For years, higher education has worked with a reasonably stable proposition. Learn the foundations of a discipline, develop expertise, earn a degree, and enter the profession prepared to begin.

Artificial intelligence has not made that proposition irrelevant. It has made the distance between education and employment harder to ignore.

The World Economic Forum’s 2026 report, Artificial Intelligence and the Future of Entry-Level Work, identifies education system alignment as one of four dimensions shaping the future of early-career work. Its concern is not whether universities still matter. The more difficult question is whether education and training systems can adapt quickly enough when the skills, technologies, and expectations graduates encounter at work continue to change.

The report calls for curricula that reflect workplace technologies, more work-integrated learning, alternative pathways alongside traditional degrees, and closer collaboration between educators and employers. WEF_Artificial_Intelligence_and…

For universities, this presents a challenge that goes beyond adding AI to a syllabus.

It asks what an education should prepare a person to do when the work itself refuses to stand still.

The skills required at work are changing

The World Economic Forum expects 39% of workers’ existing skill sets to be transformed or become outdated between 2025 and 2030.

That number deserves some perspective.

It does not mean that nearly four in ten skills will simply disappear. It reflects the scale at which employers expect skill requirements to change as AI, digital technologies, economic shifts, demographic changes, and other forces reshape work.

Entry-level positions sit particularly close to this transformation.

These are the roles where graduates first translate education into professional practice. Yet they are also among the positions being reconsidered as AI assumes certain tasks, changes workflows, and allows employees to engage with more complex work earlier in their careers.

The result is a difficult alignment problem.

Universities educate students over several years. Technology can alter how a profession operates in considerably less time.

Education cannot be redesigned every time a new tool appears

It would be easy to conclude that universities simply need to update their curricula faster.

The reality is more complicated.

Education systems operate through structures designed to ensure academic quality and coherence. Programs require planning, approval, faculty preparation, assessment frameworks, and institutional oversight. Those mechanisms serve an important purpose.

They can also make rapid change difficult.

The World Economic Forum highlights this structural tension. Education and training systems must prepare people for workplace requirements that are evolving continuously, while formal curricula cannot realistically be rewritten every time a technology changes. WEF_Artificial_Intelligence_and…

There is also the danger of confusing currency with relevance.

Teaching students whichever AI platform happens to dominate today may make a course appear contemporary. It does not necessarily prepare graduates for the technology they will encounter five years from now.

The more enduring question is whether students understand their discipline well enough to work with new tools, question their outputs, learn unfamiliar systems, and apply knowledge when the circumstances change.

That requires something deeper than software proficiency.

The degree is not disappearing

The arrival of generative AI has encouraged plenty of predictions about the future of universities. The World Economic Forum report takes a more measured position.

Traditional degrees remain part of the education landscape. At the same time, the report argues for expanding work-integrated learning and alternative pathways alongside them. WEF_Artificial_Intelligence_and…

The distinction matters.

The issue is not necessarily degree versus skills, or academic education versus practical experience. Increasingly, graduates need the ability to connect them.

A student may understand financial principles in a classroom and still need to learn how decisions are made when information is incomplete.

An architecture student may understand design theory but develop a different kind of judgment when confronting actual site, client, regulatory, and budget constraints.

A hospitality student can study service systems, then discover through professional exposure how those systems behave when a guest, a team, and an operational problem arrive at the same time.

Knowledge establishes a foundation. Application tests what that knowledge can actually do.

This is why the report places emphasis on work-integrated learning and closer connections between education and employment. WEF_Artificial_Intelligence_and…

Experience is becoming part of the education question

Internships have traditionally occupied an interesting place in higher education. They are often treated as the point at which a student briefly leaves academic life to experience the “real world.”

That distinction is becoming less useful.

If workplaces are changing quickly, professional exposure can also function as a feedback mechanism for education.

Students encounter current technologies, workflows, expectations, and problems. Employers see where emerging talent is well prepared and where capability gaps remain. Educators gain a clearer view of how industries are evolving.

The World Economic Forum therefore calls for continuous employer-educator alignment, including real-time feedback and co-designed curricula. WEF_Artificial_Intelligence_and…

This changes the role of industry engagement.

An employer partnership should not simply produce an internship slot or appear on a university website. Its educational value comes from what moves in both directions: students into professional environments, and current professional knowledge back into education.

For institutions such as Enderun Colleges, this reinforces the importance of an educational model that connects academic study with internships, industry engagement, projects, and applied learning.

The objective is not to train a student for one narrowly defined job.

It is to shorten the distance between knowing something and knowing how to use it.

AI makes foundational knowledge more important, not less

There is an irony in the discussion around AI and education.

The easier technology makes it to produce an answer, the more important it becomes to know whether that answer deserves to be trusted.

A student can ask an AI system to summarize information, generate ideas, analyze data, produce an image, draft a proposal, or write code. These capabilities can accelerate work considerably.

But acceleration is not the same as understanding.

Part Two of the World Economic Forum report raised concerns about what happens when automation removes tasks that previously helped junior employees develop judgment and capability. Part Three similarly examined the need to preserve foundational experiences as early-career professionals begin working with sophisticated AI tools.

Education sits upstream from both problems.

A graduate who has developed disciplinary knowledge has something against which to evaluate AI output. A graduate who understands only how to prompt a system may have considerably less basis for deciding when the system is wrong.

This is why preparing students for an AI-enabled workplace cannot mean teaching them to surrender difficult thinking to AI.

They need enough expertise to challenge it.

Human capability still has to be developed

AI changes the mix of capabilities required at work, but it does not remove the need for human judgment.

The report’s education framework focuses on equipping learners for changing workplace demands rather than preparing them around a fixed set of roles. WEF_Artificial_Intelligence_and…

That difference is important.

Specific tools will change. Job descriptions will change. Some tasks will disappear, while new ones will emerge.

A graduate therefore needs more than proficiency in the technology available at the moment of graduation.

They need the capacity to learn.

They need to work with other people, evaluate information, solve unfamiliar problems, communicate ideas, exercise judgment, and understand enough of their chosen field to recognize when something does not make sense.

These capabilities are hardly new. AI simply makes their value more visible.

The paradox of an increasingly technological workplace may be that knowing how to use technology becomes expected, while the ability to think beyond what the technology produces becomes more distinguishing.

Universities and employers need a shorter conversation

For much of modern education, universities and employers have occupied adjacent worlds.

One develops graduates. The other hires them.

The World Economic Forum’s framework suggests that this relationship needs to become more continuous. It calls for stronger feedback between employers and educators so education and training can remain aligned with changing workplace needs. WEF_Artificial_Intelligence_and…

That can take several forms.

Industry professionals can contribute to curriculum development. Employers can participate in projects and applied learning. Internship experiences can reveal emerging skill requirements. Universities can identify where students struggle when academic knowledge encounters professional practice.

The objective should not be to allow employers to dictate education.

Universities have responsibilities that extend beyond producing immediately employable workers. They develop disciplinary knowledge, intellectual independence, ethical reasoning, research capability, and broader understanding.

The opportunity lies in connecting those foundations more effectively with the environments in which graduates will eventually use them.

Academic rigor and professional relevance do not need to compete.

Done well, they strengthen each other.

Learning will continue after graduation

Perhaps the most significant adjustment is psychological.

Education has often been understood as something that precedes a career. You study first. Then you work.

An AI-enabled economy makes that sequence increasingly difficult to sustain.

When technologies and skill requirements continue to evolve, graduation cannot represent the completion of professional learning. It marks the point at which responsibility for that learning increasingly shifts to the individual and the organizations in which they work.

The World Economic Forum’s education framework consequently extends beyond universities. It includes education, training, employers, and alternative pathways because capability development must continue throughout working life. WEF_Artificial_Intelligence_and…

For students, this changes what being “prepared” should mean.

No university can teach everything a graduate will need across a career that may last four decades. Nor can anyone reliably identify every technology that will matter during that period.

A more realistic objective is to leave university with strong foundations, meaningful experience, and the ability to keep learning when the next change arrives.

Preparing for a career that will keep changing

Across its four dimensions, the World Economic Forum report tells a connected story.

AI is affecting access to entry-level work.

It is changing how those jobs are designed.

It is forcing organizations to reconsider how future talent develops.

And ultimately, it is asking education systems how they will prepare people for careers whose requirements will continue to move.

There is no single curriculum change that answers that question.

Universities need stronger connections with industry. Students need opportunities to apply what they learn. Employers need to participate more actively in developing emerging talent. Alternative learning pathways need room to grow alongside traditional degrees. And graduates need to accept that professional education will not end when they receive a diploma. WEF_Artificial_Intelligence_and…

The future of education is therefore not about trying to outrun AI.

It is about giving people the foundations to keep moving when the technology does.

For a student choosing a college today, that may be one of the more useful questions to ask.

Not simply, “What will I learn?”

But, “Will I learn how to keep learning?”