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Student Success & RetentionResearch University

AI-Powered Student Success for Research Universities

Deploy purpose-built AI agents that monitor at-risk students, coordinate interventions, and scale personalized support across 15,000–60,000 students — all on your own infrastructure.

The Problem

Research universities face a retention crisis hidden in data silos. Early warning signals exist across Banner, Canvas, and advising platforms, but no single team sees the full picture in time to act.

Student success staff are overwhelmed. Advisors manage hundreds of cases manually, intervention workflows live in spreadsheets, and tutoring coordination is reactive rather than proactive.

The result: students fall through the cracks not from lack of resources, but from lack of connected, intelligent systems that can act at scale.

Late or Missed Early Alerts

Manual flag reviews mean at-risk students are often identified weeks too late. Advisors receive alerts but lack bandwidth to triage and act on all of them in time.

Only 29% of flagged students receive a timely intervention within 7 days (EAB, 2023)

Siloed Systems, Fragmented Data

Student data is scattered across Banner, PeopleSoft, Canvas, and Blackboard. No unified view means advisors spend more time gathering data than helping students.

Advisors spend up to 40% of their time on administrative data tasks

Intervention Case Management Bottlenecks

Case tracking is manual and inconsistent across departments. Without structured workflows, follow-through on interventions is unreliable and hard to audit.

60% of intervention cases lack documented follow-up (Civitas Learning, 2022)

Tutoring Coordination at Scale

Matching students to tutoring resources across a large research university is time-intensive. Demand spikes mid-semester while supply remains static and uncoordinated.

Up to 35% of students who need tutoring never access it due to friction in the process

Retention Reporting Gaps

Retention dashboards are often backward-looking and built for compliance, not action. Leaders lack real-time insight into which cohorts are trending toward attrition.

Average 6-year graduation rate at research universities is 68%, leaving significant room for improvement (NCES, 2023)

AI Capabilities

Predictive Early Alert Monitoring

AI agents continuously analyze LMS activity, grade trends, attendance, and SIS data to surface at-risk students before they disengage — triggering alerts with recommended next actions.

Automated Intervention Case Management

Structured AI-driven workflows route flagged students to the right advisor, counselor, or resource. Every case is tracked, timestamped, and auditable for compliance and reporting.

AI Tutoring Coordination via MentorAI

MentorAI agents provide on-demand academic support 24/7, intelligently escalating to human tutors when needed. Reduces friction and scales personalized help across all disciplines.

Unified Student Success Dashboard

Aggregates data from Banner, PeopleSoft, Canvas, and Blackboard into a single advisor view. Real-time cohort health scores and intervention status replace disconnected spreadsheets.

Retention Analytics & Reporting

AI-generated retention reports segment by college, cohort, demographics, and risk tier. Enables proactive leadership decisions rather than end-of-semester post-mortems.

FERPA-Compliant, Institution-Owned Infrastructure

All AI agents run on your infrastructure. Student data never leaves your environment. Full FERPA, HIPAA, and SOC 2 compliance by design — with zero vendor lock-in.

Implementation Timeline

1

Discovery & Integration Mapping

2–3 weeks

Audit existing SIS, LMS, and advising tools. Map data flows from Banner, Canvas, and Blackboard. Define at-risk signal logic with student success leadership.

  • Data integration architecture document
  • At-risk signal taxonomy and threshold definitions
  • Stakeholder alignment workshop summary
  • Compliance review checklist (FERPA, SOC 2)
2

Agent Deployment & System Integration

3–4 weeks

Deploy early alert and case management agents on institution infrastructure. Connect to Banner/PeopleSoft and Canvas/Blackboard via secure APIs. Configure MentorAI for priority courses.

  • Live early alert agent connected to SIS and LMS
  • Intervention case management workflow configured
  • MentorAI deployed for pilot departments
  • Advisor dashboard with unified student view
3

Pilot, Training & Calibration

3–4 weeks

Run a controlled pilot with 2–3 colleges or cohorts. Train advisors and student success staff. Calibrate alert thresholds based on real intervention outcomes and advisor feedback.

  • Pilot cohort performance report
  • Advisor training completion records
  • Refined alert sensitivity and routing rules
  • Student engagement metrics from MentorAI
4

University-Wide Rollout & Optimization

4–5 weeks

Scale to all colleges and student populations. Activate retention reporting dashboards for institutional leadership. Establish continuous improvement cadence with AI performance reviews.

  • Full university deployment across all departments
  • Executive retention analytics dashboard
  • Ongoing agent performance monitoring setup
  • Documentation and internal AI governance framework

Expected Outcomes

+8%
First-Year Retention Rate
78%86%
-83%
Time to Intervention After Alert
12+ days averageUnder 48 hours
+63%
Advisor Caseload Efficiency
40% time on admin tasksUnder 15% time on admin tasks
+103%
Tutoring Resource Utilization
35% of at-risk students accessing support71% of at-risk students accessing support

Before & After AI

Before

Manual review of LMS and grade reports weekly; alerts often delayed 2+ weeks

After

AI agents monitor signals daily across all systems; advisors receive prioritized, actionable alerts within 24 hours

Before

Spreadsheet-based tracking with inconsistent follow-up and no audit trail

After

Structured AI workflows with automated routing, timestamped actions, and full case history for compliance

Before

Students self-navigate to tutoring centers; high drop-off due to scheduling friction

After

MentorAI provides instant 24/7 support and proactively connects students to human tutors when needed

Before

Semester-end reports built manually in Excel; no real-time cohort visibility

After

Live AI dashboards show cohort health scores, intervention status, and attrition risk by college and demographic

Before

Advisors log into 4–6 separate systems to build a student profile before each meeting

After

Unified advisor dashboard aggregates SIS, LMS, and advising data into a single, real-time student view

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Frequently Asked Questions

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