Educating the Future AI-savvy Human Workforce - TechTarget

Alpha School's AI-driven learning

Alpha School, a growing network of private schools, is built around an AI-first model. Geared toward students in kindergarten through grade 12, the approach rethinks how students spend their time in the classroom. Core academic instruction in subjects like math, reading and science is delivered through AI-powered, self-paced learning platforms for roughly two hours a day. The rest of the day is focused on workshops and applied learning skills such as communication, entrepreneurship, collaboration and problem-solving. Teachers act more as guides than traditional instructors.

The goal is to use AI to personalize learning at a level that traditional classrooms often can't, according to Alpha School co-founder and CEO MacKenzie Price. "We are finally able to elevate the role of the teacher from just content deliverer to mentor and motivation expert and provide very specific personalized academic plans that meet each and every student at exactly the level and pace that works for them," she said in a recent interview with Fox Business.

Alpha School describes the model on its website as "school reimagined," positioning it as an approach to preparing students for a workforce where AI is already part of how work gets done. That approach, Price explained, enables students to complete core academics in a fraction of the time. "We're teaching life skills … helping develop kids that are ready to be successful in this AI-first world," Price said in her Fox interview.

AI in the classroom: Delivery model vs. skill development

While this new approach to education is built around AI, the role technology plays is less about teaching AI as a subject and more about using it to deliver the core educational experience. Models like Alpha School demonstrate that AI can personalize the pace and delivery of learning in ways traditional instruction often can't, Fuller said. And that principle is exactly what the workforce needs.

Today's most effective learning is "interactive, personalized and embedded in real-world contexts," rather than static and one-size-fits-all, Fuller noted. That distinction matters. "Using AI as a delivery mechanism builds comfort with the tools, but comfort is a precondition, not a substitute, for readiness," he said.

Comfort alone isn't enough, however. Learners still need a foundation of basic digital literacy and an understanding of how AI works before they can use those tools effectively. Without that, businesses and educators risk moving too quickly into AI without a clear sense of how it should be used.

That gap isn't just theoretical. AI adoption in education is being held back less by the technology itself and more by a lack of human readiness, particularly around AI literacy, suggested Gartner VP analyst Tony Sheehan in the research report "AI in Higher Education 2026: How to Reduce Three Barriers and Enhance AI Maturity."

AI fluency vs. job readiness

The question is not just how students learn but what students will need to do in the real world.

Businesses still require AI specialists like data scientists, machine learning engineers and AI architects, Fuller said, but the broader need is for AI fluency across roles. Hiring practices are increasingly shifting toward a skills-first approach, where employers prioritize demonstrated, real-world capability over degrees or titles, he added.

"The most in-demand candidates combine technical AI literacy with human-centric skills -- critical thinking, communication, collaboration and ethical awareness," Fuller explained. "Those are the people who can interpret AI outputs and use AI responsibly, not just operate the tools."

Alpha School's model aligns more closely with that broader definition of AI fluency than with training for specific technical roles. It emphasizes adaptability and comfort working with AI over deep technical expertise. By embedding AI into the learning process and emphasizing self-directed learning and life skills, the approach prioritizes adaptability, problem-solving and comfort working alongside AI systems.

But familiarity isn't the same as fluency. While students might get comfortable using AI, it's less clear whether that translates into the deeper understanding needed to question, interpret and apply those tools in real-world settings.

Personalization is not enough

While that level of personalization can improve efficiency and engagement, it also raises questions about what could be lost when learning is increasingly mediated through AI systems.

Alpha's model is built around continuous feedback and adaptation, using AI to tailor lessons to a student's ability and interests. In theory, that approach enables learners to move faster through educational material they've mastered while spending more time where they need support -- one of the core promises of AI-driven education.

But personalization alone doesn't guarantee understanding. Fuller warned of what he calls a "knowledge cliff," where learners appear capable because they can produce results with AI but lack the underlying expertise to operate independently. "AI raises the floor for everyone," he said, "but it can also lower the ceiling if learners never develop deep, domain-specific knowledge."

That concern is becoming apparent in education. Gartner's research found that while AI use is widespread, most institutions still aren't seeing meaningful results, with just a small percentage of them reporting tangible returns from their investments. More broadly, the research points to a deeper issue: AI readiness is less about the technology itself and more about whether people know how to use AI effectively. Students could rely on AI without really knowing when to trust and question it. The work they produce might look polished, but they lack the ability to explain it or use it outside the classroom.

Future of AI-driven education

Learning models like Alpha School could point to a broader shift in education, but it's still early. "It's a meaningful signal, not yet a proven shift," Fuller said. "What models like Alpha Schools get right is the underlying principle: AI should make learning more personalized, more applied and more measurable. That's the direction the entire learning industry needs to go."

Whether in schools or the workplace, much of AI training still follows a familiar pattern: Consume content, check a box and move on. The shift Fuller described is toward a more hands-on, adaptive approach, with a focus on building real capability over time.

But that transition comes with challenges, particularly around scale, equity and outcomes. As these models evolve, questions remain about how widely they can be applied, who benefits from them and whether they consistently deliver measurable results. While AI has the potential to transform education, Gartner research suggests that AI adoption remains uneven and often lacks a clear strategy.

Alpha Schools might not represent a new way to teach AI, but rather a new way to teach in a world where AI is already embedded. Whether that approach leads to true AI fluency or simply familiarity remains an open question.

Liz Hughes is an award-winning editor and writer covering AI and emerging technology and the former editor of AI Business and IoT World Today.