Generative AI in education has been taking a lot of heat lately—and honestly, most of it’s fair. The biggest complaints? It spoon-feeds learners answers, doesn’t promote real understanding, and asks questions that either go way over learners’ heads or are so basic they’re bored out of their minds. At its core, AI just doesn’t know better. It hasn’t been trained to do what makes an effective instructor or trainer so impactful. Put someone struggling with a new concept in front of AI and ask them to master it, and yeah, you probably won’t see great results.
And I kinda feel for AI here. When I think back to my first year facilitating learning, I probably asked, “Does that make sense?” more than anything else. For those outside of teaching or training, you might be thinking, “What’s wrong with that?” (Spoiler alert: that’s a whole separate discussion.) The point is, learning how to ask the right questions—the kind that actually help people move forward—takes time, practice, and a deep understanding of how learning happens.
Let’s be real: AI is never going to replace a skilled instructor, trainer, or mentor. Nothing can replicate the countless ways a great facilitator connects with and supports learners. But that doesn’t mean AI can’t play a meaningful role in education and professional development. It has the potential to reinforce long-term retention, ask thoughtful questions that meet learners where they are, and encourage them to explain their thinking without simply giving away answers.
Learning professionals know how to ask the right questions and provide individualized support. But as anyone in the field knows, there’s only one of you and dozens of them, often with limited time. It’s simply not possible to give every learner the level of attention they might need. (If you’ve figured out how to do this, let’s talk!)
AI can help bridge that gap. If AI-powered tools could engage with learners in ways personalized to their needs, we could create adaptive learning experiences that scale to meet the demands of higher education and workforce training. This is how we expand access to the kind of support that empowers learners to succeed.
A lot of ed tech companies love to say, “Look, we hire former educators and trainers!” And don’t get me wrong—those folks are incredible, and more companies should hire them. Often, though, they’re brought into roles like sales or curriculum development, which are great fits and proof of their versatility. At Kyron, we’re doing something different.
We’re leveraging the expertise of former educators, instructional designers, and training professionals on our team to become prompt engineers. These are people who’ve spent years facilitating learning, who deeply understand how learners think, and who know how to guide them through challenges. They’re working directly with our AI engineers to write, refine, and perfect the prompts that shape how our models interact with learners.
This matters because prompts are the foundation of how AI communicates. A thoughtfully designed prompt doesn’t just ask a question—it helps learners engage critically, connect new concepts to what they already know, and build understanding in ways that stick. This is what skilled professionals do every day, and now we’re bringing that expertise to AI in a way no algorithm can replicate.
Yes, a lot of AI in education and professional development hasn’t lived up to its potential. If I were leading a training session or teaching in higher ed, I’m not sure I’d trust most AI tools enough to rely on them just yet. But here’s what I do know: if I were to use AI, I’d want tools built by teams that truly understand the realities of teaching and learning. That means bringing those with firsthand experience into the process—not just to consult, but to directly shape the products being built.
Our engineers are brilliant, and I’m constantly amazed by what they create. Most haven’t worked in education or training—well, except for you, Kelsey! No book, article, or secondhand experience can fully capture the challenges of facilitating learning for individuals with unique goals and needs. That’s why combining the expertise of learning professionals and engineers is so important. It’s how, and why, we’re on a mission to build tools that truly work for learners and the people who support them.
Will every interaction with AI be perfect? No, of course not. There will be challenges along the way, and we’ll keep learning as we go. But the potential here is undeniable. Bringing together learning professionals and engineers to build AI that supports personalized, meaningful learning—whether in a college classroom, a workforce training program, or a professional certification course—is the kind of work that gives me hope for the future. The needs are great, and a little hope for what’s ahead can go a long way right now.