Student Success Prediction
AI that identifies at-risk students before they fail or drop out. These systems analyze academic and behavioral data to forecast struggles, explain root causes, and recommend interventions—adapting to each learner. The result: higher retention, closed achievement gaps, and personalized support at scale.
The Problem
“You’re discovering at-risk students after they fail—because signals are scattered and manual”
Organizations face these key challenges:
1
Advisors/counselors can’t triage thousands of students; outreach happens only after grades drop
2
Risk signals live in separate systems (SIS, LMS, attendance, tutoring), making a single view hard
3
Interventions are inconsistent—depends on which teacher/advisor notices and how they respond
4
Dashboards are backward-looking; by the time reports are reviewed, the student has disengaged