1,105 doctoral students

Sector Watch

OfS requires clearer assessment feedback at Northampton, and why student voice evidence matters

NSS 2026 has closed, and what universities should do before the July results

From the Archive

Does personal development in business studies improve outcomes?

Are medical technology students let down by communications?

Inverted learning: turning traditional teaching methods upside-down

Practical Takeaway

Take one recent set of assessment or feedback comments and split them into three piles: timing, clarity and usability. Then check whether the actions you had planned actually match the pattern in the comments.

Full episode page: https://www.studentvoice.ai/podcast/episodes/012-when-scores-miss-the-feedback-story/

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The episode covers research on first-year introduction week, student voice as partnership, Portsmouth's assessment regulation changes and Westminster's mid-module check-ins.

In This Episode

• Why welcome week mainly strengthens peer belonging, not staff belonging.
• Why belonging needs to be designed and checked across the semester, not only in week zero.
• How dropout intention and wellbeing connect to belonging evidence.
• Why student voice should be treated as partnership, not extraction.
• How student feedback can reshape assessment regulations.
• What earlier module feedback can look like in practice.

Student Voice Practice

The episode includes a Student Voice update on previews of the new NSS 2026 reports and the gap between central analysis and department-level action.

Research Spotlight

Welcome week attendance boosts peer belonging, but not staff belonging

Student Voice as Partnership, Not Extraction

Sector Watch

Portsmouth's assessment regulation changes show how student feedback can reshape assessment rules

University of Westminster's Mid-Module Check-ins show what earlier module feedback can look like

From the Archive

Student perspectives on HRM course content

Obstacles to students voice in curriculum design

King's Wellbeing Survey, and why joined-up student feedback matters

Practical Takeaway

Check whether your belonging evidence distinguishes peer belonging from staff belonging. If it does not, you may be measuring one part of the experience and acting on another.

Full episode page: https://www.studentvoice.ai/podcast/episodes/011-welcome-week-is-not-your-belonging-strategy/

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The episode covers student engagement research, generative AI feedback, Glasgow's assessment and feedback tool and Wonkhe's AI assessment report.

In This Episode

• Why engagement gaps often point to institutional design.
• How unclear briefs, late feedback and inconsistent expectations shape student behaviour.
• What research suggests about institutional design and student background.
• Why students may use generative AI for feedback while still trusting teachers more.
• How Glasgow's assessment and feedback tool shows action on student voice.
• Why late feedback can drive student AI use.

Student Voice Practice

Stuart starts with a deceptively simple question: when engagement is weak, where should universities look first?

Research Spotlight

Student engagement depends more on institutional design than student background

Students use Generative AI for feedback, but trust teachers more

Sector Watch

Glasgow's assessment and feedback tool shows how universities can act on student voice

Wonkhe's AI assessment report shows how late feedback drives student AI use

From the Archive

What do art students need from learning resources?

King's College London partners with Student Voice AI

Do adult nursing students feel their courses support personal development?

Practical Takeaway

Take one engagement concern and identify the design friction underneath it: unclear expectations, timing, access, workload, assessment structure or feedback delay. Fixing the design is usually more useful than another reminder.

Full episode page: https://www.studentvoice.ai/podcast/episodes/010-engagement-is-a-design-problem-not-a-student-problem/

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The episode connects research with 739 students to QAA subject benchmark statements, QAA GenAI assessment focus groups and evidence on who completes student evaluations.

In This Episode

• Why GenAI disclosure is not just a policy communication problem.
• How trust, fairness and implementation fidelity shape whether students disclose AI assistance.
• Why inconsistent interpretation by staff turns disclosure into a social risk.
• What non-response bias means for student evaluation evidence.
• How QAA subject benchmark statements connect to student feedback evidence.
• Why student voice on AI assessment needs more structure.

Student Voice Practice

The episode focuses on how overall sentiment averages can hide whose experience is changing, especially when institutions need to understand patterns by group, context or assessment design.

Research Spotlight

Students disclose AI use when governance feels fair and trustworthy

Who Actually Fills In Student Evaluations? New Evidence on Non-Response Bias

Sector Watch

QAA Subject Benchmark Statements, and what they mean for student feedback evidence

QAA's GenAI assessment focus groups show why student voice on AI needs more structure

From the Archive

Student Voice AI + evasys + Advance HE for PTES & PRES 2025

What did COVID-19 mean for business and management students?

Do history students benefit from clearer assessment methods?

Practical Takeaway

Look at one AI disclosure rule and ask whether students would hear the same explanation from every tutor. If not, the governance problem is implementation, not just wording.

Full episode page: https://www.studentvoice.ai/podcast/episodes/009-trust-is-the-missing-piece-in-ai-disclosure/

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The episode covers a review of 66 studies on belonging, mature student induction, DMU's block teaching evaluation and Jisc's decision to retire Digital Experience Insights.

In This Episode

• Why belonging is not one thing and should not be treated as a single diagnostic score.
• How belonging evidence changes when the job is descriptive, evaluative or predictive.
• Why mature student induction needs to be designed beyond the default student.
• What DMU's block teaching evaluation shows about faster feedback and survey evidence.
• What the retirement of Jisc Digital Experience Insights means for benchmarking.
• Why data security questions matter when universities process student comments with AI.

Student Voice Practice

Stuart reflects on the Future Facing Learning and AI in Higher Education conference at Teesside University and the question of who controls the AI infrastructure of higher education.

Research Spotlight

Retention work needs belonging evidence, not just a single score

Induction can build mature students' belonging, but only if universities design beyond the default student

Sector Watch

DMU's block teaching evaluation links faster feedback with stronger survey evidence

Jisc will retire Digital Experience Insights on 31 July 2026

From the Archive

Gamification in statistics teaching

Do accounting students value a balanced mix of assessment methods?

Do extracurricular activities genuinely benefit law students?

Practical Takeaway

When looking at belonging data, decide first whether you are describing experience, evaluating an intervention or predicting risk. Each job needs different evidence.

Full episode page: https://www.studentvoice.ai/podcast/episodes/008-belonging-is-not-a-single-number/

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The episode covers evaluation dialogue, belonging survey validation, UUK quality principles, Jisc Online Surveys changes and how complaint comments often include practical fixes.

In This Episode

• Why student feedback is not the intervention by itself.
• Why staff need time and context to interpret student comments well.
• How dialogue turns evaluation reports into teaching improvement.
• Why belonging surveys need validation before universities benchmark them.
• How UUK's quality principles place student feedback evidence inside efficiency decisions.
• Why survey question type changes can affect the evidence you collect.

Student Voice Practice

The episode looks at the operational reality behind feedback use: where is the time, and who is in the room, when teams ask what students are actually saying?

Research Spotlight

Student evaluations help teaching improve when staff can discuss them

Belonging surveys need better validation before universities benchmark them

Sector Watch

UUK's five quality principles put student feedback evidence at the centre of efficiency decisions

Jisc Online Surveys changes question types, and why it matters for student feedback survey design

From the Archive

What is Student Voice?

Are accounting students overloaded and under-supported?

Do biology students want different assessment methods?

Practical Takeaway

Create a 45-minute interpretation slot for one evaluation report. Ask what is a one-off, what is a signal, and what can be fixed through clearer communication before writing the action plan.

Full episode page: https://www.studentvoice.ai/podcast/episodes/007-evaluations-do-not-improve-teaching-conversations-do/

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The episode connects a six-year evaluation redesign study with OfS evidence requirements, PTES survey design and the risk of comparing scores that are not really comparable.

In This Episode

• Why a clean dashboard cannot rescue a badly designed survey question.
• What co-design reveals about how staff and students understand good teaching.
• Why concepts like rigour and learning environment need careful unpacking.
• Why student evaluation scores are not automatically comparable across departments, programmes or time.
• What OfS quality assessment tells us about missing module evaluation evidence.
• How PTES incentives and confidentiality shape postgraduate feedback.

Student Voice Practice

The episode starts from a simple point: if the feedback instrument is badly designed, the data will never be as trustworthy as the dashboard suggests.

Research Spotlight

Student evaluations improve when staff and students redesign them together

Student evaluation scores are not automatically comparable across departments, programmes, or time

Sector Watch

OfS quality assessment flags missing module evaluations and student surveys at King Stage Limited

Westminster's PTES 2026 launch shows how survey incentives and confidentiality shape postgraduate feedback

From the Archive

Do extracurriculars enhance psychology students' experience?

Do electrical engineering students prioritise hands-on learning?

The Best Text Analysis Software for Education

Practical Takeaway

Choose one survey item that staff often dispute and ask students and staff separately what they think it means. If the answers differ, the item is not ready to carry diagnostic weight.

Full episode page: https://www.studentvoice.ai/podcast/episodes/006-co-design-your-evaluations-or-you-will-not-trust-the-data/

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The episode covers QAA research on student representation, accreditation visibility, survey benchmarking and the governance work needed to make student voice credible.

In This Episode

• Why having feedback channels is not the same as having a healthy student voice system.
• What QAA research across 78 institutions suggests about student representation practice.
• Why accreditation and quality assurance work needs to be visible to students.
• How benchmarking and triangulation can stop teams overreading sector averages.
• What Glasgow's Student Voice Framework signals about connected governance.
• Why "about average" is not always the same as "no action needed."

Student Voice Practice

Stuart reflects on conversations with institutions preparing for NSS season and the recurring gap between collecting feedback and knowing whether the system behind it is working.

Research Spotlight

Students see accreditation work better when quality assurance is visible

Student survey data works better when universities benchmark and triangulate it

Sector Watch

QAA research on student representation practices across 78 UK institutions

University of Glasgow launches a Student Voice Framework

From the Archive

What are accounting students telling us about university life?

Do structured collaborations improve learning for history students?

Lancaster University partners with Student Voice AI

Practical Takeaway

Pick one feedback channel and map the route from student comment to owner, decision and communication back to students. If that route is unclear, the system may look performative even when work is happening.

Full episode page: https://www.studentvoice.ai/podcast/episodes/005-make-quality-assurance-visible-or-students-will-assume-it-is-not-working/

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The episode covers correlated evaluation scores, gender stereotypes in teaching recognition, joined-up wellbeing survey practice and Glasgow's MyGrades rollout.

In This Episode

• What halo effects mean in student evaluations.
• Why correlated scores make fine-grained diagnosis risky.
• How comments and operational data can make broad score signals more useful.
• How gender stereotypes shape who gets recognised for teaching excellence.
• Why King's wellbeing survey and Glasgow's MyGrades point towards more joined-up student feedback systems.
• Why coding consistency matters when small teams analyse thousands of comments.

Student Voice Practice

The episode reflects on the challenge of keeping student comment analysis consistent when a small team is coding large volumes of feedback across cycles.

Research Spotlight

Halo effects in student evaluations: what correlated scores are actually telling you

Gender stereotypes shape who gets recognised for teaching excellence

Sector Watch

King's launches a wellbeing survey positioned alongside NSS and PTES

Glasgow rolls out MyGrades university-wide after acting on student feedback

From the Archive

Are adult nursing students' workloads manageable?

Oral examination as an online assessment tool

Can online psychology match the on-campus experience?

Practical Takeaway

Treat small differences between evaluation items with caution. Use comments to check whether a low score reflects one specific issue, a broader experience problem or a general halo effect.

Full episode page: https://www.studentvoice.ai/podcast/episodes/004-when-scores-all-move-together-halo-effects-bias-and-what-to-do-with-student-feedback/

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The episode connects a study of 3,070 misconduct reflections with evidence on feedback speed, the latest TEF data dashboard and OfS condition E10 on subcontracting.

In This Episode

• Why misconduct, feedback complaints and regulation dashboards should not be treated as separate stories.
• What student reflections after misconduct cases reveal about confusion, workload and confidence.
• Why faster feedback policies do not automatically produce better NSS outcomes.
• What teams should look for in the latest TEF dashboard.
• Why OfS condition E10 makes student voice evidence important in subcontracted provision.
• How comment analysis can separate punitive explanations from design problems.

Student Voice Practice

March assessment season brings misconduct, feedback and workload into the same student experience. Stuart explains why these signals need to be read together.

Research Spotlight

What 3,070 misconduct reflections reveal about academic integrity policy

Faster feedback policies do not guarantee better NSS results

Sector Watch

OfS publishes latest TEF data dashboard

OfS condition E10 tightens subcontracting requirements

From the Archive

Scheduling Challenges for Education Students

Challenges of Collaborative Learning and Its Assessment

Definitions of Fairness in Machine Learning, Explained Through Examples

Practical Takeaway

Review one recent set of misconduct or assessment comments and mark where students describe confusion, workload pressure, unclear collaboration rules or low confidence. Those categories point to different interventions.

Full episode page: https://www.studentvoice.ai/podcast/episodes/003-confusion-workload-and-the-real-drivers-of-misconduct/

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The episode covers research on teaching evaluation participation, postgraduate feedback, OfS student pulse results and QAA assessment literacy guidance.

In This Episode

• Why shortening a survey is rarely enough to change student participation.
• What an experiment with 1,061 undergraduates suggests about incentives and impact messaging.
• How postgraduate feedback changes when students see themselves more like customers.
• What OfS pulse survey findings say about current student experience pressure points.
• How QAA assessment literacy guidance links expectations, assessment and feedback.
• How to think about response rates as a value-exchange problem.

Student Voice Practice

March is when module evaluations, NSS messaging and committee evidence needs collide. The episode looks at what happens when feedback collection meets operational reality.

Research Spotlight

What gets students to fill in teaching evaluations? Evidence on incentives and messaging

From student to customer: what changes in postgraduate feedback

Sector Watch

OfS student pulse survey results, what universities should do now

QAA assessment literacy toolkit, aligning expectations to improve student feedback on assessment

From the Archive

Student voices in evaluation - motivations and perceptions

The important role of student voice in curriculum design

Impacts of peer tutoring on academic performance

Practical Takeaway

Before shortening another questionnaire, test whether students can see what changed last time they gave feedback. If they cannot, the issue may be credibility rather than survey length.

Full episode page: https://www.studentvoice.ai/podcast/episodes/002-response-rates-are-not-a-shortening-problem/

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The episode connects research on student resilience with OfS NSS promotion guidance, corrected TEF dashboard calculations, and the practical question of how universities read student comments about clarity, difficulty and support.

In This Episode

• Why resilience is shaped by course design, not just wellbeing services.
• How active learning and clear goals can reduce avoidable ambiguity for students.
• What students mean when they talk about teaching excellence.
• Why NSS promotion needs careful, neutral handling.
• What institutions should check after OfS corrected TEF dashboard calculations.
• A simple way to separate comments about clarity, difficulty and support.

Student Voice Practice

Stuart reflects from the HESPA conference on how university planning, administration and student voice evidence meet in practice.

Research Spotlight

Active learning and clear goals are linked to stronger student resilience

What Students Really Mean by Teaching Excellence

Sector Watch

OfS updates NSS promotion guidance, avoiding inappropriate influence in 2026

OfS corrects TEF data dashboard calculations, what institutions should check in student experience evidence

From the Archive

What is Student Voice? What is Student Voice AI?

Are accounting students overloaded and under-supported?

Do biology students want different assessment methods?

Practical Takeaway

Try a two-question clarity check on one course or module: do students know what good work looks like, and do they know where to go when expectations feel unclear? Then act on one concrete finding.

Full episode page: https://www.studentvoice.ai/podcast/episodes/001-resilience-is-not-a-wellbeing-service-it-is-a-teaching-design-outcome/

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