# AI-Powered Student Success for Research Universities

> Source: https://ibl.ai/resources/use-cases/ai-student-success-research-university


*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.

## Pain Points

### 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.

*Metric: 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.

*Metric: 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.

*Metric: 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.

*Metric: 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.

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

## Solution 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

### Phase 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)

### Phase 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

### Phase 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

### Phase 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

| Metric | Before | After | Improvement |
|--------|--------|-------|-------------|
| First-Year Retention Rate | 78% | 86% | +8% |
| Time to Intervention After Alert | 12+ days average | Under 48 hours | -83% |
| Advisor Caseload Efficiency | 40% time on admin tasks | Under 15% time on admin tasks | +63% |
| Tutoring Resource Utilization | 35% of at-risk students accessing support | 71% of at-risk students accessing support | +103% |

## FAQ

**Q: How does ibl.ai integrate with Banner and Canvas at a research university?**

ibl.ai connects to Banner, PeopleSoft, Canvas, and Blackboard via secure REST APIs and LTI integrations. No rip-and-replace required — agents layer on top of your existing stack and pull data in real time without duplicating records or violating FERPA boundaries.

**Q: Is the early alert AI customizable for our university's specific at-risk criteria?**

Yes. During implementation, your student success team defines the signal logic — grade thresholds, LMS inactivity windows, attendance patterns, and more. Agents are configured to your institution's definitions of risk, not a generic vendor model.

**Q: How does ibl.ai ensure FERPA compliance for student success AI agents?**

All agents run on your institution's own infrastructure. Student data never leaves your environment or is shared with third-party AI providers. ibl.ai is FERPA, HIPAA, and SOC 2 compliant by design, with full audit logging for every data access event.

**Q: Can MentorAI handle tutoring support across STEM and humanities disciplines at scale?**

MentorAI agents are purpose-built and can be configured with discipline-specific knowledge bases. For a research university, you can deploy separate agents per college or course cluster, each with tailored content, escalation rules, and human tutor handoff protocols.

**Q: What happens to our AI agents if we stop using ibl.ai?**

Because ibl.ai operates on a zero vendor lock-in model, your institution owns the agent code, training data, and infrastructure. You are never dependent on ibl.ai's continued operation to run your student success programs.

**Q: How long does it take to deploy AI-powered early alerts at a large research university?**

Most research universities complete initial deployment in 8–12 weeks. The timeline depends on the complexity of existing SIS and LMS integrations. A phased rollout — starting with 2–3 pilot colleges — is recommended before university-wide scaling.

**Q: How does AI intervention case management differ from our current advising CRM?**

Traditional advising CRMs are passive record systems. ibl.ai's case management agents actively monitor student signals, trigger workflows, route cases to the right staff, send follow-up reminders, and generate outcome reports — reducing manual overhead by over 60%.

**Q: Can the AI retention dashboards segment data by first-generation status, Pell eligibility, or other equity indicators?**

Yes. Retention analytics can be segmented by any demographic or enrollment attribute available in your SIS, including first-generation status, Pell eligibility, race/ethnicity, major, and enrollment type — enabling equity-focused intervention strategies.