Digital Twins and Living Labs: a new paradigm for advanced education and capacity building

Digital Twins and Living Labs: a new paradigm for advanced education and capacity building

Author: Sebastiano Martignano, research strategy advisor -CRF Italy

 Abstract

As the Euro-Mediterranean region navigates the dual challenges of ecological transition and technological transformation, the question of how territories build knowledge, skills, and resilience becomes central. While infrastructure and data are essential, they are not sufficient: capacity building—the ability of regions to learn, adapt, and innovate—is what ultimately defines long-term sustainability.

In this context, the convergence between Living Labs and Digital Twin systems offers a unique and powerful response. It combines the material presence of operational systems with the modeling, simulation, and interoperability capacities of digital infrastructures. But beyond optimization and monitoring, it enables something deeper: a new paradigm for advanced education, technical training, and institutional learning.

From passive users to active producers of knowledge

Traditional models of technical assistance or training often treat territories as recipients of pre-defined knowledge: content is developed elsewhere, standardized, and transferred. This approach underestimates the diversity of local conditions—and misses the opportunity to activate the full potential of territorial systems as learning environments.

A network of Living Labs, each embedding a real and operational Digital Twin, changes this dynamic. These are not abstract simulations detached from reality. They are working systems—agro-industrial plants, water-energy loops, solar fields, circular economy platforms—instrumented and virtualized to support modeling, scenario design, and decision-making in real time.

Training within such a system is no longer about absorbing concepts. It becomes situated learning: engineers, technicians, planners, and administrators interact with live data, test responses to simulated shocks, explore policy scenarios, and engage in cross-border comparisons. In doing so, they become co-creators of models, not just users of tools.

Advanced learning for a distributed technical culture

Digital Twins embedded in Living Labs enable a new generation of training programs that go beyond classic disciplines. Participants learn how to:

• Integrate physical processes with computational models
• Work with semantic metadata and interoperable structures
• Simulate system behavior under variable and uncertain conditions
• Adapt interfaces and control strategies to local constraints
• Collaborate across nodes with shared frameworks but autonomous logic

This opens the door to modular and scalable curricula, adapted to the specific configuration of each node, but also part of a larger ecosystem. A greenhouse in Tunisia, a battery reuse hub in Italy, and a logistics platform in Morocco may all run different systems—but they can participate in a common learning architecture, exchanging experiences, methods, and improvements in real time.

Moreover, this infrastructure supports non-technical learning as well: governance actors can visualize the implications of planning decisions; SMEs can test business models in safe environments; communities can engage with resource flows through transparent, interactive interfaces.

Capacity building as territorial intelligence

The ultimate goal is not just to train individuals, but to build territorial intelligence: the collective ability of a region to sense, learn, respond, and evolve in the face of dynamic challenges. This involves:

• Localizing innovation, so that new technologies are not just imported but appropriated, transformed, and re-applied;
• Retaining knowledge within institutional and educational ecosystems;
• Supporting decision-makers with real-time feedback and modeling capacity;
• Fostering cross-institutional cooperation across public, private, and academic domains.

In this sense, Digital Twin–based Living Labs become permanent infrastructures for capacity building, not just technical demonstrators. They are always on, always learning, and always open to integration with new actors, questions, and programs.

Implications for Euro-Mediterranean development

For the Euro-Mediterranean region, this model holds particular strategic value. It provides a mechanism to balance asymmetries, enabling each territory to build competence on its own terms, while participating in a federated network. It strengthens regional cooperation through shared operational languages, rather than political harmonization. And it creates a platform for continuity, where capacity building is not an add-on, but a built-in function of the system.

In this perspective, Living Labs with Digital Twins are more than a pedagogical innovation. They are a structural response to fragmentation, dependency, and disconnection. They turn cooperation into co-production, training into transformation, and learning into resilience.

References

• Ballon, P., Pierson, J., & Delaere, S. (2005). Test and experimentation platforms for broadband innovation: Examining European practice. Proceedings of the 16th European Regional Conference, Porto.
• Boschert, S., & Rosen, R. (2016). Digital Twin—The Simulation Aspect. In Mechatronic Futures (pp. 59–74). Springer. https://doi.org/10.1007/978-3-319-32156-1_5
• European Commission. (2020). Digital Education Action Plan 2021–2027: Resetting education and training for the digital age. COM(2020) 624 final.
• Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
• Grieves, M., & Vickers, J. (2017). Digital Twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7_4
• European Network of Living Labs (ENoLL). (2021). Living Labs: A practical guide for co-creation and open innovation. ENoLL Publications.
• Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging Digital Twin technology in model-based systems engineering. Systems, 7(1), 7. https://doi.org/10.3390/systems7010007
• Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital Twin: Values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143
• Schaffers, H., Komninos, N., Pallot, M., Trousse, B., Nilsson, M., & Oliveira, A. (2011). Smart cities and the future Internet: Towards cooperation frameworks for open innovation. In The Future Internet Assembly (pp. 431–446). Springer. https://doi.org/10.1007/978-3-642-20898-0_31
• Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. https://doi.org/10.1016/j.jmsy.2018.01.006