In this micro-segment from our full webinar, The Realities of AI Data, Colton Meyers and Mike LeClare dig into one of the most misunderstood challenges in today’s AI landscape: hallucinations — and why even the most advanced models still fail when the underlying data isn’t accessible, accurate, or nuanced enough.
Colton begins by highlighting something organizations often overlook: not all data lives on highly ranked, SEO-optimized pages that search-connected AI tools can reliably surface. State directories, agency-published files, school listings, and regulatory datasets often sit far outside the reach of typical crawling or ranking systems. Traditional AI models, especially those tethered to search engines, simply won’t find or prioritize this information.
That’s when the trouble starts.
As Colton explains, large language models are trained to guess—to statistically predict the next best word, even when the correct information is missing. OpenAI recently published research on this phenomenon, outlining how current training and evaluation methods reward confident, plausible answers over uncertainty or admission of incomplete knowledge.
So when an AI model can’t locate niche or unindexed education data, it doesn’t pause.
It fills the gap.
It hallucinates.
And in data-dependent environments like K–12, that small guess can snowball into serious inaccuracies.
Mike LeClare expands on the real-world implications. If an AI system reaches for information from a weak or outdated source — or worse, invents missing details — it risks producing summaries, explanations, or storylines that are simply wrong. When organizations rely on those outputs to shape campaigns, strategic decisions, or market insights, it can quietly distort the entire decision-making process.
This is where deterministic systems still have enormous value.
Unlike generative models, deterministic data pipelines rely on explicit rules, verifiable sources, and repeatable processes. They don’t guess. They don’t fill gaps. They don’t generate wrong answers with confidence. And in education, where job roles change, schools restructure, and data points shift constantly, that foundational reliability matters.
The key takeaway from Colton and Mike is clear:
AI is powerful — but only when paired with authoritative, human-verified data that eliminates ambiguity and removes opportunities for hallucination.
When grounded in accurate, structured, deeply contextualized information, AI can enrich workflows and amplify insight. But when left to operate in the blind spots of state directories, niche datasets, or incomplete public records, it folds under pressure.
Hallucinations aren’t a glitch.
They’re a predictable outcome of models forced to operate without the right foundations.
That’s why strong data governance, deterministic processing, and ongoing human oversight remain essential — especially for teams making high-stakes decisions in a rapidly shifting K–12 landscape.
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.
This year’s funding adjustments are not just figures on spreadsheets or topics in budget meetings. They are tangible, felt in the very heart of our education system-in classrooms, teacher lounges, and school hallways.
This year’s funding adjustments are not just figures on spreadsheets or topics in budget meetings. They are tangible, felt in the very heart of our education system-in classrooms, teacher lounges, and school hallways.
Discover when to build AI data infrastructure in-house versus licensing Agile’s ready-made education data to cut costs, reduce risk, and speed time-to-market.
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