Data-centric engineering and AI skills for engineers

Report overview

This report is a national blueprint for embedding data-centric engineering and AI skills for engineers in higher education. It sets out how UK universities can transform engineering education to meet the demands of a data-driven and AI-enabled future.

As industries across the board in sectors including manufacturing, infrastructure, transport, and energy rapidly adopt digital and AI-enabled technologies, demand is growing for engineers who can work confidently with data-intensive systems.

Engineering education must evolve accordingly if the UK is to remain globally competitive and support future innovation, resilience, and sustainability goals. Universities play a critical role in preparing graduates with the knowledge, skills and behaviours needed to lead this transformation.

This report marks the first release from the Skills Observatory, a key function of the Academy’s National Engineering Skills Centre. The Skills Centre is a new centre of expertise that will upskill and reskill engineers at scale, responding to the UK’s chronic and growing shortfalls in engineering skills capacity. 

Download the report (6.2MB) (PDF)

“Data engineering is now a fundamental part of engineering practice in the twenty-first century. That’s why this nationwide initiative to map how it's being taught across the country is so important. This report provides crucial evidence for how data-centric engineering can be better embedded across higher education, while also offering a practical framework to support wider international discussions about the engineering skills that graduates will need in the future.”

Professor Adam Sobey, Mission Director of Sustainability at the Alan Turing Institute

“The application of big data principles in the engineering sector has potential to deliver huge benefits to society. By providing evidence on how these principles are applied in the national engineering curricula, and identifying where improvements can be made, this report marks a big step in understanding and managing the new risks to safety introduced by digital and AI-enabled technologies."

Tim Slingsby, Director of Skills and Education at Lloyd’s Register Foundation

“Data is now a crucial commodity for engineering businesses, but we are seeing that engineers and technicians do not yet have the right skills to take full advantage of the improved engineering outcomes that data can provide across the whole engineering lifecycle. This report, Data-centric engineering and AI skills for engineers is the first output from the Academy’s new Skills Centre and its Skills Observatory, the purpose of which is to provide insight on future skills for engineers and technicians working in a world of increasingly interconnected and complex systems and rapidly emerging new technology.”

Dr Rhys Morgan, Director of Education and Skills at the Royal Academy of Engineering and report co-author

Key report findings

Drawing on curriculum analysis and engagement with leading academics, the report highlights how DCE is currently embedded in UK engineering degree programmes. It sets out the learning outcomes for engineering undergraduates in UK higher education institutions aligned to DCE and the use of AI tools for engineering, across seven thematic areas:

Foundational statistics

Advanced Statistics

Data engineering and management

Data analytics

Data governance

ML and AI for data analytics

Additional AI skills for engineering

The findings show that while core skills like statistics and data analytics are well established, advanced areas such as data engineering, machine learning, and advanced AI remain inconsistently integrated. The findings reinforce a sector-wide consensus that DCE should be embedded across the curriculum as a core, cross-cutting capability to prepare graduates for data-driven engineering roles.

There is a broad consensus across the academic community that the curriculum is already too full, and institutions face challenges with limited staff capacity. Rather than adding content to already full programmes, DCE should be integrated throughout existing courses, supported by greater use of laboratories, project-based learning, and real-world applications.

Recommendations to embed data-centric engineering in higher education

Two-tier data-centric engineering outcomes map

Universities should adopt a two-tier data-centric engineering outcomes map (baseline for all; advanced for some) with explicit assessment blueprints tied to application and critique.

This baseline should include learning outcomes in foundational statistics, data handling, analytics, and data governance and ethics. In addition, institutions should offer a second tier of specialist DCE components, including advanced statistics, machine learning and AI, and large-scale data engineering, that should be offered as elective modules for those who want to specialise in this area.

Integrated practice-based DCE learning

Ensure integrated, practice-based DCE learning through laboratories and project-based approaches.

To embed DCE effectively, universities should integrate it into existing laboratories, design work, and capstone projects, incorporating elements such as data management, modelling, and decision-making. Greater use of simulation and digital tools will be essential, particularly for complex systems, supported by flexible teaching resources and reusable project templates. Where resources are limited, institutions should prioritise a small number of high-quality lab or project experiences, balancing hands-on learning with validated simulations and realistic staff capacity.

Institutional capacity at scale

UK engineering higher education should build a community of practice to support teaching and learning in data engineering, including sector-wide micro-credentials on statistics for engineers, machine learning literacy, and the general growing use of AI in engineering education.

A community of practice should be established to support the development and dissemination of professional development opportunities for academics and teaching staff – in statistics and data analytics for engineers, data engineering, ethics and governance around data and machine learning and AI literacy. The community of practice will help to support new pedagogy, spread good practice and reduce duplication of effort.

Programmes such as the Academy’s Visiting Professors can help support with real-life applications of data engineering from industry.

Accreditation and standards alignment

Propose explicit data engineering language in Engineering Council accreditation (AHEP 5) and other skills standards, using national exemplars to guide programmes.

Professional bodies and sector partners should explore explicit data engineering language in the next iteration of the Engineering Council accreditation standard for higher education, AHEP 5 for Bachelor’s (level 6) and Integrated (MEng) and standalone (MSc) Master’s (level 7), supported by national exemplars and constructive alignment between outcomes, teaching activities, and assessment evidence. Where possible, the learning outcomes included in this report should also be aligned with the recently produced UK Standard Skills Classification.

Ensure inclusion and widen participation in DCE pathways

To broaden participation, programmes should use diagnostics and just-in-time student support in subject areas such as maths, coding, and data handling, alongside inclusive communication and societally-relevant examples linked to sustainability, infrastructure, and safety-critical systems.

Enable sector coordination and a targeted implementation pathway

Use a maturity pathway model: Emerging → Adopters → Innovator.

Using this model to support a staged approach aligned to the institutional typologies identified in the analysis:

Type A Emerging: enable minimum viable DCE (core outcomes + starter packs)

Type B Adopters: deepen practice-based learning (laboratories/projects)

Type C Innovators: scale exemplars and mentor others

Update teaching, learning and assessment

As DCE and AI tools become more ubiquitous in engineering, universities need to redesign assessment to foreground judgement, explanation and critique. The Community of Practice should work collaboratively with the HE community to develop guidance on AI tools in teaching, learning and assessment.

What happens next?

We will continue to support the integration of DCE across tertiary education, including extending this work to engineering and related technician qualifications in further education. In higher education, this support will be guided by the report’s recommendations, helping institutions embed DCE more consistently and effectively.

While focused on the UK higher education system, the report is also designed to inform broader international discussions on the future of engineering education, digital transformation, and the skills needed to build a globally competitive workforce.

Acknowledgements

The report has been developed by the Royal Academy of Engineering in partnership with The Alan Turing Institute and Lloyd’s Register Foundation.

Authors

Dr Rhys Morgan (Royal Academy of Engineering)

Dr Gabin Kayumbi (The Alan Turing Institute)

Steering Group

Chair: Professor Adam Sobey (The Alan Turing Institute, University of Southampton)

Members

Professor Tim Broyd FREng CEng (University College London)

Professor Elizabeth Cross (University of Sheffield)

Lydia Amarquaye CEng (The Institution of Mechanical Engineers)

Professor John Chudley CEng (Engineering Council)

Poppy Harrison CEng (AtkinsRéalis)

Katy Henderson (The Alan Turing Institute)

Professor Claire Lucas CEng (Aston University)

Professor Omar Matar FREng CEng (Imperial College London)

Dr Vera Matser (The Alan Turing Institute)

Dr Thomas Popham (University of Warwick)

Dr Annalisa Riccardi (University of Strathclyde)

David Short FREng CEng (BAE Systems)

Dr Tim Slingsby (Lloyd’s Register Foundation)

The authors would like to thank the many academics who contributed to the survey and qualitative interviews.

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