1. The End of Memorization-Based Education and the Shift to Question-Driven Learning

Spring 2026. A classroom in an elementary school in Gangnam, Seoul. An eleven-year-old girl stares at her tablet screen. On it, her AI tutor has posted a single question: "If Rome had electricity, how would its slave economy have changed?" She thinks for about thirty seconds, then asks the machine a question back: "If there were fewer slaves, who would have built the Colosseum's spectator seats?" In 0.8 seconds the machine lays out three alternative paths for construction labor. The girl picks one and fires off another follow-up. Nothing in this scene needed to be memorized. The year Rome fell, the height of the Colosseum, all of it already lives inside the machine. What the girl did was the one thing the machine cannot do on its own: throw a question from an unexpected angle.

A 2025 experiment at Harvard's physics department puts numbers to this scene. Students who used an AI tutor learned more than twice as much as students in a traditional active-learning classroom, and they did it in less time. In a global survey that same year, student AI usage jumped from 66% to 92%. By early 2026, an estimated 86% of higher-education students were using AI as their primary research and brainstorming partner. The center of gravity in the classroom had already shifted: from a place where information is delivered to a place where questions are designed.

Demis Hassabis of Google DeepMind believes that artificial intelligence will match every human cognitive ability within five to ten years. His evidence is AlphaFold, which is already untangling clues to disease treatment and energy problems by predicting protein structures. In this environment, what students need to do during class is not memorize correct answers. It is to craft questions that steer the machine toward its best possible answer, and then to sort the useful responses from the nonsense. The principle Socrates demonstrated 2,500 years ago has come back wearing the clothes of technology. The principle that questions, not answers, are the essence of thought.

The problem is that assessment systems haven't caught up with this shift. Filling in correct answers on a standardized test sheet cannot measure the ability to solve complex problems in collaboration with artificial intelligence. According to a UNESCO survey, only 10% of more than 450 schools and universities worldwide have established AI usage guidelines. The exam papers are stuck in the twentieth century, the students hold twenty-first-century tools in their hands, and the teachers are somewhere in between, unable to find their bearings. The transition from an era of possessing knowledge to an era of operating knowledge demands curiosity that seeks new paths, not compliance that follows prescribed ones. By early 2026, AI-related programs in the United States alone had grown to 193 bachelor's and 310 master's programs, and Carnegie Mellon had already created the first AI bachelor's degree back in 2018. UCSD enrolled 150 freshmen in its new AI major and set a plan to scale it to 1,000 undergraduates by 2029. Universities are sensing the change. But if education fails to answer this demand, schools become museums.

2. The Halving of the College Degree Premium and the End of the Resume Era

In April 2026, Gallup, the Walton Family Foundation, and GSV Ventures jointly released a report called "The AI Paradox," capturing the psychological state of Generation Z in numbers. The share who said they felt excited about AI plummeted from 36% to 22% year over year. Those who said they felt angry rose from 22% to 31%. Those who called it hopeful fell from 27% to 18%. The most agitated age group was people in their early twenties, those who had just graduated from college or were about to. Gallup's senior education researcher Zack Hrynowski put it this way: "This is a generation that paid tuition for four years and then realized AI is upending their industry."

The anger has grounds. A late-2025 study found that AI-related skills add a 23% wage premium, while a bachelor's degree adds only 8%. Eighty-one percent of employers believe skills should be prioritized over degrees, yet 52% still hire degree holders. It feels less risky. These contradictory numbers pinpoint exactly where the college degree stands today. Its value as a signal remains, but the price-to-performance ratio of that signal is falling fast.

Kim Seojun, CEO of Hashed, diagnoses that three functions universities long monopolized are being unbundled one by one: information, relationships, and selection. All three used to come packaged together. Information is being replaced by MIT OpenCourseWare, YouTube, and GitHub. Relationships are being forged through open-source communities and hackathons. And for selection, GitHub star counts and actual user numbers have, in some cases, become higher-resolution signals than a 4.5 GPA. When Kim looks at the growth velocity of talent coming out of development-focused high schools he interacts with, he says he's forced to seriously ask whether college is still the best default. That's his honest admission.

Michael Spence's signaling theory deserves a re-read here. The idea that education doesn't directly raise productivity; rather, people who are already productive use a degree to prove it. If this model is correct, a degree is not evidence of learning but a signal of learning ability. When faster, cheaper, harder-to-fake signals emerge, the price of the degree gets cut. IBM, Apple, and Google dropping degree requirements for certain roles isn't a rejection of education's value. It happened because what the degree used to measure can now be measured more directly. Peter Thiel called college a bubble out of the same concern: can a state where price outpaces value last forever? With total U.S. student loan debt exceeding one trillion dollars, that question is not rhetoric. It is accounting.

A Goldman Sachs report projecting the automation of 300 million jobs over the next decade makes the problem sharper. Repetitive, structured tasks once handled by entry-level employees are rapidly migrating into the domain of machine intelligence, severing the apprenticeship path where senior workers taught junior ones and built their skills over time. The ability to produce immediate results using AI tools, rather than an impressive academic record on a resume, is becoming the decisive hiring criterion. The resume must evolve from a document listing past achievements into a portfolio proving the ability to solve complex future problems alongside machines. The deeper problem with universities lies in the structure of time itself: confining talent at its fastest growth stage inside a curriculum, midterms, and finals rhythm designed decades ago. In Kim Seojun's words, "The moment you park a high-speed bus at a stop called 'diploma,' it can take four years to merge back onto the main road."

3. The Changing Reason Cram Schools Exist: From Improving Grades to Managing Anxiety

Ten p.m. The lights of Daechi-dong's cram school district still blaze. A peculiar anxiety lingers on the faces of parents moving between buildings, but the nature of that anxiety has changed. Five years ago, the worry would have been "Can my child get a top grade in math?" Now it is "Will this job still exist by the time my child graduates?" The receipts still read Korean, English, Math, but what parents are really buying is relief from anxiety. The more opaque a child's future becomes, the closer a cram school enrollment resembles not a rational investment but a psychological insurance policy.

In Kim Seojun's "30 Cracks" memo, this phenomenon is summarized in a blunt sentence: "Agent tutors analyze each student's weak points in real time. But what cram schools really sell is childcare and parental anxiety management, so an industry that sells reassurance more than grades is hard for agents to displace." Four AI models independently assessed the probability of this happening within three years at 45%. Technically, the core function of cram schools can be replaced, but the real product they sell, psychological insurance, is something machines cannot provide.

A 2026 Gallup survey measures the temperature of this anxiety. Seventy-four percent of Gen Z K-12 students said "AI could make learning harder," and 83% of Gen Z adults already in the workforce expressed the same concern. The share who believed AI could speed up learning dropped from 53% to 46% year over year. Parental anxiety is the shadow of these numbers. Watching their children's generation wrestle with uncertainty, enrollment is the only action within reach.

In the 1830s, Benjamin Day's newspaper The Sun in New York created the prototype of the attention economy, stoking public anxiety and curiosity to generate advertising revenue. The cram school district is something close to that structure translated into the language of education. It commodifies the hope that there is something only humans can do in an age when machine intelligence handles everything else, and every time that hope wavers, it launches a new curriculum. When coding became trendy, coding classes opened. When prompt engineering became a buzzword, prompt classes appeared. The content changes, but the structure stays the same: detect parental anxiety, give it a label, and convert the labeled anxiety into tuition fees.

When the purpose of education degrades from the growth of a human being into becoming a component for survival in a platform-driven competitive society, cram schools stop functioning as workshops for academic improvement and start functioning as massive anxiety management centers. The reason this structure is dangerous is clear. Anxiety does not resolve. As machine intelligence advances, the total volume of anxiety grows, and total cram school spending grows with it. Regardless of how much a child's ability has improved, whether the parent's anxiety has decreased becomes the criterion for re-enrollment. Like Skinner's pigeons pressing a lever in response to intermittent rewards, parents flood into the cram school district in response to the stimulus of anxiety. Cram school revenue is proportional not to educational outcomes but to the total volume of anxiety. And as artificial intelligence advances, the source of that anxiety never runs dry.

4. The Importance of Filtering Errors in AI Output

A paper presented at the ICLR 2026 conference, "The Reasoning Trap," exposed an uncomfortable fact: the more you strengthen a model's reasoning ability, the more its tool hallucination rate rises in tandem. The smarter you make it, the more convincingly it gets things wrong. With 96% of companies already deploying AI agents in real work, this fact is close to an alarm.

The numbers are specific. In a 2026 benchmark covering 37 models, hallucination rates ranged from 15% to 52%. In medical case summaries, a 64.1% hallucination rate was observed without mitigation prompts, and even with mitigation techniques applied, it only came down to 43%. The legal field is worse. According to research from Stanford's Regulatory Studies Center, large language models hallucinate on specific legal questions 69% to 88% of the time. That means in three out of four cases, the model fabricates nonexistent precedents or distorts statutory provisions.

The daily routine of Sarah Lowden, a lawyer at a major London firm, is the real-world version of these numbers. She marvels at the speed with which AI can analyze three years of WhatsApp conversation logs and vast volumes of meeting minutes in just minutes, yet she never blindly trusts the machine's answers. In legal services, a small error from the machine can directly lead to the disclosure of client confidences or the compromise of legal privilege. A significant portion of her working hours goes to verifying whether machine-generated documents meet legal standards and comply with data protection regulations. The center of gravity has moved from an era of production to an era of editing and verification.

Kim Seojun's prompting benchmark essay imagines this ability in the form of an exam. The hardest section gives test-takers AI output with deliberately planted flaws and asks them to catch the errors. "Plausible but subtly wrong analysis, logical but built on false premises. Swallowing the confident wrong answers AI produces is the most dangerous trap of this era, and the ability to filter them out is the true core of prompting competence." At the 2026 CHI conference, a hallucination awareness training experiment was presented involving 48 children aged ten to fourteen. The children were asked to build their own AI chatbots while being given hallucination detection scaffolds, and pre-post testing showed significant gains in AI knowledge, hallucination awareness, and confidence in building trustworthy chatbots. The children independently developed multi-layered strategies: searching for inconsistencies and cross-checking against external sources.

More than solving complex equations, the ability to verify the validity of a machine's answers. That will become the new intellectual authority in an era of coexistence with artificial intelligence. Because the only entity that bears responsibility for machine output is a human being. The machine is an excellent assistant, but it cannot be the one making decisions. The ability to hold that boundary becomes the new qualification for a professional. Even the best-performing model, with a hallucination rate of 17%, gets it wrong one time in six. In the face of that reality, acceptance without verification is professional suicide.

5. The New Educational Inequality That Prompting Skill Measurement Will Create

Same ChatGPT, same model, same price, yet one person gets a one-line summary while another extracts an analysis at the level of an academic paper. What creates this gap is prompting skill. Kim Seojun is convinced it will become the most important productivity metric of the twenty-first century. And there is an uncomfortable shadow attached to it.

Prompting skill is deeply linked to critical thinking, the ability to structure problems, and metacognition. These are heavily influenced by educational environment and cultural capital. To ask good questions, you need to have been exposed to good questions. The person who types "write me a marketing strategy" gets a blog-post-level overview. The person who structures a request from target customer psychological profiles to competitive positioning maps to channel-by-channel ROI hypotheses gets an executable strategy document. This gap widens as models grow stronger. The higher the ceiling of the tool rises, the more the difference in the hands operating it is amplified exponentially.

According to a report to the UN General Assembly, 118 countries have fallen completely behind in the artificial intelligence race, and a third of the global population still lacks basic internet access. Students in wealthy nations hone their prompting skills with advanced models and unlimited computing resources, while students in developing countries worry about electricity supply and internet speed. In a world where AI skill commands a 23% wage premium, prompting competence risks becoming not a ladder for social mobility but another wall of discrimination.

The situation in South Korea is no different. In a 2026 Gallup survey, 48% of Gen Z workers said the risks of using AI at work outweigh the benefits, an 11-percentage-point jump from 37% the previous year. Fewer than three in ten said they trusted AI-assisted work, and virtually none said they trusted work performed by AI alone. The irony is striking: the more distrust of the technology grows, the higher the premium climbs for the few who wield it well. The market puts a higher price tag on those who are skilled with a tool that the majority avoids.

CEO Kim Seojun draws an analogy to the Scouter from Dragon Ball. "Power level 5, trash." Crude but powerful as a number, it was a device that compressed every dimension of complex combat ability into a single figure, enabling instant judgment. What we'll soon need, he argues, isn't a college entrance exam score or a diploma, but a number that predicts the quality of output a person can produce when collaborating with AI. That test would naturally be open-book, with AI use as a given. Measuring ability without AI is as anachronistic as testing math skills without a calculator.

But the real question may not be about the score itself. A benchmark is ultimately a mirror of what society considers important. Because the CSAT existed, Korea valued memorization and problem-solving speed, and the private tutoring market grew into a massive industry calibrated to that metric. Once a prompting benchmark emerges, the criteria for education, hiring, and promotion will reorganize around it. Measurement creates reality. So will the world this new metric shapes actually be fairer than the one we have now? In a structure where gaps widen not linearly but exponentially, it's hard to be optimistic about that question.