Date: Friday, February 27, 2026
Hi, I’m Courtney Flanders, former teacher, instructional coach, school leader, and longtime believer that everything is figureout-able. At Manta AI, I partner with education leaders across K-12 and higher ed who are navigating complex decisions with limited time. Today, I want to share 5 big questions I see leaders answering with agentic AI—and why it matters for the evaluation field.
I believe that education leaders are drowning in data but starving for clarity. Dashboards exist. Surveys exist. Strategic plans exist. What’s often missing is the connective tissue: What does this actually mean? How do all these pieces of data tie together? And what should we do next?
Leaders often know how many students disengage. The harder question is why.
Agentic AI can analyze attendance, grades, behavior data, course performance, and survey responses together—surfacing patterns humans might miss. For example, it might detect that ninth graders in specific feeder patterns struggle after a schedule change or that chronic absenteeism spikes after certain assessment windows.
Unlike static dashboards, agentic AI can also suggest next investigative steps: interview questions, subgroups to examine, or additional data to collect.
I spent six years as a partner at a national consulting firm. Year after year, districts paid us for support. I believed in what we were doing, but I believed more that at some point, we should have worked ourselves out of a job. It’s difficult to know what the return on investment is for the products, services, and tools educators use.
With agentic AI, leaders can run robust correlations and cost modeling to understand where their spending is actually moving the needle.
Now I see districts ask about afterschool programming, bringing on another AP, or even the cost-benefit analysis of Saturday School. Whether money is limited or not, our most successful education leaders are going to evaluate what’s worth investing in and what subscriptions to cancel.
What would happen if we reduced our early college GPA requirement from 3.2 to 3.0?
What would happen if we needed to cut spending by 10%?
What would happen if we maintained staffing at schools with declining enrollment?
What would happen if we expanded our NES intervention to schools districtwide?
In the not-distant-at-all past, leaders simply didn’t have time to run robust data analytics to answer these questions. With agentic AI tools that clean, prep, merge, and analyze data—all while you tackle other responsibilities—leaders can model out their big ideas, Board goals, and future scenarios to get an honest, data-based assessment of what would happen.
These types of questions allow for balanced risk-taking while visualizing and supporting community engagement with evidence. Take this causal analysis of Houston ISD’s work as an example. In less than an hour, leaders have data-based evidence they can point to about expanding the work to other schools.
Agentic AI isn’t about replacing evaluators. It’s about expanding capacity.
When AI agents can synthesize vast, messy datasets and surface meaningful insights, evaluators and leaders spend less time pulling reports and more time interpreting, contextualizing, and acting.
The big shift is this: instead of asking, “Can we run that analysis?” leaders can start asking, “What else should we understand?”
And in today’s education landscape, that shift—from static reporting to dynamic inquiry—may be the most important evaluation advancement yet.
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