Korea risks 'capability divide' in its pursuit for AI leadership

Korea is unusually well placed to benefit from artificial intelligence (AI). It has world-class digital infrastructure, highly educated workers, globally competitive technology companies and a culture that places enormous value on educational achievement. But those same strengths create a distinctive risk: AI can raise visible performance faster than it develops the underlying capability that performance is supposed to represent.

Generative AI can already help students produce polished essays, young employees prepare sophisticated analyses and managers generate professional presentations in minutes. These gains are real. Yet the quality of an output and the capability of the person producing it are not the same thing.

A student can submit an excellent answer without having fully constructed the argument. A junior analyst can deliver a persuasive report without having wrestled with the evidence. A young professional can appear to perform at a senior level while remaining dependent on a system whose reasoning he or she cannot independently reproduce or challenge.

I call the emerging inequality around this phenomenon a "capability divide": the growing distance between environments where AI deepens human judgment and expertise and environments where it substitutes for the activities through which those capacities would otherwise develop.

This distinction matters particularly in societies where performance is intensely measured. When examination scores, rankings, credentials and workplace output carry great weight, AI-assisted performance can become difficult to distinguish from genuine development. The danger is not cheating in the narrow sense. It is that institutions may begin rewarding the appearance of capability without ensuring that capability itself has formed.

Human expertise is developmental. People learn to write by writing, to diagnose by diagnosing, to analyze by analyzing and to judge by taking responsibility for judgments that may later prove wrong. Much of this process is inefficient. It involves repetition, uncertainty, correction and time.

AI can improve this process dramatically. It can act as a tutor, generate counterarguments, explain difficult concepts, simulate cases and provide rapid feedback. Used this way, it can accelerate learning. But the same system can also deliver the finished product before the learner has done enough of the cognitive work to benefit from the experience.

The difference is not in the technology. It is in the surrounding institution.

Two students can use the same model. One is required to form an initial position, compare sources, defend a conclusion and explain how the AI changed her thinking. The other is rewarded primarily for submitting a polished result. The first environment uses AI to develop capability. The second risks using AI to bypass it.

The same problem enters the workplace. Many entry-level tasks are repetitive and therefore attractive candidates for automation. They are also often the first stage of professional apprenticeship. Junior lawyers review ordinary cases before they can recognize unusual ones. Engineers solve routine problems before they acquire intuition. Analysts prepare imperfect drafts before they learn which assumptions matter.

If organizations automate these tasks without redesigning how experience is acquired, they may preserve today’s senior experts while weakening the pipeline that produces tomorrow’s.

This creates a further problem for AI governance. Organizations often promise to keep a human in the loop. But meaningful oversight requires more than a human signature. It requires enough independent knowledge to recognize when the machine is wrong, enough confidence to disagree and enough authority to override the recommendation.

If AI adoption gradually removes the experiences through which this independence develops, human oversight can remain formally present while becoming substantively weaker.

Korea therefore has an opportunity to lead on a more demanding form of AI adoption. The goal should not simply be to make students and workers more productive with AI. It should be to ensure that repeated use leaves them more capable.

In education, this means assessment should increasingly focus on reasoning, explanation, source verification and oral defense rather than only on finished output. Students should sometimes be required to form a view before consulting AI and then explain how their judgment changed.

In companies, managers should distinguish tasks that are merely burdensome from tasks that are also formative. Some should disappear. Others should be redesigned so that AI supports the learning process rather than replacing it entirely.

Organizations should also measure a variable that rarely appears in AI dashboards: After one year of using these systems, what can employees do better on their own?

That question may become as important as productivity itself. A society can become more efficient while becoming more dependent. It can produce better outputs while weakening the distributed human expertise required to understand, challenge and govern the systems producing them.

Korea has spent decades building institutions capable of producing extraordinary educational and professional performance. The challenge of the AI era is to make sure that performance continues to represent real human capability rather than increasingly borrowing it from machines.

Nicholas Paparoidamis is professor of marketing and dean of faculty and research at ISG International Business School in Paris. He is also a member of the Board of Governors of the Academy of Marketing Science. His work examines artificial intelligence, human capability, organizations and institutional responsibility.