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The new skills order
From critical thinking and metacognition to AI literacy and micro-credentials, a new hierarchy of skills is emerging for the age of intelligent machines.
By Seb Murray
"The more significant shift is not what AI can produce, but what humans must now contribute."
"The human role becomes one of oversight: deciding what to trust, what to challenge and what to change."
“Machines and AI can help to amplify productivity and effectiveness, but only so far. People are the vital component in unlocking the full potential of AI.”
"The signals from employers, students and universities point in the same direction: AI is changing what counts as a skill."
In brief
- Generative AI is upending traditional degrees, shifting the value of education from simple knowledge recall to uniquely human skills like critical thinking and ethical judgement.
- As machines automate routine tasks, demand for "oversight" capabilities is soaring. Students are increasingly pairing traditional degrees with micro-credentials to prove practical, AI-literate workforce readiness.
- Universities must fundamentally redesign assessments and curricula. Success lies in training staff and students to verify, improve, and responsibly use machine-generated insights in a professionalised workforce.
Generative artificial intelligence has unsettled a long-standing assumption in higher education: that the ability to recall and apply knowledge has been one of the main ways graduates prove they are ready for work.
Systems that can summarise, draft text and create code in seconds have forced universities to question the assessment tasks that machines can now do too. In 2023, a professor at the Wharton Business School, Christian Terwiesch, found that ChatGPT would have earned a B grade on his MBA operations management exam; the chatbot was strong on structure and analysis, weaker on maths.
For Rose Luckin, Emeritus Professor of Learner Centred Design at University College London, the implication is clear. AI is “increasingly capable of performing the kinds of knowledge recall and application tasks that have traditionally been at the heart of university assessment”.
The premium, she argues, is shifting towards “the elements of human intelligence that AI cannot replicate”: understanding how you think and learn (metacognition), collaborative problem solving and making ethical judgements.
That view is echoed at the policy level. Andreas Schleicher, Director for Education and Skills at the OECD, notes that while many routine and predictable cognitive tasks can now be undertaken using AI, demand for “higher-order cognitive abilities – such as critical thinking – will be sustained”.
Yet employers are already reporting gaps in skills. According to the 2026 Talent Shortage Survey by recruitment firm ManpowerGroup, AI-related skills are now the hardest to find — not specialist research expertise, but basic literacy and confidence in using the tools.
As this year’s QS World University Rankings by Subject are published, universities face a sharp question: if AI can reproduce knowledge, what exactly should a degree now prove?
If AI is putting assessment under pressure, it is also reshaping the hierarchy of skills.
The more significant shift is not what AI can produce, but what humans must now contribute. As Professor Luckin argues, these systems can generate plausible answers, but they do not understand what they produce. That distinction matters for students, who must develop “a genuine understanding of what AI actually is and how it works” — not simply how to prompt a tool, but how to recognise its limits and predictable failures.
“This is not about learning to use particular tools, which will come and go,” Professor Luckin argues. “Large language models predict plausible text rather than retrieve verified facts.”
In practice, this shifts the emphasis towards skills that sit above subject knowledge. Professor Luckin highlights the importance of students being able to judge what they know and do not know, to regulate their own thinking and to justify decisions. The question is no longer whether a student can produce an answer, but whether they can assess its quality — especially when a machine has helped to generate it.
Data from online learning platforms suggests this shift is already under way. Marni Baker Stein, Chief Content Officer at Coursera in California, says enrolments in generative AI courses reached 15 per minute in 2025, up from eight per minute in 2024. But alongside the surge in AI courses, enrolments have also risen sharply in courses focused on things like debugging; spotting and fixing errors in code and systems.
Also, among people working in data-related roles and learning through their employers, enrolments in critical thinking courses rose by 168 percent year-on-year, while courses in data quality and data cleansing more than doubled. The pattern suggests a growing focus on checking and correcting what AI systems produce, rather than simply generating more content.
For Schleicher at the Paris-based OECD, this pattern is somewhat predictable. As routine tasks are handed to machines, the human role becomes one of oversight: deciding what to trust, what to challenge and what to change. Schleicher tell QS Insights: “It is crucial that students develop their capacity for independent thinking and analysis and do not become blindly reliant on potentially inaccurate content generated by AI.”
For all the warnings about jobs being lost to automation, employers are not reporting a glut of talent. They are reporting the opposite in some labour markets.
According to a survey by recruitment firm ManpowerGroup, 73 percent of UK employers report difficulty finding the skilled workers they need. The strain is particularly acute in the automotive sector, where 92 percent of companies report shortages.
Engineering remains the hardest skill set to source, followed by manufacturing and production. Public services, health and social care are also struggling to recruit enough skilled staff, despite sustained investment in training.
Notably, AI-related skills are now the toughest to find, cited by 19 percent of companies. As Michael Stull, Managing Director of ManpowerGroup UK, puts it: “Machines and AI can help to amplify productivity and effectiveness, but only so far. People are the vital component in unlocking the full potential of AI.”
Employers are responding not by only replacing staff, but by investing in them. Upskilling and reskilling existing employees is now the most common response to shortages, cited by 33 percent of organisations in the ManpowerGroup report. By contrast, only 10 percent point to automation as a primary solution, and 9 percent to outsourcing – moving the work outside the company. All this points to a simple fact for universities and students: AI may change how work is done, but it has not removed the need for people able to do it well.
While employers report shortages, students are moving quickly to build new skills.
The growth in demand for AI-related learning has been rapid. But the shift is not limited to AI tools alone. Online learning platform Coursera’s data shows that learners continue to study the basic digital skills needed to keep existing systems running – such as SQL, JSON and web applications – even as they add new AI skills on top. Enrolment data suggests that learners understand the need to work with AI rather than simply rely on it.
Students are also changing how they think about qualifications, an important finding for universities. Micro-credentials are gaining ground. Coursera’s data suggests that 94 percent of students want micro-credentials to count towards a degree, up from 55 percent in 2023. Globally, 77 percent of learners say they are more likely to enrol in a degree programme that offers them.
Employers appear receptive as well. According to the same data, 85 percent are more likely to hire a candidate who holds a micro-credential.
For Baker Stein, these trends reflect a broader reality: “The pace at which learners and employees need to acquire skills has accelerated to unprecedented rates,” she argues, “and the way in which we deliver and verify skills needs to accelerate in similar fashion.”
For many students, a degree on its own no longer feels like enough.
Universities face a more structural task: assessment redesign.
For Professor Luckin at UCL – a long-standing adviser to governments on AI in education – institutions cannot respond to it with minor tweaks to the rules. The shift runs deeper than that. She identifies three priorities.
First, curriculum. Universities should use AI to handle more of the basic delivery of knowledge to students, freeing academic staff to focus on developing what she calls “the distinctly human capabilities” of their pupils. “This is not about diminishing the role of educators,” says Professor Luckin. “It is about enabling them to do the deeper, more sophisticated work that only humans can do.”
Second, assessment. Professor Luckin argues that there is a clear contradiction in preparing students for an AI-rich workplace while assessing them as if AI does not exist. “Assessment must change fundamentally,” she asserts. Students should demonstrate that they can think critically and work alongside AI systems, not simply produce answers without them. Assessment, in her view, must better reflect the conditions graduates will actually face; a labour market being upended by AI.
Third, staff capability. “Educators need to understand AI themselves before they can teach students to work with it effectively,” she says. Too often, AI tools are introduced in universities without properly involving the academics who understand how students learn. Professors, Professor Luckin argues, should help shape how AI is used in universities, rather than simply being told to adopt it.
But approaches are far from uniform across universities, the OECD’s Schleicher notes. There is, he suggests, a case for greater coordination so that students are “more systematically exposed to best practice” in how AI is used.
Taken together, the signals from employers, students and universities point in the same direction: AI is changing what counts as a skill.
It is no longer enough to produce information. Graduates must be able to check it, improve it and use it responsibly. They still need strong subject knowledge and practical skills, and they will need to keep learning throughout their careers.
A new skills order is beginning to take shape.