Give the draft a clear job
Start with the learning goal, the information students will have and the kind of response you want. Specify the audience and any important constraints. A focused request is easier to review than a request for an entire course of materials.
Treat the output as a draft. A fluent explanation can still contain an incorrect answer, an ambiguous question or a reference that does not exist.
Review a question before its presentation
Solve the task yourself or check it against an authoritative source. For multiple choice, check that the intended answer is correct and that the alternatives are plausible without accidentally becoming correct too.
For a written response, prepare a marking guide that recognises reasonable alternative answers. Avoid a guide that rewards matching one generated paragraph instead of demonstrating the intended understanding.
Keep feedback specific and reviewable
If AI is used to suggest feedback, compare the suggestion with the student’s actual work and the agreed criteria. Check that it identifies a real feature of the response and gives a useful next step.
Do not introduce student information into an AI tool without the permissions and data arrangements required by your school.
- Check facts, calculations and source references.
- Look for hidden assumptions or missing information.
- Review reading demand and access needs.
- Match the answer notes to the task you will actually use.
- Decide what the student should do with the feedback.
Know which part is automated
When evaluating a product, ask the provider to demonstrate what happens between a prompt and the final output. Check whether responses are generated live, drawn from examples or prepared in advance.
Ask who can change a mark, whether a teacher can stop release and how a correction appears to a student. These are operational questions, not matters that a polished screenshot can settle.
What GoHiMark’s public examples show
The public example library uses curated questions and fixed answer notes. Editing a question does not regenerate its answer. That makes it useful for a question-design exercise, but it should not be mistaken for a live AI marking service.
The wider product is in development. Evaluate any future AI-assisted workflow through a specific demonstration and an agreed school use case.
Further reading
These sources offer further context. Links do not imply endorsement of GoHiMark.
Explore the question examples or estimate a task with the marking workload planner.