Changes in Education: How Providers Can Adapt To Stay Ahead
Discover how education is evolving with new technologies, teaching methods, and policies. Explore key trends shaping the future of learning.
February 19, 2026
In this micro-segment from our full webinar, The Realities of AI Data, Colton Meyers breaks down one of the most important questions facing education organizations today: When is AI genuinely useful in a data workflow—and when can it actually compromise data accuracy and trust?
AI tools often look impressive at first. They demo well, they automate tasks quickly, and they can give the impression that complex data processes are suddenly effortless. But as Colton explains, the real test isn’t the demo. It’s what happens when you begin reviewing the results.
That’s where reality surfaces.
Behind many AI-driven workflows is an error rate—sometimes 10%, sometimes more. While that might not sound alarming, the consequences multiply once you scale AI across large datasets or apply it to information that changes constantly. In K–12, data is always shifting: job titles update, schools close, districts reorganize. A static or unmonitored model simply can’t keep pace.
The fallout?
AI can unintentionally introduce inaccuracies that make your data harder—not easier to trust. But Colton is clear: the answer isn’t to avoid AI. It’s to recognize where AI adds value and where it needs strong guardrails.
AI becomes exponentially more effective when it’s grounded in reliable, human-verified context. When you combine Agile’s validated data with a large language model, the output becomes far more accurate—because the model is no longer guessing. It’s building on top of data that already carries the nuance our team has applied over years of verification.
Every word an LLM produces is influenced by what statistically should come next. When it’s rooted in correct, nuanced, up-to-date information, the results can be outstanding. But without that grounding, the model is operating on sand.
That brings Colton to the key insight of this segment:
AI is most useful when it’s a component of the data pipeline—not the engine driving it.
The win comes from understanding where to apply it:
AI can amplify great data. But it cannot compensate for incomplete, unverified, or misunderstood data.
And in education, where accuracy drives outreach, research, funding, and strategic decisions, that foundation matters more than ever.
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.
Senior Software Engineer, Agile Education Marketing
Colton Meyers, Senior Software Engineer at Agile Education Marketing, is a technically driven problem-solver with a strong foundation in software engineering and systems development. With a passion for building scalable, reliable solutions, Colton brings an analytical mindset and a learner’s curiosity to every challenge he takes on. At Agile, Colton plays a key role in designing and maintaining the technical infrastructure that supports data-driven education marketing initiatives. He collaborates closely with cross-functional teams to translate complex technical requirements into effective, real-world solutions.
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