What problem does it solve?
A student can fall behind for many small reasons: a failing grade in a course required for their major, a missed registration deadline, a balance that blocks enrolment, or simply drifting away from campus life, and by the time grades or graduation data show the problem, the moment to help has often passed. Advisors cannot manually track thousands of students against every risk signal every day, so intervention has traditionally depended on a student asking for help or a professor happening to notice.
EAB's case study says that ten years before it was written, Georgia State University's six year graduation rate hovered around 32%, especially low for its growing population of Pell Grant students, low income students who receive federal need based aid. More than 850 institutions now use a vendor supplied early alert and advising platform, EAB's Navigate360, according to EAB, which shows how far predictive, alert driven advising has moved from a handful of early deployments to a category of software many institutions now run.
How does it work?
- Track every student daily. The platform reads grades, attendance, financial holds, registration status and other institutional data for each enrolled student against a large set of predefined risk factors, refreshed daily.
- Flag a deviation from the path. When a student's data crosses a risk threshold, whether a grade, a missed deadline, a balance or an engagement signal, the system generates an alert tied to that specific risk, not a generic warning.
- Route the alert to a person, fast. The alert reaches the student's advisor, who is expected to make contact within a short, defined window, so the flag turns into a real conversation rather than sitting in a queue.
- Reach the student where they are. Outreach follows by text, email or a scheduled advising meeting, phrased around the specific issue the alert raised, not a form letter.
- Remove the barrier the alert found. Some institutions pair the alerting system with a direct intervention, such as a small completion grant that clears a blocking balance, when the flag is financial rather than academic.
- Learn from what worked. Advisors and institutional researchers review which alerts led to a successful intervention and which did not, and adjust the risk factors and outreach playbooks over time.
- Audience
- Customer facing
- Autonomy
- Supervised agent
- Adoption
- Mainstream
- Channels
- SMS, Email, Web chat
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Inclusion and access, Customer experience, Employee productivity.
Indicative value
A university with 25,000 undergraduates
USD 56,250 to USD 281,250
Advisor time directed at students flagged as off track per year
How this is calculated
Formula: students * alertRate * minutesPerAlert / 60 * staffCostPerHour. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Undergraduate students tracked students, students | 25,000 | 25,000 | Editorial assumption, broadly similar in scale to the total enrollment (25,945) EAB reports for Georgia State University. |
| Students who receive at least one risk alert per year alertRate, fraction of students per year | 0.3 | 0.5 | Editorial assumption, replace with your own alert volume after a first term. |
| Advisor minutes per alert followed up, from outreach to resolution minutesPerAlert, minutes per alert | 15 | 30 | Editorial assumption, replace with your own time study. |
| Fully loaded cost of an advisor hour staffCostPerHour, USD per hour | 30 | 45 | Editorial assumption, replace with your own cost. |
What it leaves out: Counts advisor time engaged, not saved: the goal of this use case is to spend more staff time on the students who need it, not less time overall. It leaves out the value of any resulting graduation rate gain, the cost of the platform and the advising capacity an institution must add for alerts to lead to real contact rather than a longer queue.
Who already uses it?
2 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Georgia State University
United States · Education · 2026
Georgia State University joined EAB's Student Success Collaborative in 2012 and extended a data driven approach to academic advising, using EAB's Navigate360 platform alongside course redesign, supplemental instruction and fee drop grants. EAB's case study of the university states that GSU's advisors use Navigate360 daily and credits that daily use, together with the university's other student success measures, with contributing to a rise in GSU's six year graduation rate since 2012. Georgia State's own site names the wider effort GPS Advising, though EAB's case study does not use that name or describe how the platform's alerts are routed.
No outcome disclosed.
Auburn University
United States · Education · 2018
Auburn University, which has a 78% six year graduation rate and a 90% retention rate per EAB's case study, partnered with EAB in 2014 and implemented Navigate360 to reduce how many students in its College of Engineering were referred out of the major for falling below the required GPA. Within the Engineering programme, advising leadership uses Navigate360 alerts and cases to flag students at risk of not qualifying for the major, and a dedicated counselor then advises those students directly, enforcing positive academic behaviors. EAB's case study credits this predictive alert and advisor outreach model with a large drop in mandatory referrals out of the Engineering programme within three years.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Daily feed of grades, attendance, registration status and financial holds per student
- A defined, reviewed set of risk factors and thresholds, owned by academic leadership
- Advisor capacity and a service standard for how fast an alert becomes real contact
- Consent for text messaging and clear, accessible opt out
Systems to integrate
- Student information system
- Advising CRM or case management platform
- Messaging (SMS, email) for outreach
- Financial aid and student accounts system, where alerts are balance related
Complexity: High
The predictive model itself is usually bought from a vendor, not built in house, but making it work needs a clean, daily feed from the student information system, a defined set of risk factors an institution actually believes in, enough advising capacity to turn every alert into a real, fast conversation, and a plan for barriers, such as an unpaid balance, that a conversation alone cannot fix.
- 1
Add advising capacity before you add alerts
An alert that nobody has time to follow up on is worse than no alert at all. At Auburn University, a dedicated counselor advises every student an alert flags in the Engineering programme, per EAB's case study; plan that kind of staffing before turning the system on, not after the alert backlog appears.
- 2
Choose risk factors leadership will act on
A long list of statistically significant risk factors is not the same as a short list advisors trust and will actually follow up on. Start with a handful tied to a clear, actionable next step.
- 3
Pair academic alerts with a way to remove financial barriers
Some students are on track academically but held back by a modest unpaid balance rather than a grade or a deadline. A completion or fee drop grant that clears this kind of barrier once an alert or an advisor surfaces it can help; Georgia State, for example, used fee drop grants alongside its alerting platform. Decide up front whether your institution has an equivalent.
- 4
Personalize the outreach, do not template it
Tie each message to the specific alert (a course, a deadline, a balance), respect quiet hours and consent, and give a clear, easy way to reply or opt out.
- 5
Measure against a comparison group, not alert volume
Alert and meeting counts show activity, not impact. Where possible, compare retention or graduation outcomes between students exposed to the alert and outreach system and those who were not, on comparable cohorts.
Guardrails
- No decision about admission, financial aid eligibility or academic standing made by the system alone
- A defined service standard for how quickly an alert must reach a real conversation
- Consent, quiet hours and an easy opt out for text message outreach
- Wellbeing or crisis language in a student's reply routed to trained staff immediately, not the automated flow
KPIs to instrument
- Time from alert to advisor's first real contact with the student
- Retention and completion outcomes for flagged students who received outreach versus a comparison group
- Advisor caseload and time spent per alert, to catch overload before it causes silent backlog
- Opt out rate and complaints about the frequency or tone of outreach
Human in the loop
Advisors decide what to say and do with every flagged student; the system surfaces the alert and can send the first outreach message, but a person owns the actual conversation and any decision that affects the student's standing, aid or enrollment. Academic leadership reviews the risk factors, thresholds and outcomes on a set schedule, not just at go live.
Common failure modes
- Alerts that pile up faster than advisors can act
- A predictive system can generate more flags than an advising team can meaningfully follow up on, so alerts sit unread. Staff for the volume the system will actually produce, and monitor the backlog, not just the alert count.
- Counting meetings, not outcomes
- A large number of advisor meetings prompted by alerts says nothing about whether students actually stayed enrolled or graduated faster. Measure against a comparison group.
- A risk factor that quietly encodes bias
- A predictive model trained on historical outcomes can learn patterns correlated with race, income or first generation status without anyone intending it. Review which factors drive alerts and whether outcomes differ by student group.
- Outreach that feels like surveillance
- Students who realize they are being tracked against a set of risk factors, without being told, can react with mistrust rather than engagement. Disclose that the institution uses predictive alerts to support students, and what data that involves.
What are the risks and rules?
EU AI Act
Depends on design
The risk score itself, used only to inform an advisor and prompt a human conversation, sits outside Annex III. It moves toward high risk under Annex III point 3(b) if its output is used to evaluate a student's learning outcomes or to steer the learning process, rather than only to prompt a conversation; a score built mainly on grades and coursework can come close to that line. Once a student replies, the AI agent that answers them and sends outreach messages interacts directly with a natural person and falls under the Article 50 transparency duty, so the student must be told they are talking to an AI system unless this is obvious from context. Keep the actual decision about a student's support or standing with a person to stay on the lower tier.
Rules that apply
Guidance
- Annex III, high risk AI systems (point 3, education and vocational training) (European Union, Europe). Lists evaluating learning outcomes, including when used to steer the learning process, as a high risk education use.
- Article 50, transparency obligations for providers and deployers of certain AI systems (European Union, Europe). People must be informed that they are interacting with an AI system unless this is obvious from the context.
- Protecting Student Privacy (US Department of Education, Student Privacy Policy Office, North America). Guidance on FERPA, relevant when the platform draws on education records to generate and route a risk alert.
Controls to put in place
- Disclosure to students that predictive alerts and outreach are in use, and what data feeds them
- Advisor, not system, ownership of every decision affecting a student's standing or aid
- A defined service standard for time from alert to human contact
- Review of risk factors and outcomes by student group on a set schedule
- Consent and opt out records for text message outreach
Frequently asked questions
- Does predictive alerting and outreach actually improve graduation rates?
- EAB, the vendor, credits Georgia State University's advisors' daily use of its Navigate360 platform with contributing to a rise in the university's six year graduation rate since 2012, though EAB's case study does not isolate the alerting platform from the course redesign, supplemental instruction, freshmen learning communities and fee drop grants Georgia State introduced alongside it. In a separate case study, EAB credits Auburn University's use of Navigate360 alerts, followed up by a dedicated counselor, with a 73 percentage point drop in students referred out of its Engineering major within three years. Both figures come from the vendor's own case studies of named customers, not an independent study.
- How fast does an alert need to reach a student?
- The sources behind this page do not state a specific response time standard. What they show is that a predictive system only works alongside enough advising capacity, such as Auburn's dedicated counselor for every flagged engineering student, to turn a flag into a real conversation; a system that generates alerts faster than advisors can act on them creates a backlog, not an intervention.
- Can this replace an academic advisor?
- No. At Auburn University, per EAB, Navigate360 alerts flag at risk engineering students and a dedicated counselor then advises them; at Georgia State, EAB describes advisors using Navigate360 daily. In both cases, a person, an advisor or a dedicated counselor, owns the actual conversation, the judgment about what a student needs, and any decision that affects the student's standing or aid. The system's job is to make sure the right conversation happens sooner, not to have the conversation itself.
- Is flagging students with a predictive model regulated under the EU AI Act?
- It depends on what the flag is used for and who it talks to. A risk score used only to inform an advisor sits outside Annex III unless it is used to evaluate a student's learning outcomes or steer the learning process, which moves it toward high risk under Annex III point 3(b). The AI agent that then messages the student directly falls under the Article 50 transparency duty, so the student must be told they are interacting with an AI system.
How to cite this page
Blits.ai AI Use Case Library, "AI predictive analytics for student retention and advisor outreach", last verified 29 September 2026, https://www.blits.ai/ai-use-cases/predictive-retention-analytics-and-advisor-outreach. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 29 September 2026: First published