News

A new academic paper from researchers at Queen Mary University of London is proposing a radical redesign of undergraduate education, blending the best elements of degree apprenticeships with traditional university teaching to create a model fit for the age of artificial intelligence (AI).

Since the UK introduced Degree and Higher-Level Apprenticeships, employers and universities have widely adopted work-based learning to create impactful opportunities for diverse learners.

After a decade of experience delivering degree apprenticeships in higher education – with Queen Mary the first Russell Group university to introduce them – there is now valuable, long-term insight that can help improve pedagogy further, not only for apprenticeships but more broadly across the sector.

Published in Higher Education, Skills and Work-based Learning (May 2026), this new research outlines how lessons from apprenticeship delivery can be used to reshape full-time degrees, with a particular focus on preparing students for a fast-changing, AI-driven workplace.

A world-leading collaboration

Led by Queen Mary but developed in collaboration with leading researchers and scholars in education, this paper brings together expertise from across pedagogy, work-based learning and curriculum design, combining academic rigour with real-world insight.

The approach is explicitly interdisciplinary and outward-facing, informed not only by research evidence but also by direct engagement with global thought leaders. As part of the development process, the team travelled to Silicon Valley to consult with world-leading universities and education experts—including advisers who had previously informed US federal policy—on how institutions are responding to the rise of generative AI.

A new hybrid model of higher education

At the heart of the research is a bold new idea: the future of undergraduate education lies in combining the strengths of two traditionally separate models.

Degree apprenticeships have long been recognised for integrating academic study with practical, work-based learning and close employer collaboration.

Traditional degrees, meanwhile, provide theoretical depth and disciplinary foundations.

This paper argues that by merging these approaches, universities can create a new kind of undergraduate programme—one that is academically rigorous, deeply embedded in industry and designed from the ground up for an AI-enabled world.

Industry embedded from day one

A defining feature of the proposed model is its close integration with industry.

Rather than treating employability as an outcome at the end of a degree, the approach embeds industry engagement from the very first week. Students encounter real-world challenges early and continuously, with curricula co-designed alongside employers to ensure relevance and responsiveness to emerging demands for skills.

This reflects one of the paper's core pedagogical principles: "integrated employability and industry collaboration", identified as a key lesson from degree apprenticeship delivery.

Rethinking assessment in the age of AI

The research also responds directly to one of the most pressing challenges facing higher education: the impact of generative AI on assessment and academic integrity.

Rather than focusing narrowly on traditional outputs—such as essays that can be easily assisted by AI—the model prioritises:

critical thinking development

authentic, real-world assessment

application of knowledge in context

Authentic assessment, a central principle highlighted in the paper, mirrors workplace practices and makes student thinking more visible, reducing reliance on easily automatable outputs.

This shift is significant. Across the sector, universities are grappling with how to maintain academic standards as AI tools become ubiquitous. By emphasising process, reflection and real-world problem-solving, the model is designed to be inherently more resilient to these challenges.

From outcomes to learning process

A further departure from conventional approaches is a reduced emphasis on predefined outcomes.

Instead, the model foregrounds the learning process itself—encouraging curiosity, adaptability and deeper intellectual engagement. This aligns with wider research suggesting that, in an AI-rich world, education should prioritise skills that are difficult to automate, including creative and analytical thinking.

A testbed for future AI education

The paper uses the example of a new Applied AI undergraduate degree as a "testing ground" for these ideas. The programme introduces three integrated learning streams—foundations, tools and applications—underpinned by principles such as programme coherence, inclusive learning environments and industry collaboration.

Students from Queen Mary University of London have shared their experiences:

Rhea Sidhu, 1st year student on the Applied AI degree

SPC4001 was really beneficial for CV building, as it gave us the freedom to apply machine learning methods that interested us, while ensuring we were exposed to both classification and regression problems. This proved incredibly useful when applying for internships, it helped me secure a Product and UX internship at a startup, where I worked on building an AI-powered recommendation quiz. As someone who aspires to become an AI consultant, having the opportunity to complete projects across different contexts has been instrumental in developing the skills I need to achieve that goal.

Amr Hasanin, 1st year student on the Applied AI degree

I've found the programme's practical approach to be one of its strongest aspects. Many of the assessments are built around real-world scenarios, from evaluating LLM performance to developing ML-based solutions, which reflects some elements of the work I do in my AI business.

Even in the first year, the course has provided a solid foundation in applying AI tools to real problems. With plans to bring in external industry briefs in the following years, it's shaping up to be one of the closest experiences to a degree apprenticeship I know of, but within a university setting.

I would recommend the course to those looking to enter the AI field, particularly if they value hands-on, applied learning alongside their studies.

Leading edge—and globally distinctive

Taken together, the research presents one of the most ambitious attempts yet to rethink undergraduate education in response to artificial intelligence.

By combining world-class scholarship, deep industry co-design, and insights from global leaders in education, it moves beyond incremental reform to propose a fundamentally new model—one that integrates academic and workplace learning from the outset, and places critical thinking at its core.

At a time when universities worldwide are searching for sustainable responses to generative AI, the paper positions this approach not simply as an adaptation, but as a step change.

As the authors suggest, it may represent a blueprint for the next generation of undergraduate education—one that is as much about how students think as what they produce.

Paper details:

Using degree apprenticeships to shape the future of traditional undergraduate degrees was published in Higher Education, Skills and Work-based Learning on May 5, 2026 and can be accessed here:

https://www.emerald.com/heswbl/article/doi/10.1108/HESWBL-07-2025-0290/1363983/Using-degree-apprenticeships-to-shape-the-future