Short answer: yes, with limits, and not on its own. AI can read a written answer, a transcript, or a file and produce a defensible score against a rubric. It cannot be trusted as the only decision-maker, and anyone who tells you otherwise is selling you risk.
Here is what the technology actually does well in 2026, where it falls down, and how to use it without getting burned.
What AI grading does well
Modern language models are good at a specific task: comparing a piece of writing against a set of stated criteria and explaining the comparison. When the rubric is clear, the model can:
- Find evidence. It can point to the exact sentence where a candidate addressed a criterion, or note that they never did. This is the part that saves assessors the most time.
- Apply a consistent standard. A tired human marking the fortieth submission drifts. The model applies the same rubric to submission one and submission four hundred.
- Handle multiple formats. A spoken answer transcribed, a written case study, a code sample, an uploaded document - the model can read across them against the same criteria.
- Draft feedback. It can write the first version of the feedback comment, which the assessor then edits rather than writes from scratch.
Where it falls down
The failure modes are real and you need to design around them:
- Confident wrong answers. A model can produce a clean, plausible score that is simply incorrect, especially on edge cases or unusual answers. It does not signal doubt the way a human marker does.
- Vague rubrics. Garbage rubric, garbage grade. If your criteria say "demonstrates good understanding", the model will guess at what "good" means, and so will every human, but the model guesses silently.
- Context it cannot see. The candidate referenced a class discussion, a workplace incident, or a prior submission. The model only has what is in front of it.
- Gaming. Candidates learn to stuff answers with rubric keywords. A model that pattern-matches on keywords gets fooled. Evidence-based scoring helps, but a human catches the genuinely empty answer faster.
- Judgement calls. Borderline pass or fail, partial credit, "they got the wrong answer but for an interesting reason" - these need a person.
The non-negotiable: human sign-off
For any assessment that affects a qualification, a job, or a certification, a solely-automated decision is the wrong design. It is bad practice, and under frameworks like the EU AI Act it can also be a regulatory problem. The right pattern is the model does the heavy reading and proposes a score with cited evidence, then a qualified person reviews and signs off. The human stays accountable; the AI removes the grind.
This is not a compromise. It is faster than pure manual marking and more reliable than pure automation. The assessor spends their time on the calls that need judgement instead of reading every word of every submission cold.
How to use AI grading without getting burned
- Write a rubric the model can actually use. Specific criteria, observable performance levels, no fuzzy adjectives. We cover this in detail in our guide on how to write an assessment rubric AI can grade against.
- Demand cited evidence. A score with no reasoning is unauditable. Every score should point back to what in the submission justified it.
- Keep a human in the loop. Always, for anything consequential. See why a person still has to sign off.
- Check the compliance footprint. Where is the data stored, what is retained, can you produce an audit trail. More on this in AI grading and the EU AI Act.
What this looks like in practice
Scorafy is built around exactly this pattern. It reads the open-ended answer, scores it against your rubric, cites the evidence for each score, and routes it to a qualified assessor who reviews and signs off before the result is final. The point is not to replace the assessor. It is to give them a strong first pass and a clear audit trail, so they spend their judgement where it counts.
If you grade open-ended work and want to see whether AI can take the first pass on yours, book a demo with a real submission and a real rubric.