Artificial intelligence is exposing what's wrong with modern education

Artificial intelligence is exposing what's wrong with modern education

Artificial intelligence is exposing weaknesses in modern education that existed long before chatbots arrived.

A new paper argues that many of the skills students are still graded on, including summarizing information and writing basic essays, can now be performed by AI tools in seconds.

Rethinking what students should learn

Professor Yong Zhao at the University of Kansas (KU) has spent years asking why schools barely change no matter how hard people try.

He argues that the real problem is not students using artificial intelligence, but schools continuing to assess work that technology can easily replicate.

If AI can successfully complete the assignment, it may be the assignment that needs to change.

This has sparked debate over bans, cheating policies, and AI detectors.

Professor Zhao’s new paper suggests that educators may be overlooking a larger opportunity to rethink what students should learn and how they demonstrate it.

The paper opens with a blunt claim. The real trouble with AI in classrooms, he writes, is not the technology.

Generative AI can now do much of what schoolwork asks students to do. The tools summarize readings and turn a plain prompt into a passable essay in seconds.

When the assigned task is one a chatbot can finish, students reaching for the chatbot stops looking like a character flaw.

A recent survey found that nearly six in ten U.S. teens think cheating with AI happens regularly at their school. For Zhao, the pattern reveals a deeper problem than cheating.

The goal itself has gone stale, because schools still reward the kind of work a machine can now produce. “AI did not create this obsolescence. It revealed it,” he writes.

In one small experiment, people who wrote essays with a chatbot showed weaker connections between brain regions while writing and remembered less of what they had produced.

The study tracked 54 writers wearing brain-monitoring caps. The ones given AI showed the faintest mental engagement of any group and felt little ownership over their own essays.

None of this explains why schools are so hard to change, which is the puzzle Zhao spends much of the paper on.

Reformers have tried for a century, yet the basic structure holds. Classes are still sorted by age, subjects still stand apart, and tests and rankings still run the show.

Researchers call this stubborn pattern “the grammar of schooling,” the deep set of habits that outlasts reform after reform.

Zhao’s own explanation for its staying power is a peace treaty. A school, he argues, is a settlement among people who want different things from it.

Parents want clear signs their children are doing well. Colleges want familiar credentials for sorting applicants, and governments want numbers they can compare across schools.

Grades, schedules, and standardized tests keep all these groups reasonably content at once, so any change that unsettles the arrangement meets resistance before it starts.

Professor Zhao draws a line between improvement, which makes the existing system run better, and transformation, which questions what the system is for.

Zhao’s answer is not to fix the whole system at once, which he thinks is close to impossible. It is to start where change is actually within reach.

In his words, a courageous minority is the small number of teachers, students, and leaders in almost any school who are unhappy with how things run.

They rarely hold much power. What they do hold is a space they control, be it a single classroom, a small mentoring group, a capstone project, or a school-within-a-school.

Professor Zhao builds on panarchy theory, a way of describing how complex systems change. The theory suggests that large systems rarely change from the top down.

Small, protected corners are freer to experiment, and a workable experiment can spread outward, winning allies and slowly remaking the whole.

In practice, this looks like ordinary teachers rebuilding ordinary assignments.

Instead of a standard persuasive essay, students might pick a real local problem, gather their own data, and pitch recommendations to an audience that is not their teacher.

A math class might study traffic around the school and propose safer crossings instead of solving invented rate problems.

The point is to ask students to do work a machine cannot simply hand back. The theory is already being tested.

Zhao and colleagues have started a network of about 20 schools across several countries, where educators join calls at odd hours to design small experiments inside their own buildings.

These include student-directed learning days, student-run podcasts, and inquiry projects where children choose what is worth investigating.

Global labor forecasts expect AI to create around 170 million jobs and wipe out about 92 million by 2030, with the surviving work leaning on judgment and the ability to work well with others.

A school that keeps rewarding routine output prepares students for the part of the economy shrinking fastest. Piling on more technology is not a safe default either.

Population research has linked heavier screen time among children and teens to lower well-being, including weaker self-control and more trouble finishing tasks.

The pattern held across thousands of young people in one large study. More digital tools do not automatically mean better learning.

What Zhao adds to the debate is a change of target. The question is no longer how to keep AI out of school, but what school asks students to do once AI is in the room.

His theory says real change will not arrive as a grand policy handed down from above.

It will start with a few people building better learning in the spaces they already hold, and it may spread from there.

The study is published in the journal ECNU Review of Education.

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