Structured Prompts,
ML-Analyzed
Short, structured written prompts give every student a clear way in, and the same rubric-aligned machine learning pipeline that scores spoken responses, reads what they write. You get the answer, plus the thinking.
What The Models See In
A Written Reflection
Every reflection is scored the way a trained human rater would score it; against observable rubric levels, not sentiment. Here's a simulated response with the evidence the models pick up.
“At first I thought my idea was better, but when Maya explained the shadow part I realized she was thinking about where the Earth is, and I was only thinking about the Moon. Last month I would have just argued with her, but this time I asked her to draw it. Now I want to test it with the flashlight and the globe so we can see whose idea matches what actually happens.”
The student explicitly reconstructs a peer's reasoning ("she was thinking about where the Earth is") and contrasts it with their own frame — the distinguishing move between rubric levels 3 and 4.
"Last month I would have… but this time" marks self-referenced growth. The models flag this learning-progression framing as evidence of metacognitive monitoring, not just recall.
The reflection closes with a testable next step tied to evidence ("see whose idea matches what actually happens") — forward-oriented reasoning scored under curiosity and collaboration rubrics.
See Written Reflections
In Your Framework
Through our Crosswalk methodology, every dashboard reads in your district's language: Portrait of a Graduate, CTE employability skills, community schools, or wellness.