[Bonus Episode] How to Empower Student Retention Efforts with AI

In a recent episode of the Higher Ed Geek Podcast, Will Ballard, CEO of Civitas Learning, joined host Dustin Ramsdell to discuss a simple truth about student success: knowing more doesn’t help if you know it too late. 

While the conversation touched on the growing role of AI, Ballard repeatedly returned to a more fundamental point—technology only creates value when it helps institutions see earlier and more clearly, moving beyond traditional, lagging inputs and enabling people to respond with human judgment.

Subscribe: Spotify | Apple Podcasts | Youtube Music | Full Transcript

The Limits of Backward-Looking Data

For decades, student success strategies have leaned on what data was easiest to access: grades, credit accumulation, end-of-term reports. These indicators are familiar—and deeply limited.

“The grade is the measurement. It’s not the input, it’s the output,” Ballard explains. “If you want to help folks, you need to know upstream of the grade—what goes into making that happen.”

Lagging data tells us what already happened. They don’t tell us when a student started drifting, disengaging, or encountering friction that could still be addressed. If institutions want to change outcomes, they need visibility into what happens before performance collapses.

Why Timing Depends on Frequency

What’s quietly reshaped student success over the past few years isn’t just better tools, it’s higher-frequency signals.

Learning platforms now surface daily patterns: logins, assignment activity, participation, peer interaction. On their own, these data points can feel noisy. But viewed together—and reviewed consistently—they become early indicators of risk.

“You can know day by day when there’s an inflection in someone’s risk,” Ballard notes. “As opposed to finding out after the midterm.”

That difference in timing matters. Running models once per term explains the past. Running them daily creates space to intervene while it still counts.

The Myth of “Clean Data”

One of the most common barriers institutions raise is data quality. The assumption is that progress must wait until systems are pristine. That assumption is wrong.

“There’s no such thing as clean data in the world,” Ballard says. “People think their data is dirty, but most of the time, it’s just hard to use.”

Student data reflects real life: transfers, stops and starts, work obligations, family pressures. Waiting for perfection often becomes a reason to delay action indefinitely.

Modern AI and analytics aren’t designed for ideal conditions. They’re designed to surface patterns despite imperfection, so humans can decide what to do next.

Human Judgment Still Matters Most

Predictive insight doesn’t replace people. It supports them. Ballard is explicit about this distinction: “I don’t want to predict someone’s going to fail and then they fail. I want to predict someone’s at risk, and then you do something about it.”

That “something” isn’t automated intervention or robotic outreach. It’s a conversation. A check-in. A human response informed by better context.

“We’re using the technology to make people be better informed,” Ballard explains, “so they can solve the problem with humans.”

AI, in this framing, isn’t the decision-maker. It’s the early-warning system.

Why Context Beats Benchmarks

Another hidden failure point in student success strategy is over-reliance on national averages and generic models. Institutions are not interchangeable.

“We don’t have a national model,” Ballard says. “We don’t have a stereotype of students across the U.S. We take each school’s data and history and build models for that school.”

What works in one region, one student population, or one institutional culture may not work in another. Treating risk as universal often leads to misaligned—or ineffective—interventions.

Effective insight is contextual. It reflects your students, your outcomes, and your reality.

Technology Should Create Presence, Not Distance

Perhaps the most important idea in the conversation isn’t technical at all, it’s human. Too often, systems pull advisors away from students: digging through platforms, preparing manually, documenting instead of listening.

Ballard describes the goal differently: “Technology lets you be more present and more of a person.”

When preparation, synthesis, and recall are handled behind the scenes, advisors can focus on what only humans do well—listening, interpreting nuance, building trust.

The best student success systems don’t replace relationships. They protect them.

A Better Question for Campus Leaders

The strategic question facing higher ed leaders isn’t “How much data do we have?” or even “What tools should we adopt next?”

It’s this: Are we seeing our students early enough to matter—and acting with confidence when we do?

As Ballard cautions, institutions shouldn’t delay progress waiting for ideal conditions. “Don’t put off using data to help your students because you think you need to clean it all up first. It just isn’t necessary.”

Student success isn’t a reporting function. It’s a responsiveness function. And the institutions that recognize that difference will be the ones that move from reaction to real impact. 

–

If you’d like to hear the full conversation, listen to Will Ballard’s appearance on the Higher Ed Geek Podcast (Spotify | Apple Podcasts | Youtube Music) or read the complete transcript to explore these ideas in more depth.

To learn more about how Civitas Learning partners with institutions to surface earlier insight and support human-centered student success, contact us to continue the conversation.