# AI Academic Advising at Scale: Caseload Triage, Proactive Outreach and Human Handoffs

> Higher Education · ibl.ai
> Source: https://ibl.ai/courses/higher-education/ai-academic-advising-caseload-management
> Last updated: 2026-11-14

*Caseload Triage, Proactive Outreach and Human Handoffs*

**A higher education course from ibl.ai (self-paced, 12 to 14 hours, free).** A practitioner course on academic advising caseload management: triage caseloads, run the proactive outreach RCTs support, and design human handoffs.

- **Price:** Free
- **Format:** Self-paced, online
- **Time:** 12 to 14 hours
- **Pace:** 3 hours per week
- **Level:** Intermediate
- **Language:** English
- **Subject:** Student Success

[Enroll](https://lms.ibl.ai/platform/highereducation/courses/course-v1:highereducation+ai-academic-advising-caseload-management+self-paced)

## Course description

Academic advising caseload management is the operational problem behind most advising redesigns: the latest national median NACADA publishes is 296 advisees per full-time advisor, from a 2011 survey, and more than 70 percent of large and public institutions were raising caseloads in 2025. This course teaches advising directors and student-success leaders how to triage a caseload, run proactive outreach that randomized trials support, and design the handoff so advisors spend their time where an algorithm cannot. You will read the Georgia State chatbot trials for what they show and what they do not, learn why the iPASS technology enhancements produced no academic gains, and build a proactive outreach agent on a sample caseload that drafts nudges inside rules and escalates everything else to a person. The moment is now because only 10 percent of frontline advising staff use generative AI for work, so the institutions that design the workflow deliberately will set the pattern their peers copy, and the ones that do not will inherit a vendor's.

### About this course

Academic advising caseload management is the operational problem behind most advising redesigns: the latest national median NACADA publishes is 296 advisees per full-time advisor, from a 2011 survey, and more than 70 percent of large and public institutions were raising caseloads in 2025. This course teaches advising directors and student-success leaders how to triage a caseload, run proactive outreach that randomized trials support, and design the handoff so advisors spend their time where an algorithm cannot. You will read the Georgia State chatbot trials for what they show and what they do not, learn why the iPASS technology enhancements produced no academic gains, and build a proactive outreach agent on a sample caseload that drafts nudges inside rules and escalates everything else to a person. The moment is now because only 10 percent of frontline advising staff use generative AI for work, so the institutions that design the workflow deliberately will set the pattern their peers copy, and the ones that do not will inherit a vendor's.

### What you will be able to do

- Compute their institution's advisor-to-student ratio on a stated basis (headcount and FTE, primary and secondary assignments) and place it against the latest NACADA median with the survey year attached.
- Map an existing advising model onto the CCRC SSIPP framework (sustained, strategic, integrated, proactive, personalized) and name the component that is weakest.
- Read a randomized advising trial and restate its effect in caseload terms (students affected per advisor, per term), including the control-group baseline and the population the effect was measured on.
- Build a triage rubric that sorts a caseload into outreach tiers using administrative data the institution already holds, and document the data each tier depends on.
- Configure and test a proactive outreach agent on a sample caseload that drafts scripted nudges within rules and escalates every judgment case to a named human.
- Design a handoff protocol and a small randomized pilot with a hold-out group, with metrics a provost will accept as evidence.

### Who this course is for

**Roles:** directors and assistant directors of academic advising, vice presidents and deans of student success, advising center managers, first-year and retention program leads, institutional research staff who build early-alert and advising dashboards, and enterprise application analysts who administer advising technology.

**Institutions:** community colleges, regional public universities, large public research universities with centralized or hybrid advising, and private nonprofit colleges where faculty advising is being supplemented by professional staff.

**Before you start:** familiarity with how advising is organized at your institution (centralized, decentralized or shared) and basic comfort with a spreadsheet. No coding is required. The lab uses an AI agent through a browser and a supplied sample caseload of 300 synthetic advisees.

### Format

- Estimated time: 12 to 14 hours.
- Level: intermediate. Assumes you run or support an advising operation; assumes nothing about software.
- Hands-on: one lab on a supplied sample caseload (300 synthetic advisees with registration, milestone, course-enrollment and contact-history fields), five worked examples (two on the same data), four templates you keep, two discussions, and a capstone on your own caseload.
- Structure: 4 sections, each ending in an AI conversation assessment, then a capstone assessment.

Self-paced. Every lesson is taught by an AI tutor that opens with a short story and works through one idea and one question at a time. Each section ends with an AI conversation assessment, and the course closes with a capstone assessment. Section assessments count for 60% and the capstone for 40%; 70% passes. There are no multiple-choice quizzes.

### Currency of this course

Every source in this course was checked on 2026-09-28. Rules and guidance change; check the linked sources before relying on a date or figure.

## Outline

### Section 0 · Start here

- 0.1 Welcome and orientation

### Section 1 · Know your caseload and what students get for it

- 1.1 The caseload problem
- 1.2 What students say they get
- 1.3 Section 1 assessment

### Section 2 · Judge the evidence for proactive advising

- 2.1 Defining proactive advising
- 2.2 Nudge chatbots: what the randomized trials show
- 2.3 When technology alone does nothing
- 2.4 Section 2 assessment

### Section 3 · Build triage and proactive outreach

- 3.1 Triage: segmenting a caseload
- 3.2 Hands-on lab: a proactive outreach agent
- 3.3 The adoption gap
- 3.4 Section 3 assessment

### Section 4 · Design handoffs, governance and measurement

- 4.1 Designing the handoff
- 4.2 Measuring whether it worked
- 4.3 Section 4 assessment

### Section 5 · Capstone and wrap-up

- 5.1 Capstone: your advising operation
- 5.2 Capstone assessment
- 5.3 Reading list and next steps

## Instructors

- ibl.ai Higher Education, Course team (ibl.ai)

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