Pearson & AWS warn of UK AI skills gap in graduates

Pearson and Amazon Web Services have published UK research on AI readiness among learners and employers. The study found that 14% of UK learners consider themselves highly prepared for an AI-enabled workforce.

The findings point to a gap between exposure to AI in education and the ability to apply it in workplace settings. Among employers surveyed, 43% said graduates struggle to use AI knowledge in real-world situations. Meanwhile, 47% of learners said the best preparation for an AI-enabled career is a mix of university education and practical work experience.

Focusing on the transition from higher education into employment, the report argues that universities and employers need to align more closely on what AI readiness means in practice. Employers are looking not only for technical familiarity with AI tools, but also for judgement, communication, collaboration and adaptability.

The results come as concerns about graduate employment and job preparedness continue to shape debates over the role of universities in labour market outcomes. The research suggests that while many students use AI tools, they do not believe that use alone leaves them ready for work.

Sharon Hague, UK Chief Executive Officer at Pearson, said: "AI is changing the expectations graduates face as they move from education into the world of work. The UK benefits from world-class institutions and strong graduate adaptability, but without the right skills this potential will not translate into the workplace. High youth unemployment and an impetus among businesses to drive enterprise value highlight the need to help students build, practise and prove AI-ready capability that prepares them for work."

Conducted with AWS, the study examines what it describes as friction points between learning and employment. It introduces an AI Readiness Friction Framework, which sets out six areas where progress is being held back across the education-to-work pathway.

Key gaps

According to the framework, one issue is the pace at which curricula and institutional decisions adapt to changing workplace demands. Another is the connection between universities and employers, with the report calling for stronger feedback loops from industry into teaching and course design.

It also highlights staff training, governance, practical experience and broader skills development. The report argues that institutions should invest more in faculty and instructor understanding of AI, set clearer guidance on acceptable AI use in learning and assessment, and create more structured opportunities for students to apply AI in workplace-like contexts.

On skills, the emphasis is on combining technical knowledge with judgement and other qualities that employers say remain important when AI is part of daily work. The report identifies strategic intelligence, ethical stewardship and other human skills as priorities alongside direct familiarity with AI systems.

Valerie Singer, General Manager of Global Education at AWS, said: "Students are using AI, but usage alone isn't what employers are looking for. They want evidence that graduates can apply AI to solve real problems. The gap isn't access, it's the distance between exposure and proof. Programmes like the AWS Skills to Jobs Tech Alliance give students hands-on experience so they leave with skills they can show, not just tools they've tried. That's how we make sure what students learn actually prepares them for the workforce."

Workplace focus

A central theme in the research is that access to AI tools is no longer the main issue. Instead, the challenge is whether learners can demonstrate that they know when and how to use those tools effectively in a professional environment.

That distinction matters for employers hiring graduates into roles where AI is becoming a routine part of work. The survey results indicate that organisations want recruits who can combine AI use with critical thinking and clear communication, rather than simply show they have experimented with the technology.

The report also points to apprenticeships and other forms of work-based learning as one route to narrowing the gap. It suggests students need more opportunities to practise with AI in settings that resemble workplace tasks, rather than encountering the technology only in abstract or isolated academic exercises.

For higher education institutions, the findings add to pressure to review how quickly courses are updated and how closely content reflects employer expectations. For policymakers, they add evidence to a wider debate over productivity, graduate outcomes and the skills needed as AI reshapes entry-level work.

Only 14% of UK learners in the study said they were highly AI-ready, a measure defined as combining AI knowledge with the human skills needed to apply it effectively.