Beyond Native Ads: The State of Native Advertising
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December 29, 2025
When education data is used to measure progress, allocate funding, and guide district strategy, accuracy isn’t just important—it’s essential. But as Verlan Stevens explains in this micro-segment from our full webinar, The Realities of AI Data, even the cleanest datasets can tell the wrong story if the context behind them is missing.
While reviewing four years of third-grade reading scores for Agile’s Districts of Distinction project, Verlan encountered a challenge that many educators and data teams know all too well: some states’ “improvements” weren’t improvements at all. Wisconsin and Georgia had recently changed their state assessments—changes that made proficiency rates jump dramatically. On paper, it looked like a major breakthrough. In reality, it was simply the byproduct of a redesigned test and reset scoring cutoffs.
This is far from uncommon. States have complete autonomy in how they define proficiency and where they choose to set those benchmarks. If too few students meet the bar, some states simply adjust the bar. Overnight, a dataset can appear more successful without any instructional change whatsoever.
Mike LeClare adds that sometimes the trend moves in the opposite direction. When states introduce new assessments, proficiency scores often drop—not because teaching or learning has declined, but because educators need a year or two to understand how the new test works and how best to prepare students for it. Typically, once the transition stabilizes, scores rise again.
Both leaders highlight the same core insight: raw numbers never tell the full truth. A rise or fall in proficiency may reflect policy changes, shifting benchmarks, or unfamiliar testing formats—not actual student achievement. Without the surrounding context, even the most sophisticated analysis can be misleading.
This becomes even more critical as organizations explore AI-driven analytics. AI tools can spot statistical trends instantly, but they can’t understand context—they don’t know when a state has redefined proficiency, replaced its assessment, or introduced new scoring models. Without human interpretation, an AI model may label artificial gains as instructional success, or temporary dips as instructional failure.
Verlan and Mike’s conversation highlights why human oversight remains irreplaceable in education data work. AI can scale analysis, but humans provide the judgment, nuance, and historical understanding needed to interpret it responsibly.
In education, where performance data influences public perception, funding, and instructional decisions, accuracy isn’t optional. It’s foundational.
Ready to boost your educational marketing strategy? Watch the full webinar to learn more about the promises, pitfalls, and best practices of AI in education marketing, or explore Agile’s education data insights to get started.
As Managing Partner and CIO of Agile Education Marketing, Verlan Stephens brings more than four decades of experience collecting, curating, and publishing large-scale databases to his leadership role. Since co-founding Agile in 2009, he has guided the company’s data compilation strategy, driven new business development, and helped grow Agile’s reputation as a trusted partner in the education space.
Verlan graduated with honors from Boise State University with a degree in Mathematics and Computer Science and has lived in Broomfield, Colorado for over 40 years. He is passionate about connecting mission-driven companies with the schools, districts, and educators who benefit from their work.
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