AI IN STUDENT SUCCESS: HOW PREDICTIVE ANALYTICS AND EARLY ALERT SYSTEMS ARE RESHAPING ADVISING

AI · Student Success · Predictive Analytics · Early Alert Systems · Higher Education

For a long time, the rhythm of academic advising followed the academic calendar rather than the student. An advisor might not learn that someone was struggling until midterm grades posted, or until a registration deadline passed and a seat in next term’s gateway course quietly disappeared. By the time anyone noticed, the window to do much about it had often already closed. That rhythm is changing. Across community colleges and large public universities alike, institutions are layering predictive analytics and early alert systems on top of data they already collect — learning management activity, attendance patterns, financial aid status, even how often a student badges into the library or a tutoring center — to surface risk weeks or months before a transcript would ever show it. The underlying idea isn’t new, but the scale and reliability of what’s possible heading into the 2026-2027 academic year represents a real departure from where things stood even a few years ago.

From Reactive to Proactive: What Actually Changed

The old model of student support was fundamentally reactive. A student had to fail a class, miss a payment deadline, or stop showing up before anyone in an advising office had a reason to reach out — and even then, the trigger was usually a single data point viewed in isolation. What’s different now is that institutions can combine dozens of signals into a single risk profile that updates continuously throughout the term. A dip in LMS logins combined with a missed assignment and a recent change in course load can flag a student as at-risk well before any grade reflects it. Research on this shift has found that instructor-initiated academic alerts, in particular, are associated with measurably lower withdrawal rates and higher final grades, which underscores that the value isn’t in the prediction itself but in how quickly it gets a person involved (Hussain et al., 2026). The technology’s job is to shorten the distance between “something is going wrong” and “someone noticed.”

The Platforms Behind the Shift

A handful of vendors dominate this space, and each has taken a slightly different approach. EAB Navigate leans heavily on a predictive modeling engine built from EAB’s research network across hundreds of partner institutions, generating risk scores that help advisors prioritize their caseloads toward the students most likely to need intervention. Starfish, now part of Anthology following its acquisition from Hobsons, takes a more relationship-driven approach — its strength has traditionally been connecting the people in a student’s “care network,” from instructors to tutors to financial aid counselors, so that a concern raised by one person is visible to everyone who might be able to help. Civitas Learning focuses on continuous monitoring, helping advisors notice subtle shifts in a student’s trajectory — a slipping GPA trend, a change in enrollment intensity — before those shifts become a crisis.

What ties all of these together, and what makes 2026 different from a decade ago, is integration. These platforms increasingly sit on top of — or are built directly into — the ERP and student information systems institutions already run, whether that’s Ellucian Banner or Colleague, Workday Student, or PeopleSoft Campus Solutions. That integration is precisely what makes the predictive layer possible in the first place: a risk model is only as good as the data pipeline feeding it, and for most institutions that pipeline runs straight through their ERP.

What the Data Actually Captures

It’s worth being concrete about what feeds these systems, because the answer is broader than most people expect. LMS data contributes login frequency, assignment submission timing, and quiz performance trends. The student information system contributes registration patterns, credit load, course withdrawals, and academic standing history. Financial systems contribute information about holds, outstanding balances, and aid disbursement status — all of which correlate strongly with whether a student returns the following term. Some institutions go further, incorporating campus card swipe data from dining halls, recreation centers, and libraries as a proxy for engagement and belonging. None of these signals on their own would tell an advisor much. Combined and weighted appropriately, they can identify students at elevated risk with enough lead time to actually do something — which is the entire point, and also where things get complicated.

Why the Human Touch Still Decides the Outcome

One of the more sobering findings in this space is that the prediction itself doesn’t move the needle — the response does, and not all responses are equal. Civitas Learning has documented that automated, transactional outreach messages can actually correlate with a drop in persistence of one to ten percentage points compared to a control group, while warmer, growth-mindset-oriented messaging from a real person produced a comparable lift in the opposite direction (Civitas Learning, 2025). In other words, a poorly executed early alert system can do measurable harm — flagging a student as “at risk” and then following up with a generic, vaguely punitive email is sometimes worse than doing nothing at all. The 2025 EDUCAUSE Horizon Report on data and analytics frames this as the central challenge of the current moment: institutions have gotten reasonably good at the prediction problem, but the intervention design problem — how, when, and by whom a student gets contacted — remains underdeveloped at most institutions (EDUCAUSE, 2025).

The Honest Challenges

The data integration problem deserves to be named plainly. Predictive models are only as good as the connections between systems that, on most campuses, were never designed to talk to each other. An ERP implementation that leaves the LMS, the CRM, and the financial aid system as semi-isolated islands will produce an early alert system that’s only seeing part of the picture — and a partial picture can be worse than no picture, because it creates false confidence.

Alert fatigue is the second major issue. When an advising team with a caseload of several hundred students suddenly receives dozens of flags a week, the system can become noise rather than signal, and advisors begin to triage or ignore alerts altogether — which defeats the purpose entirely. Equity is the third, and arguably the most serious. Risk models trained on historical institutional data can inadvertently encode the very disparities they’re meant to help address, flagging first-generation students, students from lower-income backgrounds, or working adult learners as “high risk” in ways that become self-fulfilling if the resulting interventions are stigmatizing rather than supportive. Institutions that are getting this right tend to treat their models as living systems that need regular auditing, not as a one-time configuration step, and they involve faculty, advisors, and students themselves in deciding what an “alert” should actually trigger.

What to Watch For

The next stage of this technology is already visible on the horizon. Several vendors are beginning to pair predictive scoring with generative AI tools that draft personalized, context-aware outreach for advisors to review and send — narrowing the gap between “the system flagged this student” and “a thoughtful message reached them” without removing the human from the loop entirely. For institutions that have spent the last few years getting their ERP and data infrastructure into shape, this is the moment that investment starts to pay off in a way students can actually feel. For institutions that haven’t, predictive analytics will likely remain a dashboard that looks impressive in a board presentation but doesn’t change much for the student sitting in a dorm room at 11 p.m., unsure whether to drop a class.

Beidat LLC works with colleges and universities on ERP implementation, data integration, and the broader digital transformation work that makes initiatives like predictive analytics and early alert systems actually function as intended. If your institution is evaluating a student success platform — or trying to figure out why the one you already have isn’t delivering the results you expected — the team at Beidat would be glad to talk. Reach out at support@beidat.com or call 888.384.1992.

References

Civitas Learning. (2025). Is your early alert system harming student success? Civitas Learning. https://www.civitaslearning.com/blog/effective-early-alert-system-in-higher-education/

EDUCAUSE. (2025). 2025 EDUCAUSE Horizon Report: Data and Analytics Edition. EDUCAUSE.

Hussain, F., Hammad, M., & Qahtani, H. (2026). AI-driven predictive analytics for student success and institutional decision-making in higher education. International Journal of Information Technology. https://doi.org/10.1007/s41870-025-03076-w

Last updated on June 13, 2026