By Yerkin Tatishev, Founder of Almaty High Tech Academy, Chairman of Almaty Management University (AlmaU), and Founder of Kusto Group
In 1996, IBM’s supercomputer Deep Blue defeated world chess champion Garry Kasparov. In 2016, AlphaGo, developed by Google DeepMind, beat South Korea’s Lee Se-dol—not just calculating, but learning from its own mistakes. These milestones revealed a sobering truth: artificial intelligence can outthink us.
For many, the response was predictable: compete. Schools doubled down on speed, standardized tests, and content delivery, as if the goal were to outpace machines. But this is the wrong race. The real challenge is not to run faster than AI—it is to cultivate what AI cannot replicate: character, judgment, and the ability to learn from experience.
We cannot outcompute AI. We cannot out-memorize it. And increasingly, we cannot outlearn it—at least not the narrow way we have long defined learning. The task is to raise human beings who can think, act, and grow in ways machines cannot.
Decades of research already point the way. People learn best not by absorbing information, but by using it—by testing ideas, reflecting on what works, and learning from failure. Yet most classrooms still reward uniform pacing, passive listening, and short-term recall. We are using yesterday’s model to prepare students for tomorrow’s world.
At High Tech Academy (HTA) in Almaty, our goal is to redesign teaching to meet the needs of today’s students and tomorrow’s economy. Together with the Korda Institute for Teaching, we turn science into practice. We follow a simple principle: learn, apply, reflect. This is not a slogan—it is a discipline.
In kendo, the Japanese martial art I’ve practiced for nearly 20 years, students begin not with theory but with practice: repetition, failure, correction. Reflection is constant, not occasional. Progress comes from engaging with mistakes, not avoiding them.
Nomadic traditions followed the same rhythm. Children learned by doing—riding, building, navigating uncertainty alongside those with more experience. Knowledge emerged through use. Judgment through experience.
Both traditions arrive at the same conclusion: growth requires friction.
Modern education often tries to remove it. We minimize failure, standardize outcomes, and prioritize efficiency. The result is a system that produces answers but not thinkers; performance, but not resilience. And resilience is exactly what this moment demands.
AI can analyze, predict, and improve through iteration. But it does not develop character. It does not learn courage through failure. It does not build judgment by acting under uncertainty. These are human advantages. They are also learned.
If we take science and experience seriously, the implication is clear: students must spend less time consuming knowledge and more time using it.
This begins with a shift in purpose. Education should aim for individual growth, not uniform results. People learn deeply when knowledge becomes a tool, not an endpoint. In practice, this means giving students real work—not simulations for assessment, but challenges that reflect the complexity of the world beyond school.
At HTA, students learn this way every day. Younger students have explored how to protect urban wildlife while ensuring safe coexistence with people. Older students have tackled transportation systems to reduce congestion and pollution. Math becomes a way to make decisions. Science becomes a way to test ideas. Writing becomes a way to influence others. When the work matters, students rise to meet it.
Equally important is ownership. When students are responsible for solving problems, they begin to see themselves differently. Teachers shift roles—from delivering information to guiding thinking, from correcting answers to developing judgment. The result is not only stronger academic learning but stronger individuals: students who can collaborate, adapt, and persist. Students are defined not by getting it right the first time, but by their ability to improve over time.
We do not need to guess what works. We have the research. We have examples. What we lack is not knowledge, but the will to change.
Change does not require a complete redesign. It begins with small, deliberate shifts: embedding reflection into daily work, giving students problems worth solving, and measuring growth instead of ranking performance.
In a world where machines can learn, the human advantage lies elsewhere. We will not succeed by trying to become more like AI. We will succeed by becoming more fully human.
