Live webinar of how scholarship programs are using AI to get through review cycles faster and make sharper, more consistent decisions.
Committees are volunteering their time. Applications sprawl across forms, transcripts, essays, reference letters, and portfolios. Scorecards ask questions whose answers live in four different places. And no matter how clear the rubric is, scoring drifts from reviewer to reviewer.
The result? Review fatigue, inconsistent decisions, and a nagging worry that the right applicants aren't always the ones being surfaced.
1. Applicant Summaries: Lower the Barrier to Review
Turn 40-page application packets into scannable, reviewer-ready briefs. Use them as a first-phase filter for high-volume programs, or to help any committee move faster without sacrificing context. Every claim traces back to the original submission.
2. Review Briefs: Restructure Applications Around Your Scorecard
Your scorecard already defines what matters. AI restructures each application so reviewers see all the relevant evidence — from the form, the transcript, the reference letter, everywhere — mapped directly to each scoring criterion. No more hunting. No more missed details. More consistent scores across your committee.
3. AI Pre-Scoring: Give Your Committee a Head Start
AI compares the application against your scorecard and the qualities you're looking for, then produces an initial score with reasoning for each criterion. Reviewers see the pre-score, then confirm or override with their own judgment. It's a calibration tool, not a verdict — and it flags the outlier cases both humans and AI should take a second look at.
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