Marking with AI: what to automate and what to keep

A line between the parts of marking that are transcription and the parts that are judgement.

By Jiangke editorial · Published · Last reviewed

Marking is two jobs wearing one name. Separating them is most of the answer.

The transcription job is recording what is there: this answer is missing a unit, this paragraph has no topic sentence, this proof skips a step. Repetitive, rule-shaped, and the same across thirty scripts.

The judgement job is deciding what it means and what to say: is this student stuck on the concept or on the notation, is this the third time, does this one need pushing or reassuring.

AI is genuinely useful at the first. It is not doing the second, however fluent the output sounds.

A workflow that respects the line

  1. Mark five scripts yourself, first. You are calibrating, and you are finding out what this cohort actually got wrong — which is rarely what you predicted.
  2. Write the rubric from those five. Now it describes the real distribution of errors, not an imagined one.
  3. Use the tool for the transcription pass. Feed it the rubric and let it flag against it. Ask for observations, not grades.
  4. You write the comment. The one thing the student actually reads. This takes a fraction of the time when the observations are already listed.
  5. Spot-check. Pull five marked scripts at random and check the flags. If the tool is wrong in a patterned way, you need to know before the class does.

What not to automate

The grade. Not because a model cannot produce a number, but because the number is the part with consequences, and the accountability stays with you regardless of what produced it.

Anything a student reads unedited. A confidently wrong correction from a machine is more damaging than no feedback, because it carries authority the student has no way to challenge.

The pattern across the class. "Nineteen of them made this mistake, so I taught it badly" is the single most valuable output of marking. That insight comes from you reading, not from a summary.

The data question, which comes first

All of the above assumes you have an answer about where student work goes. Pasting a class set of essays into a personal consumer account is not a workflow, it is a disclosure. Sort out the tooling question with whoever runs procurement before you sort out the marking question.

How we tested

A method piece based on teaching practice and on how current tools behave in general. No product has been tested against real student work by Jiangke, and doing so would raise the data questions described in the piece.

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