After the Hype Cycle: Looking Back and Looking Ahead at AI in Education

When ChatGPT first arrived in late 2022, both of us, Jon as a longtime educator and advocate for accessibility and equity and Jason as the executive director of the Montana Digital Academy, felt the ground shifting beneath our feet. We had each spent decades navigating technological waves in education, from the early learning management systems to the explosion of Google Apps. But nothing hit with the velocity and disorientation of generative AI.

And as two people who often run into each other on conference circuits, open-education events, and long-form conversations about teaching and learning, we quickly realized that our separate curiosities about AI were intersecting.

This blog post is a reflection on what we’ve observed over the last few years, including what’s changed, what hasn’t, and where we see the work needing to go next.

Seeing the Shift Up Close

Jon: My own journey into AI didn’t start with excitement; it started with confusion. Students began submitting work that felt off. The pacing was strange, the voice unfamiliar, the explanations oddly polished yet oddly empty. Like many teachers, I went down the rabbit hole of detection tools, only to realize how quickly they fell apart. That pushed me to study AI more deeply. What I found, especially around bias, sycophancy, and the way these tools reflect and reinforce stereotypes, was alarming.

I began calling large language models “bias engines,” not to dismiss them, but to reveal what they actually are. And once you see them through that lens, it becomes impossible to unsee it.

Jason: Meanwhile, in Montana, our team at the Montana Digital Academy was starting to hear the first panicked questions from teachers and administrators across the state:

• “How do I know if a student used AI?”

• “How do I stop it?”

• “Can we block it?”

But we’ve run a statewide digital learning system for over a decade. We knew immediately that access control wasn’t a real strategy. Students carry powerful devices in their pockets. App stores are packed with AI-enabled tools, many with no age restrictions. And once something becomes ubiquitous outside of school, it will show up inside school, whether we have a plan for it or not.

Our responsibility shifted quickly; we moved away from policing and toward helping teachers preserve academic integrity, relationships, and learning.

Predictions, Hopes, and What Actually Happened

Jason: One prediction I made early on was that educators, understandably exhausted, would look for easy answers. And we saw that play out fast:

• “It’s age-restricted so kids can’t access it yet.”

• “We’ll just block it.”

• “The detectors will catch everything.”

None of that held. Meanwhile, the tools improved at a pace I didn’t expect. Hallucinations declined, integrations exploded, and the environmental impact became impossible to ignore. It felt like every time someone pointed to a limitation as a reason to ignore AI, the tech industry shipped a fix six weeks later or less.

Jon: My predictions skewed more skeptical. In 26 years, I’ve watched inequity grow with nearly every new tech adoption. Students with tech-confident teachers get one kind of future. Students without them get another. Every time.

So when colleagues predicted sweeping transformation through AI tutors or one-to-one digital mentoring, I wasn’t convinced. Not because the tech wasn’t capable, but because the system rarely shifts at the same rate.

And after several years? The inequities are widening. Access varies wildly. And vendors have become the loudest voice shaping the conversation.

The Noise, the Hype, and the Vendor Problem

If you’ve been to an education conference lately, you probably recognize the pattern:

• The theme is AI.

• The expo hall is pulsing with startups.

• The social feeds are full of lists like “100 AI Tools Every Teacher Must Use.”

• The selling is fear-based: Use this or fall behind.

We’ve both sat in these rooms. Jon recently watched a vendor instruct teachers to use AI without any warnings about bias or safety and declare that “prompting no longer matters.” The room ate it up. Teachers are overwhelmed and desperate for support; vendors are happy to fill the vacuum.

Jason: We see the same thing from the administrative side. Vendors promise transformation, but when you ask about rural access, privacy compliance, integration, or roadmaps, the answers evaporate. Some companies genuinely want to partner; many others just want a big check.

Jon: And without a counter-voice, one rooted in equity, accessibility, and actual teaching practice, the industry narrative becomes the only narrative.

So What Do We Do?

We’ve each found our own north star in this space.

Jason’s approach: Stay tool-agnostic. Test relentlessly. Keep a set of standard prompts to evaluate any new model. And above all: let educational purpose, rather than hype, drive adoption.

Jon’s approach: Teach educators to see AI outputs as first drafts, not truth. Build awareness of bias. Create policies that reflect realities we already understand from accessibility and equity work. And insist on human judgment as the final step in any AI-mediated process.

Together, we believe the path forward looks like this:

1. AI must be taught, not ignored.

Students are already using it. If adults stay silent, the only voices they hear will be influencers and app developers.

2. Relationships come before reactions.

Accusing students of cheating without sufficient understanding damages trust, especially online.

3. Equity must be the anchor.

Uneven access and uneven teacher preparedness will widen gaps unless we intervene deliberately.

4. Policymakers need to stop waiting for “stability.”

Education has always written policy for evolving tech; AI isn’t different.

5. Professional development has to shift from tools to thinking.

Not “Here are 20 apps,” but:

• How do we evaluate outputs?

• How do we mitigate bias?

• When is AI appropriate, and when isn’t it?

6. Teachers need permission to be cautious.

Skepticism is not resistance; it’s professionalism.

Looking Ahead: After the Hype

We’re both asked regularly what comes next for AI in education.

The honest answer is: the technology will keep evolving far faster than the system can. But the work, specifically the human work, remains the same:

• Teach critical thinking.

• Protect relationships.

• Preserve equity.

• Stay curious, but grounded.

• Keep students at the center.

The future of AI in education is not about the tools. It’s about the people who guide their use. And if we stay thoughtful, skeptical, and student-focused, we can turn this moment of disruption into a moment of deeper learning and greater inclusion.

Listen

NGLC is grateful for our collaboration and partnership with EDU Café Podcast that brings fresh voices and insights to the blog. Listen to the full episode of the podcast that inspired this article.

Photo at top by Allison Shelley/The Verbatim Agency for EDUimages.