Providing Feedback with AI Support

AI can help an educator draft, organise, or vary feedback. It should not become the assessor. Effective feedback remains grounded in the student’s actual work, the intended learning outcomes, and professional judgment.

What useful feedback should do

Strong feedback helps a learner answer three questions:

• Where am I going? Restate the relevant goal or criterion.
• How am I doing? Point to specific evidence in the work.
• What should I do next? Give a feasible action the student can apply.

Feedback is more useful when it addresses the task and the learner’s process, not presumed personality or ability. It should prioritise a small number of changes rather than overwhelm the student with every possible correction. Research syntheses show that feedback effects vary substantially with its content and delivery; more feedback is not automatically better.^{1 2}

A safe drafting workflow

Copyable prompt for a feedback draft

Use this only in an environment approved for the material you provide.

You are helping me draft formative feedback. I remain the assessor and will
review every comment.

<assignment>
[assignment instructions]
</assignment>

<learning_outcomes_and_criteria>
[relevant learning outcomes and rubric criteria]
</learning_outcomes_and_criteria>

<student_work>
[authorised student work or excerpt]
</student_work>

For each of the two highest-priority points:
1. name the relevant criterion;
2. identify the exact passage or feature in the work that supports the comment;
3. describe one strength or successful choice, where supported;
4. propose one specific next action; and
5. offer one question that could help the student revise independently.

Do not assign a grade. Do not infer motivation, effort, personal characteristics,
misconduct, or AI authorship. Do not introduce requirements absent from the
assignment or criteria. If the evidence is insufficient, write "insufficient
evidence" and explain what I should inspect. Return a concise table.

Human review checklist

Before sending the feedback, confirm:

• every observation points to evidence in this student’s work;
• the advice follows the published task, learning outcomes, and rubric;
• any factual or disciplinary claim has been independently verified;
• the tone is clear, respectful, and appropriate for the learner;
• the priorities are achievable within the time and support available;
• the feedback does not infer protected characteristics, intent, or authorship;
• the grade, if any, was determined and checked by the responsible educator;
• the student can ask for clarification or challenge a factual misreading; and
• the feedback creates a chance to act, revise, or transfer learning.

Evaluate the workflow

Test the process on a small, representative sample before wider use. Compare the AI-supported draft with educator-only feedback and inspect both false criticisms and missed issues. Check whether quality differs by language variety, topic, or student group. Record the time saved after review—not just draft-generation time—and stop if the workflow weakens feedback quality or fairness.

Early studies of LLM-generated feedback show potential in particular settings, but this is still a developing evidence base and results do not transfer automatically across subjects, tools, or student groups.³

References & Footnotes