The Student Voice Weekly

Dr Stuart Grey

The Student Voice Weekly is a short podcast for UK higher education leaders who want to turn student feedback, policy and research into practical action. Each week, Dr Stuart Grey, founder of Student Voice AI and Senior Lecturer at the University of Glasgow, unpacks the latest evidence on student voice, assessment, feedback, regulation and institutional improvement. Expect concise briefings on HE research, OfS and QAA developments, NSS and survey practice, and practical ways to use student comments more rigorously.

  1. 4d ago

    When participation becomes influence

    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. 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: https://www.studentvoice.ai/blog/student-voice-improves-outcomes-when-students-can-shape-teaching/ - Student engagement depends on visible care, not just teaching technique: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/sheffield-hallam-module-evaluation-local-student-voice-governance/ - OfS NSS student characteristics data adds provider typologies, but key equity splits are missing: https://www.studentvoice.ai/blog/ofs-nss-student-characteristics-data-provider-typologies-missing-equity-splits/ 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. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    When participation becomes influence

  2. Jul 17

    What QAA wants universities to do after NSS

    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. 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: https://www.studentvoice.ai/blog/student-representation-quality-assurance-needs-training-trust-usable-evidence/ Across the Sector - QAA says NSS 2026 should drive internal student voice action, not just league tables: https://www.studentvoice.ai/blog/qaa-nss-2026-internal-student-voice-action/ - QAA's short-cycle course guidance says student feedback must move faster for the LLE: https://www.studentvoice.ai/blog/qaa-short-cycle-course-guidance-student-feedback-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. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    What QAA wants universities to do after NSS

  3. Jul 10

    NSS results need more than headlines

    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. 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?: https://www.studentvoice.ai/blog/machine-learning-mid-semester-teaching-evaluations/ - High-stakes teaching evaluations can damage academic wellbeing: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/nss-2026-results-student-voice-disabled-student-gaps/ - OfS updates modular outcomes for the LLE, and why continuous student feedback matters: https://www.studentvoice.ai/blog/ofs-modular-outcomes-lle-continuous-student-feedback/ 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. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    NSS results need more than headlines

  4. Jul 3

    Student feedback should start a dialogue

    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. 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: https://www.studentvoice.ai/blog/student-evaluations-should-inform-dialogue-not-replace-academic-judgement/ - VLEs scale better when feedback and reattempt are built in: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/wonkhe-ai-feedback-analysis-nss-comments-action/ - Wonkhe's sector data warning shows why NSS and student feedback still arrive too late: https://www.studentvoice.ai/blog/wonkhe-sector-data-warning-nss-student-feedback-too-late/ From the Archive - NSS open-text analysis methodology for UK HE: https://www.studentvoice.ai/resources/nss-open-text-analysis-methodology/ - The current understanding of student voice in assessment and feedback: https://www.studentvoice.ai/blog/the-current-understanding-of-student-voice-in-assessment-and-feedback/ - How to enhance student voice in university governance: https://www.studentvoice.ai/blog/how-to-enhance-student-voice-in-university-governance-through-student-representation/ 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. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    Student feedback should start a dialogue

  5. Jun 26

    What students actually mean when they talk about AI

    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. 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: https://www.studentvoice.ai/blog/ai-attitude-surveys-need-separate-usefulness-self-expression-and-concern/ - Student mini-publics only matter if universities can show what changed: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/jisc-june-he-ai-meetup-student-feedback-guidance/ - Jisc's 'human in the loop' pilot sharpens AI governance for student feedback evidence: https://www.studentvoice.ai/blog/jisc-human-in-the-loop-ai-student-feedback-governance/ From the Archive - What learning resources do business management students say they need most?: https://www.studentvoice.ai/blog/learning-resources-for-business-management-students/ - Do extracurricular activities enhance history students' academic success?: https://www.studentvoice.ai/blog/history-students-perspectives-on-extracurricular-activities/ - What assessment methods work in therapy education?: https://www.studentvoice.ai/blog/assessment-methods-in-counselling-psychotherapy-and-occupational-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. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    What students actually mean when they talk about AI

  6. Jun 19

    Students judge AI by care, not just competence

    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. 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: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/ai-detectors-privacy-false-positives/ Across the Sector - Bath's 2026 student feedback system shows how to collect the right survey at the right level: https://www.studentvoice.ai/blog/bath-2026-student-feedback-system/ - QAA launches Assessment & Feedback Roadshow, what it means for student feedback on assessment: https://www.studentvoice.ai/blog/qaa-assessment-feedback-roadshow-student-feedback-on-assessment/ From the Archive - Do history degrees build personal development?: https://www.studentvoice.ai/blog/personal-development-insights-from-history-students/ - Does module choice shape history students' engagement and success?: https://www.studentvoice.ai/blog/module-choice-in-history-courses/ - Do peer opportunities improve learning for literature students?: https://www.studentvoice.ai/blog/collaborative-opportunities-for-english-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/ Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    Students judge AI by care, not just competence

  7. Jun 12

    AI legitimacy: students want to see the human judgement

    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. 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: https://www.studentvoice.ai/blog/students-feelings-about-ai-reveal-trust-and-belonging-risks/ - Students use assessment feedback better when universities create space for questions: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/cambridge-ai-marking-higher-education-human-judgement/ - Jisc's AI marking and feedback pilot says formative feedback is the right place to start: https://www.studentvoice.ai/blog/jisc-ai-marking-and-feedback-pilot-formative-feedback-first/ From the Archive - AI and Education - Equity Challenges and Opportunities: https://www.studentvoice.ai/blog/navigating-the-intersection-of-ai-and-education-equity-challenges-and-opportunities/ - Respect is key for successful student voice as co-creation practices: https://www.studentvoice.ai/blog/respectful-student-voice/ - Which assessment methods work best in physics?: https://www.studentvoice.ai/blog/student-perspectives-on-assessment-methods-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/ Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    AI legitimacy: students want to see the human judgement

  8. Jun 5

    Time poverty is the new hidden barrier

    This week, Dr Stuart Grey discusses time poverty and student voice evidence: how low-income students lose study time through work, travel, administration, money pressure, and systems that assume spare capacity. 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: https://www.studentvoice.ai/blog/time-poverty-creates-hidden-inequality-for-low-income-students/ - Student evaluation systems earn trust through fair process, not just fair scores: https://www.studentvoice.ai/blog/student-evaluation-systems-earn-trust-through-fair-process/ Across the Sector - Cardiff's QER review says student voice mechanisms need clearer purpose and wider reach: https://www.studentvoice.ai/blog/cardiff-qer-student-voice-mechanisms-clearer-purpose-wider-reach/ - Advance HE's TEF analysis shows student voice evidence still misses part of teaching excellence: https://www.studentvoice.ai/blog/advance-he-tef-student-voice-evidence-teaching-excellence/ From the Archive - Key elements of team teaching: https://www.studentvoice.ai/blog/successful-team-teaching-in-higher-education/ - What are media studies students telling us about course organisation?: https://www.studentvoice.ai/blog/challenges-in-media-studies-course-management/ - Are medical students' workloads manageable?: https://www.studentvoice.ai/blog/workload-challenges-faced-by-medical-students-in-higher-education/ 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/ Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    Time poverty is the new hidden barrier
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About

The Student Voice Weekly is a short podcast for UK higher education leaders who want to turn student feedback, policy and research into practical action. Each week, Dr Stuart Grey, founder of Student Voice AI and Senior Lecturer at the University of Glasgow, unpacks the latest evidence on student voice, assessment, feedback, regulation and institutional improvement. Expect concise briefings on HE research, OfS and QAA developments, NSS and survey practice, and practical ways to use student comments more rigorously.

Information

  • Creator
    Dr Stuart Grey
  • Years Active
    2026
  • Episodes
    22
  • Rating
    Clean
  • Copyright
    © Copyright 2026 Student Voice Systems Limited
  • Show Website
    The Student Voice Weekly