Human review, with an optional AI assist.
A synthetic scholarship workflow that works as a human review tool, then adds evidence-backed scoring, confidence, and uncertainty routing when the AI layer is enabled.
Human review workflow
Applications, essays, reviewer notes, manual status changes, and decision tracking remain usable when the AI layer is off.
AI-assisted prioritization
When enabled, the layer adds category scoring, evidence snippets, confidence, and low-confidence routing without replacing the reviewer.
Validation before trust
Reviewer agreement, uncertainty routing, regression cases, and drift checks define what must be monitored before trust grows.
Application queue
Toggle the AI review layer to see the same scholarship workflow before and after the AI-assisted layer is enabled.
Scholarship applications
6 applicationsReview detail
Manual decision remains human-ownedFive weighted categories
Both modes use the same categories and weights. Assisted mode proposes a score from application evidence; reviewers interpret the context and own the decision.
Keep the model out of rules
- Required field checks
- Score normalization
- Routing thresholds
- Schema validation
Use AI for messy judgment
- Essay interpretation
- Evidence extraction
- Rubric classification
- Reviewer summary drafting
Final decisions stay accountable
- Award decisions
- Ambiguous cases
- Policy exceptions
- Sensitive judgments
Validation before trust
These are evaluation methods for an assisted workflow, not measured results or claims about production performance.
Reviewer agreement
Review where AI proposals and final human judgments align or diverge, then inspect the evidence behind the disagreement.
Uncertainty
Hold ambiguous, missing-context, and underrepresented cases for a reviewer instead of treating absence as confidence.
Regression cases
Re-run representative and difficult cases when the model, rubric, or decision rules change.
Drift
Monitor changes in evidence quality, recommendation patterns, and reviewer overrides before expanding use.