Why Education Is the Front Line

Adapted from No Demand for Average, Part I.

AI Didn’t Break College—It Turned On the Lights

The rise of AI has exposed just how broken the traditional education system already was—especially the model of mass lectures and formulaic essays. AI did not break education; it revealed how it was already broken. The old job of “teacher” is over because the world has changed faster than the role itself. Today, educators must shift from teacher to Learning Designer. An educator’s value is not in delivering content—AI, YouTube, and Google do that more efficiently. The real value now is designing learning experiences that build durable skills.

AI did not break college. It simply exposed how broken it already was. When four hundred essays arrive looking like carbon copies, that is not a cheating crisis—it is a teaching crisis. For years, higher education has leaned on mass lectures, standardized prompts, and rubric factories that sandpapered the humanity out of learning. AI did not cause the collapse. It just turned on the lights.

That experience reshaped me as an educator. When I became a teacher, I made myself a promise: if there is a “correct answer,” let AI handle it. Let humans handle the magic. Let us focus on what machines cannot imitate: original ideas, lived experiences, strange sparks of curiosity, personal stories that only one human on this planet could tell.

The Wall and the Arena: Why AI Cannot Replace the Human Element

In the lecture halls of NYU Stern, Professor Panos Ipeirotis recently faced a modern academic crisis. Student submissions were coming in looking like polished, high-level McKinsey memos—flawless, professional, and authoritative. Yet, underneath the corporate gloss, there was a void. The surface was brilliant, but the understanding was shallow.

To combat this “AI-generated surface,” Ipeirotis did something counterintuitive: he fought AI with AI. He revived the traditional oral exam, but scaled it for the 21st century. In his classroom, an AI agent conducts live questioning, and a “council” of diverse AI models grades the depth of the answers. It is efficient, cost-effective, and surprisingly objective. It forces students to think on their feet and defend their logic in real time. But as we watch this evolution, we have to ask ourselves a deeper question: are we training students to communicate with humans, or are we simply teaching them to perform for machines?

This dynamic reminds me of my childhood. When I first started learning ping-pong, my coach did not put me at a table with a professional. Instead, I practiced against a wall. I would hit the ball, it would bounce off the wall, and I would hit it back. The wall was the perfect training partner for a beginner—tireless, consistent, and ideal for building basic muscle memory without wasting a coach’s time on repetitive drills. However, once I could hit the ball 20 times without missing, I had to leave the wall behind. To become a real player, I had to face a human. Unlike the wall, a human has intent. A human uses spin, reads your body language, and adapts to your strategy.

AI is the ultimate “wall.” It is a fantastic tool for practice, rehearsal, and pressure-testing ideas. But if we spend our entire lives playing against the wall, we will never be ready for the arena.

We have seen this “wall-only” training fail before in the world of language learning. I grew up in a culture of hyper-optimized English exams. I aced the exams. I knew the grammar rules better than some native speakers. But when I arrived in the U.S. in 2008, I could not hold a real conversation. I had perfect scores, but zero fluency. I had learned to “pattern match” for a machine-graded test, but I had not learned to relate to a person.

AI should be the building blocks in every classroom. We should encourage students to use it to build muscle memory, practice their defenses, and refine their syntax. But the final defense—the true test of whether a student has mastered a subject—must involve a human listener. We are not just teaching students to pass tests or generate memos. We are teaching them to be thinkers, leaders, and communicators. And to do that, they need to step away from the wall and into the room with us.

Degrees Get You in the Room—But Proof Gets You the Job

In the modern job market, education provides a crucial signal: degrees get you in the room. They tell employers you can commit and learn, helping resumes pass the first filter. However, a degree is often the “same key everyone else is using to knock on the same door.”

The new measure of value in the AI age is proof—and building undeniable proof requires students to progress through three stages: Projects, which are the foundation of skill, where ability is created by solving a real-world problem using industry-standard tools; Portfolios, the curated narrative where students structure an argument for their expertise by explaining their process and showcasing results; and Proof, the undeniable evidence—a live project link, measurable business impact, or client testimonial—which answers the question, “Can you do the job?” Significantly, 99% of companies do not care where you studied. What separates successful candidates is what they can do on Monday morning.

The No-AI Professor Problem

Since AI entered everyday life around 2023, higher education has been shaken. Students began using AI to write essays, complete quizzes, and optimize their grades. In response, professors shifted toward so-called AI-resistant approaches: presentations, project-based learning, and grading the process rather than just the final result. At the same time, many faculty members made a more productive choice. Instead of banning AI, they integrated it into teaching and assessment. The rule became simple and reasonable: use AI transparently, cite it properly, and explain how it supported your work. That approach prepares students for the real world.

However, some professors still enforce strict “No AI” policies. The consequences are serious. Graduates trained under a “No AI” mindset may enter the workforce believing they should not use AI to improve productivity. This puts them at a clear disadvantage. In Silicon Valley, junior engineers have already been let go for refusing or failing to collaborate using AI-assisted coding tools like Cursor. Good teachers help students navigate a changing world. Education should be a bridge to the future, not an anchor to the past.

Part II: Foundations of Spatial AI Literacy

“The degree matters less now. What counts is how quickly you learn the tools, especially AI tools, and turn them into your own superpowers for the AI era.”

— Fei-Fei Li