Intervention Agent
Spots disengagement early and drafts check-ins before students fail.
The Intervention Agent monitors logins, missing submissions, grade trends, and participation patterns across your roster. When a student slips, it drafts a personalized check-in — with data citations — and queues it for your approval before anyone falls through the cracks.
Typical time saved: 3× faster outreach
What the agent watches
Engagement patterns
Login frequency, time on task, and participation drops.
Grade trajectories
Declining averages and missed assignments by student.
Comparative signals
Students diverging from their own baseline — not arbitrary cutoffs.
Draft interventions
Suggested messages and in-class actions with rationale.
You decide who hears from you
- —Review every draft before it sends to students or families
- —Snooze or dismiss alerts with a reason logged
- —Escalate to counselors with one-click summaries
- —Set sensitivity thresholds per course
The challenge
You notice too late
By the time a student fails the unit test, they've been disengaged for weeks. Spotting quiet strugglers in a roster of 150 is nearly impossible without data you don't have time to compile.
How this agent helps
Proactive support, not reactive rescue
The Intervention Agent surfaces who needs attention while there's still time — with draft messages that reference real data, not generic concern.
In practice
When this agent runs
Monday morning
See who didn't log in over the weekend — with a draft check-in ready.
Mid-unit slump
Catch the quiet disengagers before the summative.
New transfer student
Track onboarding engagement against class norms.
Built-in capabilities
Configurable thresholds
Set what "at-risk" means for your course and context.
Student risk profiles
Holistic view: academic, engagement, and attendance combined.
Suggested actions
Message, office hours invite, or accommodation review — not just alerts.
Counselor handoff
Export a summary packet for support staff in one click.
Weekly digest
Morning brief of flagged students before first period.
Privacy-safe aggregation
FERPA-compliant; no student data used for model training.
Workflow
How the agent runs
Agent monitors continuously
Grades, logins, and submissions feed risk models in real time.
Flags with context
Each alert explains why — with suggested next steps.
You approve outreach
Edit the draft, send, or schedule a follow-up.
“I caught three students slipping two weeks before the unit test. That never happened when I was guessing.”
Maya Kapoor
Biology Teacher · Lincoln University
FAQ
Common questions
The agent uses data your school already collects in the LMS — surfaced to help you support students, not to punish them.
Works alongside
Other agents in the Ember stack.