For over a decade, Starfish (now part of EAB Navigate) has been the default student success platform at universities. Faculty flag students, advisors follow up, cases get tracked in a spreadsheet-like interface. It works — but it relies entirely on humans noticing problems.
The Gap in Traditional Early Alert Systems
The fundamental problem with manual early alert systems is timing. By the time a professor flags a student after a failed midterm, six weeks have passed. The student has already disengaged. The intervention comes too late.
Traditional systems also suffer from faculty adoption issues. If a professor doesn't use the platform, their students are invisible to the system. And in large universities, the volume of flags can overwhelm advising staff.
What AI-Powered Student Success Looks Like
The next generation of student success platforms uses AI to continuously monitor engagement signals — not grades, but leading indicators:
- Activity patterns — Is the student logging in? Engaging with course tools?
- Reflection completion — Are they completing self-assessments?
- AI chat usage — Are they using available learning resources?
- Engagement trends — Is activity declining over time?
When the AI detects a pattern of disengagement, it automatically creates a concern with a severity level (high, medium, low) and surfaces it in a faculty triage inbox — before the midterm, not after.
From Flagging to Case Management
Modern platforms don't just flag students — they provide a complete case management workflow: Open → Acknowledged → Monitor → Resolved. Faculty can log intervention notes, track contact attempts, and see evidence timelines. Everything is auditable and FERPA-compliant.
The Shift
The universities that are moving fastest on this aren't replacing their advising staff — they're giving them better tools. AI handles the detection. Humans handle the intervention. That's the right division of labor.