The Student Voice Weekly

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/

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/

This week, Dr Stuart Grey discusses belonging and student voice evidence: why international wellbeing, friendship formation, and collaborative study are shaped by ordinary academic contact rather than slogans or one-off events. 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: https://www.studentvoice.ai/blog/everyday-community-contact-shapes-international-student-wellbeing/ - Friendships and collaborative study shape belonging more than extracurricular activity: https://www.studentvoice.ai/blog/friendships-and-collaborative-study-shape-belonging-more-than-extracurricular-activity/ Across the Sector - DfE franchise arrangements guidance raises the stakes for student feedback evidence: https://www.studentvoice.ai/blog/dfe-franchise-arrangements-student-feedback-evidence/ - Jisc Online Surveys adds a "None of the above" option, and why it matters for student feedback data quality: https://www.studentvoice.ai/blog/jisc-online-surveys-none-of-the-above-student-feedback-data-quality/ From the Archive - Does nursing course content deliver breadth and relevance?: https://www.studentvoice.ai/blog/course-content-in-nursing-education-in-the-uk/ - Architecture students on personal development: https://www.studentvoice.ai/blog/architecture-students-on-personal-development/ - Student voice in the development of assessment practices: https://www.studentvoice.ai/blog/the-benefit-of-student-voice-in-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/ Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses AI feedback and student voice evidence: how universities can use AI feedback without removing teacher judgement, and how assessment evidence connects with student value, belonging and attendance. 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: https://www.studentvoice.ai/blog/students-value-ai-feedback-most-when-teacher-judgement-stays-in-the-loop/ - Paid student voice roles can make representation more accountable: https://www.studentvoice.ai/blog/paid-student-voice-roles-can-make-representation-more-accountable/ Across the Sector - QAA national review: awarding arrangements and student voice evidence: https://www.studentvoice.ai/blog/qaa-national-review-awarding-arrangements-student-voice-evidence/ - Student Academic Experience Survey: student value, belonging and attendance: https://www.studentvoice.ai/blog/student-academic-experience-survey-student-value-belonging-attendance/ From the Archive - Computer science students' views on Covid-19 challenges: https://www.studentvoice.ai/blog/computer-science-students-views-on-covid-19-challenges/ - Navigating university life: insights from education students: https://www.studentvoice.ai/blog/navigating-university-life-insights-from-education-students/ - English literature students' perspectives on Covid-19: https://www.studentvoice.ai/blog/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/ Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses feedback evidence: how universities move beyond headline scores and into the student comments, context and action trail underneath them. 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 even when survey scores stay flat: https://www.studentvoice.ai/blog/feedback-literacy-can-develop-even-when-survey-scores-stay-flat/ PhD competency confidence varies by research style: evidence from 1,105 doctoral students: https://www.studentvoice.ai/blog/research-styles-phd-competencies/ Sector Watch OfS requires clearer assessment feedback at Northampton, and why student voice evidence matters: https://www.studentvoice.ai/blog/ofs-northampton-assessment-feedback-student-voice-evidence/ NSS 2026 has closed, and what universities should do before the July results: https://www.studentvoice.ai/blog/nss-2026-has-closed-what-universities-should-do-before-the-july-results/ From the Archive Does personal development in business studies improve outcomes?: https://www.studentvoice.ai/blog/personal-development-in-business-and-management-studies/ Are medical technology students let down by communications?: https://www.studentvoice.ai/blog/navigating-the-waves-medical-technology-students-perspectives/ Inverted learning: turning traditional teaching methods upside-down: https://www.studentvoice.ai/blog/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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses belonging, partnership and timing: why welcome week is not enough to be a university's belonging strategy. 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: https://www.studentvoice.ai/blog/welcome-week-attendance-boosts-peer-belonging/ Student Voice as Partnership, Not Extraction: https://www.studentvoice.ai/blog/student-voice-as-partnership-not-extraction/ Sector Watch Portsmouth's assessment regulation changes show how student feedback can reshape assessment rules: https://www.studentvoice.ai/blog/portsmouth-assessment-regulation-changes-student-feedback/ University of Westminster's Mid-Module Check-ins show what earlier module feedback can look like: https://www.studentvoice.ai/blog/westminster-mid-module-check-ins-earlier-module-feedback/ From the Archive Student perspectives on HRM course content: https://www.studentvoice.ai/blog/student-perspectives-on-hrm-course-content/ Obstacles to students voice in curriculum design: https://www.studentvoice.ai/blog/obstacles-to-students-voice-in-curriculum-design/ King's Wellbeing Survey, and why joined-up student feedback matters: https://www.studentvoice.ai/blog/kings-wellbeing-survey-joined-up-student-feedback-system/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses engagement as a design problem. Weak engagement often tells universities something about the conditions they have created, not simply something about student motivation. 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: https://www.studentvoice.ai/blog/student-engagement-depends-more-on-institutional-design-than-student-background/ Students use Generative AI for feedback, but trust teachers more: https://www.studentvoice.ai/blog/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: https://www.studentvoice.ai/blog/glasgow-assessment-feedback-tool-student-voice/ Wonkhe's AI assessment report shows how late feedback drives student AI use: https://www.studentvoice.ai/blog/wonkhe-ai-assessment-report-late-feedback-student-ai-use/ From the Archive What do art students need from learning resources?: https://www.studentvoice.ai/blog/art-students-views-on-learning-resources/ King's College London partners with Student Voice AI: https://www.studentvoice.ai/blog/student-voice-and-kings-college-london-2025/ Do adult nursing students feel their courses support personal development?: https://www.studentvoice.ai/blog/adult-nursing-students-views-on-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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses GenAI disclosure as a trust problem. Students are more likely to disclose AI use when governance feels fair, consistent and credible in real teaching practice. 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: https://www.studentvoice.ai/blog/students-disclose-ai-use-when-governance-feels-fair/ Who Actually Fills In Student Evaluations? New Evidence on Non-Response Bias: https://www.studentvoice.ai/blog/who-fills-in-student-evaluations-non-response-bias/ Sector Watch QAA Subject Benchmark Statements, and what they mean for student feedback evidence: https://www.studentvoice.ai/blog/qaa-subject-benchmark-statements-student-feedback-evidence/ QAA's GenAI assessment focus groups show why student voice on AI needs more structure: https://www.studentvoice.ai/blog/qaa-genai-assessment-focus-groups-student-voice/ From the Archive Student Voice AI + evasys + Advance HE for PTES & PRES 2025: https://www.studentvoice.ai/blog/student-voice-ai-evasys-advancehe-ptes-pres-2025/ What did COVID-19 mean for business and management students?: https://www.studentvoice.ai/blog/business-and-management-students-experiences-of-covid-19/ Do history students benefit from clearer assessment methods?: https://www.studentvoice.ai/blog/evaluating-assessment-methods-in-history-courses/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses belonging and retention: why one belonging score is rarely enough to explain who feels connected, who is drifting and what universities should do next. 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: https://www.studentvoice.ai/blog/retention-work-needs-belonging-evidence/ Induction can build mature students' belonging, but only if universities design beyond the default student: https://www.studentvoice.ai/blog/induction-can-build-mature-students-belonging/ Sector Watch DMU's block teaching evaluation links faster feedback with stronger survey evidence: https://www.studentvoice.ai/blog/dmu-block-teaching-evaluation-student-survey-evidence/ Jisc will retire Digital Experience Insights on 31 July 2026: https://www.studentvoice.ai/blog/jisc-digital-experience-insights-retirement-student-feedback-benchmarking/ From the Archive Gamification in statistics teaching: https://www.studentvoice.ai/blog/gamification-in-statistics-teaching/ Do accounting students value a balanced mix of assessment methods?: https://www.studentvoice.ai/blog/student-views-on-assessment-methods-in-accounting-education/ Do extracurricular activities genuinely benefit law students?: https://www.studentvoice.ai/blog/law-students-views-on-extracurricular-activities/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses why student evaluation reports do not improve teaching on their own. The improvement happens in the conversations staff and students have after the data is collected. 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: https://www.studentvoice.ai/blog/student-evaluations-help-teaching-improve-when-staff-can-discuss-them/ Belonging surveys need better validation before universities benchmark them: https://www.studentvoice.ai/blog/belonging-survey-validation-before-benchmarking/ Sector Watch UUK's five quality principles put student feedback evidence at the centre of efficiency decisions: https://www.studentvoice.ai/blog/uuk-five-quality-principles-student-feedback-evidence/ Jisc Online Surveys changes question types, and why it matters for student feedback survey design: https://www.studentvoice.ai/blog/jisc-online-surveys-question-types-student-feedback-design/ From the Archive What is Student Voice?: https://www.studentvoice.ai/what-is-student-voice/ Are accounting students overloaded and under-supported?: https://www.studentvoice.ai/blog/accounting-students-challenges-with-workload-and-support/ Do biology students want different assessment methods?: https://www.studentvoice.ai/blog/assessment-methods-in-biology-education/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses evaluation design: why staff and students need to co-design feedback instruments before anyone can fully trust the data they produce. 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: https://www.studentvoice.ai/blog/student-evaluations-improve-when-staff-and-students-redesign-them-together/ Student evaluation scores are not automatically comparable across departments, programmes, or time: https://www.studentvoice.ai/blog/student-evaluation-scores-not-automatically-comparable/ Sector Watch OfS quality assessment flags missing module evaluations and student surveys at King Stage Limited: https://www.studentvoice.ai/blog/ofs-quality-assessment-missing-module-evaluations-king-stage/ Westminster's PTES 2026 launch shows how survey incentives and confidentiality shape postgraduate feedback: https://www.studentvoice.ai/blog/westminster-ptes-2026-survey-incentives-postgraduate-feedback/ From the Archive Do extracurriculars enhance psychology students' experience?: https://www.studentvoice.ai/blog/enhancing-the-university-experience-a-deep-dive-into-extracurricular-activities-for-psychology-students/ Do electrical engineering students prioritise hands-on learning?: https://www.studentvoice.ai/blog/understanding-electrical-and-electronic-engineering-students-perspectives/ The Best Text Analysis Software for Education: https://www.studentvoice.ai/resources/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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses quality assurance visibility: why students often assume nothing changes when the feedback system is hard to see. 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: https://www.studentvoice.ai/blog/students-see-accreditation-work-when-quality-assurance-is-visible/ Student survey data works better when universities benchmark and triangulate it: https://www.studentvoice.ai/blog/student-survey-benchmarking-triangulation-quality-improvement/ Sector Watch QAA research on student representation practices across 78 UK institutions: https://www.studentvoice.ai/blog/qaa-student-representation-practices-student-feedback-systems/ University of Glasgow launches a Student Voice Framework: https://www.studentvoice.ai/blog/university-of-glasgow-student-voice-framework-student-feedback-governance/ From the Archive What are accounting students telling us about university life?: https://www.studentvoice.ai/blog/accounting-students-perspectives-on-university-life/ Do structured collaborations improve learning for history students?: https://www.studentvoice.ai/blog/collaborative-opportunities-for-history-students/ Lancaster University partners with Student Voice AI: https://www.studentvoice.ai/blog/student-voice-and-lancaster-university-2025/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses what to do when student evaluation scores all move together. Halo effects and bias change what the data can tell you, but they do not make student feedback useless. 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: https://www.studentvoice.ai/blog/halo-effects-in-the-student-voice/ Gender stereotypes shape who gets recognised for teaching excellence: https://www.studentvoice.ai/blog/gender-stereotypes-and-perceived-teaching-excellence/ Sector Watch King's launches a wellbeing survey positioned alongside NSS and PTES: https://www.studentvoice.ai/blog/kings-wellbeing-survey-joined-up-student-feedback-system/ Glasgow rolls out MyGrades university-wide after acting on student feedback: https://www.studentvoice.ai/blog/university-of-glasgow-mygrades-student-feedback-system/ From the Archive Are adult nursing students' workloads manageable?: https://www.studentvoice.ai/blog/the-challenges-of-workload-for-adult-nursing-students/ Oral examination as an online assessment tool: https://www.studentvoice.ai/blog/oral-examination-as-an-online-assessment-tool/ Can online psychology match the on-campus experience?: https://www.studentvoice.ai/blog/studying-psychology-online-a-detailed-examination/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses academic misconduct, workload and feedback timing: why integrity cases often point upstream to unclear assessment design, deadline pressure and uneven support. 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: https://www.studentvoice.ai/blog/what-3070-misconduct-reflections-reveal-about-academic-integrity-policy/ Faster feedback policies do not guarantee better NSS results: https://www.studentvoice.ai/blog/faster-feedback-policies-do-not-guarantee-better-nss-results/ Sector Watch OfS publishes latest TEF data dashboard: https://www.studentvoice.ai/blog/ofs-publishes-latest-tef-data-dashboard-student-experience-evidence/ OfS condition E10 tightens subcontracting requirements: https://www.studentvoice.ai/blog/ofs-condition-e10-subcontracting-student-feedback-evidence/ From the Archive Scheduling Challenges for Education Students: https://www.studentvoice.ai/blog/scheduling-challenges-education-students/ Challenges of Collaborative Learning and Its Assessment: https://www.studentvoice.ai/blog/challenges-of-collaborative-learning-and-its-assessment/ Definitions of Fairness in Machine Learning, Explained Through Examples: https://www.studentvoice.ai/blog/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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses why low response rates are not mainly a questionnaire-length problem. The real issue is whether students believe the feedback process is credible, useful and worth their time. 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: https://www.studentvoice.ai/blog/what-gets-students-to-fill-in-teaching-evaluations/ From student to customer: what changes in postgraduate feedback: https://www.studentvoice.ai/blog/from-student-to-customer-what-changes-in-postgraduate-feedback/ Sector Watch OfS student pulse survey results, what universities should do now: https://www.studentvoice.ai/blog/ofs-student-pulse-survey-results-what-universities-should-do-now/ QAA assessment literacy toolkit, aligning expectations to improve student feedback on assessment: https://www.studentvoice.ai/blog/qaa-assessment-literacy-toolkit-student-feedback-on-assessment/ From the Archive Student voices in evaluation - motivations and perceptions: https://www.studentvoice.ai/blog/student-voices-in-evaluation-unpacking-motivations-and-perceptions-in-higher-education/ The important role of student voice in curriculum design: https://www.studentvoice.ai/blog/the-important-role-of-student-voice-in-curriculum-design/ Impacts of peer tutoring on academic performance: https://www.studentvoice.ai/blog/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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

This week, Dr Stuart Grey discusses resilience, teaching design and trust in student voice evidence: why clear goals, active learning and neutral survey practice matter more than another wellbeing signpost. 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: https://www.studentvoice.ai/blog/active-learning-clear-goals-student-resilience/ What Students Really Mean by Teaching Excellence: https://www.studentvoice.ai/blog/what-students-really-mean-by-teaching-excellence/ Sector Watch OfS updates NSS promotion guidance, avoiding inappropriate influence in 2026: https://www.studentvoice.ai/blog/ofs-updates-nss-promotion-guidance-avoiding-inappropriate-influence-in-2026/ OfS corrects TEF data dashboard calculations, what institutions should check in student experience evidence: https://www.studentvoice.ai/blog/ofs-corrects-tef-data-dashboard-calculations-student-experience-evidence/ From the Archive What is Student Voice? What is Student Voice AI?: https://www.studentvoice.ai/what-is-student-voice/ Are accounting students overloaded and under-supported?: https://www.studentvoice.ai/blog/accounting-students-challenges-with-workload-and-support/ Do biology students want different assessment methods?: https://www.studentvoice.ai/blog/assessment-methods-in-biology-education/ 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/ Subscribe to Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/