Education, as we know it, was designed in the 19th century. Its structures, curricula, and priorities reflect the needs of the early industrial revolution, when information was scarce, processing data was laborious, and institutions were tasked with producing disciplined workers to feed bureaucracies and factories. That system worked for its time. But in the 21st century, especially in the age of artificial intelligence, those same foundations now work against us. What created education in the 19th century is precisely what undermines it today.
At the heart of this outdated model is Bloom’s Taxonomy, a framework that organizes learning into a rigid hierarchy: first remembering, then understanding, then applying, then analyzing, evaluating, and finally creating. This sequence reflects the logic of a bygone era, when memorization was essential because books were scarce, libraries inaccessible, and data-processing was slow and manual. To reach higher-order thinking, one first had to build a storehouse of knowledge.
But this linear approach is not just irrelevant in today’s world, it is actively harmful.
Information Is No Longer Scarce
The very premise that “remembering” should be the foundation of learning collapses in a world where AI systems can recall, retrieve, and synthesize information in milliseconds. We no longer live in an age of scarcity; we live in an age of abundance. Insisting that students devote years to rote memorization is like training them to carry water buckets after the invention of indoor plumbing.
Ironically, the skills we continue to drill into students, memorizing facts, reciting definitions, executing formulas, are exactly the ones machines now perform better than we ever could. By making this the starting point of education, we are teaching humans to compete with AI at its own game, and guaranteeing they will lose.
Linear Progression Was Always a Myth
Even before the rise of AI, evidence showed that Bloom’s “climb the pyramid” model did not reflect how people truly learn. Humans often achieve understanding by doing first, not by waiting until after memorization.
Think about learning a language: immersion and conversation build comprehension faster than memorizing grammar tables. Or consider learning a sport: one improves by playing, failing, adjusting, and only later understanding the mechanics in detail. Even in mathematics and science, applying a concept to solve a problem often precedes, and deepens, grasping the underlying theory.
Bloom’s taxonomy suggests that learners must plod step by step up the ladder, but real learning is far more recursive, messy, and dynamic. Students jump between practice, reflection, and theory, gaining clarity through use and exploration. By clinging to the pyramid, education has constrained this natural process.
AI Turns the Pyramid Upside Down
In the AI era, the so-called “lower-order” skills, remembering, understanding, applying, are precisely the areas most easily automated. What machines cannot do is set the purpose, frame the question, or determine the meaning of the work. That is the realm of analysis, evaluation, and creation, the very top of Bloom’s pyramid.
Paradoxically, these higher-order skills should not be the end goal after years of climbing the ladder. They should be the starting point. Students ought to begin by engaging in real problems, evaluating solutions, and creating projects, with AI as their assistant. In the process, they will circle back to understand concepts and remember facts, but now in context, with purpose.
When a child uses AI to tackle a complex challenge, say designing a simple video game or analyzing a local environmental issue, they will encounter gaps in their knowledge. Those gaps then motivate remembering and understanding. Knowledge becomes a tool, not an obstacle course.
The Hidden Cost: Human Potential Wasted
The persistence of Bloom’s outdated model has a deeper social cost. By forcing students to start at the bottom of a hierarchy that machines already dominate, we systematically underutilize human potential. Instead of cultivating curiosity, creativity, and judgment, we narrow learning into test-driven exercises of memory and comprehension.
The result is the paradox we see everywhere: in schools, AI looks like a cheat code, dumbing students down. In workplaces, AI is celebrated as an innovation engine, helping professionals reach breakthroughs faster. The difference is not the technology, it is the intent. Professionals use AI for outcomes. Schools, bound by 19th-century logic, use it as a threat to their information-first curriculum.
From Pyramid to Ecosystem
If Bloom’s pyramid is obsolete, what replaces it? Not another hierarchy, but a network. Learning should be seen as an ecosystem, where application, evaluation, and creation spark the need for memory and understanding, not the other way around. In this model, purpose is the soil, curiosity is the water, and knowledge is a nutrient that circulates as needed.
This is how humans naturally learn, and it is how they must learn in the AI era. Start with intent and practice. Let information acquisition emerge in service of meaning.
The Urgency of Change
Education systems cling to Bloom’s taxonomy because it provides order, structure, and measurable benchmarks. But neatness is not the same as relevance. What worked for the 19th century now undermines the 21st. The longer we insist on information-first models, the further behind we will fall in preparing students for a world where purpose, creativity, and judgment define human value.
The challenge is not whether AI will “make students lazy.” The real danger is that our outdated model of education will make them irrelevant. To prevent that, we must reverse the pyramid, abandon the myth of linear progression, and embrace a learning ecosystem fit for the age of abundance.
Dominic “Doc” Ligot is one of the leading voices in AI in the Philippines. Doc has been extensively cited in local and global media outlets including The Economist, South China Morning Post, Washington Post, and Agence France Presse. His award-winning work has been recognized and published by prestigious organizations such as NASA, Data.org, Digital Public Goods Alliance, the Group on Earth Observations (GEO), the United Nations Development Programme (UNDP), the World Health Organization (WHO), and UNICEF.
If you need guidance or training in maximizing AI for your career or business, reach out to Doc via https://docligot.com.
