How Smarter Tech Can Help Close the STEM Education Gap

Science is moving fast, but STEM hasn’t kept pace. At a time when we need more scientists, engineers, and healthcare professionals than ever, too many students are still being left behind. For learners in rural areas, underfunded schools, or those balancing work and caregiving, the path to a STEM career can be blocked before it starts.

This isn’t just a policy problem or a budget issue. It’s a design challenge. And as someone who’s built games, learning platforms, and immersive tech for most of my career (and not to mention, as a father of four), it’s one I take very personally. We need to rethink what learning looks like, and we need to do it in a way that expands access without lowering the bar.

That’s where smarter technology comes in. Not tech for the sake of tech, but tools that help educators do more with less, give students hands-on practice no matter where they are, and make real science feel possible.

It’s not about speed. It’s about fit.

There’s a lot of hype about how artificial intelligence (AI) can make education faster. But speed alone isn’t helpful if it doesn’t serve students. What matters more is whether the content meets learners where they are and gives them what they need to succeed.

Take science labs. In-person labs are expensive, hard to schedule, and often out of reach for students who aren’t on a traditional campus. For the millions of students learning online or part-time, that’s a deal-breaker.

Virtual labs can help fix that. They make it possible to deliver complex experiments through a browser, letting students practice safely and flexibly. But building those labs takes time – and that’s where new technology can help. By using AI to support academic experts, our teams can generate simulation drafts or spot content gaps and get high-quality science education into more classrooms, faster. And we can do it without cutting corners or losing touch with humans.

Let People Lead, Not the Algorithm

There’s a right and wrong way to use technology in education. We’ve been experimenting with ways to use AI behind the scenes. That means building tools that help our teams work faster, not replacing teachers or curriculum.

Every simulation we publish will go through real scientists and instructional designers. AI might help generate an early version, but it’s the experts who shape the final product. That human layer is essential. It keeps the content accurate, age-appropriate, and aligned with how students learn.

And it doesn’t stop with internal review. We test with real educators to see how the material performs in the classroom, online, and in hybrid formats. We look at engagement rates, comprehension, and areas where students get stuck. All of that data feeds back into how we refine our content.

This isn’t just a quality issue; it’s a trust issue. If we want technology to support more equitable education, it has to be built carefully and with real oversight. It has to be part of a system that prioritizes students and teachers, not software.

Hands-on learning that sticks

One of the big questions in education right now is: how do we know students are really learning? With tools like ChatGPT, it’s easier than ever to fake an essay or solve a problem set. That’s a challenge for schools – and an opportunity for platforms that teach through experience, not memorization.

Virtual labs are one answer. When students run an experiment, troubleshoot it, and see what happens when they make a mistake, the learning is deeper. You can’t copy/paste your way through that.

And what’s just as important is the feedback loop. In a well-designed simulation, students get real-time guidance, not just scores. They’re encouraged to reflect on their actions, revisit missteps, and apply critical thinking. That kind of learning sticks, because it’s active and applied.

We’ve also seen how simulations can help students who lack confidence in science. These tools give them a safe space to experiment, fail, and try again. That’s not just good pedagogy; it’s a way to build a sense of capability. And when students see themselves as capable of doing science, they’re more likely to stay on the path.

A Real-World Example: Yavapai College

At Yavapai College in Arizona, many students are older, working, or caring for families. A few years ago, faculty introduced virtual labs into an online microbiology course. Completion rates jumped by 16%, and the gap between online and in-person students virtually disappeared.

That’s what happens when you design with real students in mind. It’s not about flashy tools, it’s about removing barriers and supporting the outcomes that matter.

Where We Go from Here

I’ve seen how technology can change how people learn. But what excites me most isn’t the pace of change, it’s the potential to finally close some of the gaps we’ve lived with for too long.

Not every solution needs to be powered by AI. But the right tools, used by expert humans, can help more students succeed in science and help more teachers do what they do best.

As a parent, I want my kids to grow up in a world where great education isn’t locked behind geography or income or dictated by generative AI. I know we can build that world if we focus less on buzzwords and more on building what actually works.