The problem
Edtech developers often wonder if the AI-generated feedback they’re delivering to students is actually good coaching. Feedback can be warm but generic, accurate but overwhelming, clear but off-task.
Feedback quality encompasses multiple dimensions, making a single quality score for a piece of feedback misleading. As AI-generated feedback enters classrooms at scale, these dimensions can fail independently and quietly.
What we’re building
Our feedback evaluators surface feedback quality as a multidimensional profile, rather than a verdict. Edtech developers can see the qualities that a piece of AI-generated feedback exhibits, which it’s missing, and what to adjust. This helps them measure, monitor, and improve the quality of educators’ feedback to students at scale.
Our feedback evaluators are anchored in the Productive Coaching rubric developed by Quill.org ↗ and Leanlab Education ↗, in partnership with Anastasiya A. Lipnevich ↗.
Scope and limitations
Feedback evaluator outputs should not be used for high-stakes applications
like grading, assessment, or placement decisions without human review.Remember that LLM scores can vary across runs, especially on borderline cases.
We recommend keeping a human in the loop and treating outputs as directional
signals vs. definitive judgments.
