AI essay scoring uses large language models to evaluate writing against a rubric — not deciding whether an essay is "good" in some abstract sense, but evaluating specific defined criteria. When calibrated with sample essays from your institution, the AI learns what each performance level looks like in your specific context.
It provides a recommended score per criterion with confidence levels and detailed reasoning. It does not replace faculty judgment — it handles the labor-intensive first pass. It does not understand meaning the way humans do — it excels at pattern recognition for demonstrable skills. It is not infallible, but errors come with transparency (confidence scores and reasoning) that allow faculty to identify and correct them.
When properly calibrated, AI scoring shows agreement with faculty comparable to inter-rater agreement between human scorers. The key variable is calibration: 2-3 representative sample essays per performance level with written explanations of why they belong at each level. Use real anonymized student work. Recalibrate when rubrics change.
Faculty define the rubric and provide calibration (setting the standards). Faculty review AI recommendations, focusing on low-confidence and flagged submissions. Faculty interpret results and make decisions about curriculum. The override rate is a quality indicator: 5-10% suggests good calibration; 30%+ means calibration needs improvement.
"This devalues faculty expertise" — The opposite: faculty expertise is applied where it has the most impact. "Students deserve human readers" — For program assessment, students typically never see individual scores; the purpose is program-level analysis. "What about creative writing?" — AI is weakest here; rubric criteria may need to account for unconventional approaches explicitly.
Read about streamlining writing outcome assessment for accreditation.