The Challenge

A leading education organization runs a rigorous review process to evaluate professional learning providers for inclusion in a national guide, which is used by schools and districts. These evaluations influence which programs will shape the professional growth of thousands of educators. The process is manual and human expert led, requiring multiple reviewers and large time investments. Good decisions depend on nuanced judgments about the quality of learning experiences, not just a checklist of criteria.

Our Approach

They needed a custom AI solution that could reduce workload while preserving the human judgment at the heart of the process. Using the TRACE AI System, we built an AI model that learns from and adapts to expert reasoning, capturing not just what reviewers score, but how they interpret quality, fairness, and impact.

Modeled expert reasoning – Ingested hundreds of pages of non-standard application content, mapped them to detailed rubrics, proposed scores with direct evidence and citations, and flagged low-confidence cases for human review
Preserved provenance and control – Linked every score and rationale to traceable sources, confidence levels, and a full audit trail. Hence enabling reviewers to verify, edit, or override with complete transparency
Revealed behavioral patterns – Identified how different reviewers interpret quality, surfaced potential bias or drift, and flagged inconsistencies for correction

The Outcome

We built a workstream-ready behavioral model that doesn't replace reviewers - it amplifies them. Reviewers spend less time on repetitive scoring and more on the nuanced, human-experience-driven judgments that truly matter.