AI Feedback Needs Teacher Judgement and Better Design

What this episode covers

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/

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