Revised majors catalog tries to address key question of AI era

A staff member trains a humanoid robot to carry objects at the Sichuan Humanoid Robot Multimodal Data Collection and Testing Center in Zigong city, Southwest China's Sichuan province, Jan 8, 2026. [Photo/Xinhua]

The recent release of China's 2026 catalog of undergraduate majors by the Ministry of Education shows the country is trying to redesign the talent pipeline between education, industry and national development.

The catalog puts emphasis on interdisciplinary fields: embodied intelligence, brain-computer science and technology, future robotics and interdisciplinary engineering. These are attempts to answer the defining question of the AI era: How to train people for the future?

Artificial intelligence, automation, biotech and clean energy are making some old majors redundant. Universities everywhere now face the same uncomfortable truth: teaching yesterday's knowledge and skills prepares students for yesterday's jobs.

China is therefore aligning majors with the talent requirements of the new quality productive forces — such as energy science, deep-earth engineering, agricultural robotics, digital finance and commercial AI. It is also attempting regional calibration, encouraging local governments to map talent shortages and create specialized clusters tied to regional economic needs.

This model moves resources quickly, maps universities with industrial policy, and reduces the gap between what students study and where the country's strategic priorities lie.

But it also faces challenges.

A university can announce a new major overnight. But it cannot instantly produce professors fluent in it.

The second challenge is overfitting to the present. Today's hot field can become tomorrow's glut. If universities chase short-term signals too aggressively, they may create graduates trained for jobs that are already outdated.

So even as new programs multiply, the foundational disciplines remain central. Mathematics, physics, chemistry and philosophy have not disappeared. If anything, they matter more. That is because AI depends on mathematics, logic, linguistics, statistics, neuroscience and ethics. Semiconductor advances depend on physics and materials science. Social stability in a period of technological upheaval depends on historical understanding and philosophical reasoning.

Consider language education. Many universities in the country have reduced or suspended traditional English and English literature enrollments. That reflects real market pressures: translation is automated, language credentials are less scarce and old career paths are shrinking. But the response was not simply retrenchment. The 2026 catalog adds Legal English, Language Science, Computational Linguistics and Language Intelligence.

This is a revealing pivot. Linguistics is being recast from a traditional humanities field into something closer to a "new liberal arts" discipline — integrating coding, data analysis and AI.

But even here, the old foundations remain indispensable. Aristotle's rhetoric still matters for persuasion systems. William Shakespeare still matters for nuance, metaphor and human motives. Ludwig Wittgenstein still matters for meaning and the limits of expression. Large language models may run on GPUs, but they also run on centuries of accumulated inquiry into the usage of language itself.

Countries that invest only in trendy courses may move faster at first. But countries that preserve and renew foundational knowledge will likely go farther.

There is one more pressure shaping all this: demographics. China recorded 16.87 million newborns in 2014, and 7.92 million last year. At this rate, after the late 2030s — just over a decade away — the number of college-age students is likely to fall sharply. Many institutions will confront excess teaching capacity, a shrinking number of applicants and existential questions. Some universities will need to merge, specialize or transform into lifelong-learning and adult-reskilling centers.

So the real story of China's course catalog is not majors; it is adaptation. Can universities become dynamic enough for technological upheaval, wise enough to preserve fundamentals, and flexible enough to survive demographic changes?

In the AI era, the future may belong not to the country with the most high-tech startups or the most renamed departments, but to the country that best understands that innovation is built twice: first in classrooms, and then in laboratories.