Faster Feedback, Still Human-Approved
WISEFLOW AI ASSISTED FEEDBACK · ADD-ON PRODUCT
Good Feedback Is One of the Strongest Drivers of Learning
Feedback is where students actually learn why they got the grade they did — not just what the grade was. Institutions are under increasing pressure to deliver it consistently: clear, timely feedback and justification that hold up to scrutiny from students, external examiners, and appeals boards alike, and that follow the same standard regardless of which marker wrote them.
But Writing It Doesn't Scale for Educators
Institutions face growing pressure to deliver high-quality, timely feedback, while educators juggle heavy workloads — and writing thorough feedback for every student is often the first thing to slip.
- Turnaround time slips when markers are stretched across dozens or hundreds of submissions.
- Explanation depth suffers, with less detail than students actually need.
- Consistency across markers breaks down, with some students getting more thorough feedback than others.
WISEflow AI Assisted Feedback was built for this reality — a faster starting point, not a replacement for academic judgement.
AI Drafts the First Version, Educators Approve Every Word
WISEflow AI Assisted Feedback drafts a first version of the grade justification, grounded only in the student's own submission and your marking criteria. The educator then reviews, edits, and decides exactly what gets published — nothing goes out unreviewed, and AI never sets the grade or has the final word.
A structured first draft based on the student's work and your marking criteria, so educators never start from a blank page.
Students get more than a number: a specific explanation of what drove the grade and what to focus on next time.
Feedback follows the same structure and level of detail across the whole grading or assessor team, making it clearer for students and easier to document.
In pilot testing, markers generally spent significantly less time per submission.
Students receive feedback while it's still useful for their learning, not weeks later.
Nothing Runs Without Safeguards for Privacy and Academic Integrity
- Human in the loop control — educators remain fully in charge: they can refine, rewrite or extend the AI suggestions before publishing anything to students.
- A closed circle — the AI works only from the assessment criteria, the assignment, the grade, and the student's own submission, and nothing else.
- Never the final word — it only runs after marking and grading have already been decided, so it can never influence the result it is meant to support.
- EU-hosted & GDPR-aligned — runs on EU-hosted infrastructure with a defensible audit trail, so supporting the marker never comes at the cost of student privacy or intellectual property.
- Explainable by design — how the AI reached a suggestion, and what it drew on, can always be surfaced and understood — never left as a black box.
See how UNIwise approaches security, privacy, and compliance across all products in our Trust Centre.
RELATED ARTICLES
See how AI Assisted Feedback performs in practice, and how UNIwise thinks about AI's role in assessment.
- AI Assisted Feedback: Findings from a pilot in two master's courses at UiT — What happens when AI-assisted feedback meets technical, quantitative coursework: real hallucinations, full human review, and the fixes now underway.
- Assist, don't dictate: where AI belongs in assessment - and where it doesn't — Why UNIwise has deliberately chosen to let AI assist markers, never decide for them.
- AI-assisted Feedback – Findings from a second, formative pilot at UiT — How a formative redesign solved the timing problem students raised in the first pilot, with 13 of 14 students finding the feedback useful.
- AI Assisted Feedback in WISEflow at UiT – First pilot — The original small-scale pilot in dentistry education that set the baseline for quality, consistency, and the case for formative use.
- AI, assessment & trust in higher education — Reflections on the Norwegian Expert Committee's preliminary report and what it means for trust in AI-assisted assessment.
LET'S TALK ABOUT YOUR FEEDBACK WORKFLOW
See how AI Assisted Feedback fits into your existing marking process, what it costs, and what it would take to bring it to your institution.
COMMON QUESTIONS FROM HIGHER EDUCATION INSTITUTIONS
It generates a structured first draft of feedback or grade justification based on the student's submission and assessment criteria. This reduces the amount of repetitive writing involved, allowing educators to focus their time on refining and approving the final version rather than starting from scratch.
No. Educators remain fully in control. The AI only provides a starting point, and all feedback must be reviewed, edited, and approved by a human before it is published. This ensures academic standards and transparency are upheld.
Because the draft is generated based on the institution's own marking criteria, the module helps ensure that explanations follow the same structure and level of detail. This reduces variation between assessors and supports clearer communication for students.
Yes, particularly on technical or quantitative work — pilot testing found real hallucinations in first-pass output, such as a misread calculation. This is exactly why every AI-generated response is reviewed by the educator before it reaches a student; no output is published unscreened.
No. The AI only runs after marking and grading have already been decided, and only assists with feedback and justification — never with setting the grade itself. The human marker stays in control of, and accountable for, the decision.
Yes. Unlike pasting student work into a general-purpose chatbot, WISEflow AI Feedback runs on EU-hosted, GDPR-aligned infrastructure, works only within a closed circle of the assignment, criteria and submission, and keeps a defensible audit trail.
Yes, in pilot testing. One professor estimated around 5 minutes per submission with AI support, against a 20-minute baseline without it — even while personally screening every AI-generated response before release. On more technical, quantitative coursework, two course coordinators still cut grading time by roughly a third despite a rockier start.
Yes. Transparency is a core design principle: when the AI is active, it is visible and traceable to everyone involved — the institution, the marker, and the student — rather than presented as if it came from the educator alone.
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