Introductory Courses on Digital Twins: an Experience Report

We describe and compare two new courses on model-based approaches to the engineering of Digital Twins. One course was delivered to doctoral students from a range of largely non-computational backgrounds, and the other to Masters students with computing experience. We describe the goals, content and delivery of the courses, and review experience gained to date. Key lessons focus on the importance of providing common baselines for participants coming from diverse technical backgrounds.

💡 Research Summary

The paper reports on the design, delivery, and evaluation of two introductory courses on Digital Twins (DT) aimed at distinct learner populations. The first course was offered at Newcastle University to a multidisciplinary cohort of early‑career doctoral researchers working on water infrastructure. It was a 5‑credit (ECTS) module delivered over two intensive weeks. The learning objectives focused on understanding DT concepts from a systems‑engineering perspective, evaluating the potential of AI in water, critiquing models, data sources, and analytic techniques, and debating challenges of deploying dependable DTs. The curriculum combined plenary lectures, hands‑on labs, and industry/academic seminars, using real sensor data from the FAIR Water project (55 homes) as a case study. In the second week, students formed cross‑institutional groups to develop a DT‑enabled solution for one of three scenarios (homes, campus, city). They were required to conduct stakeholder analysis, define a limited set of top‑level services, outline system considerations, data sources, assets, service constellations, risk factors, and lifecycle management, and present their work to examiners. Feedback indicated high engagement and appreciation of relevance, though some participants felt the coding exercises were too basic and that the proposed architectures lacked detailed service tiering and integration pathways.

The second course was delivered at Aarhus University to master’s students from Electrical & Computer Engineering, Mechanical & Production Engineering, and Civil & Architectural Engineering. It was a 10‑credit (ECTS) course with a stronger technical emphasis, requiring students to engineer a complete DT, including artefact identification, model selection and calibration, sensing and communication methods, and service design. Learning outcomes encompassed comparing PT models, evaluating calibration techniques, assessing data communication, discussing alternative DT services, and delivering a functional DT prototype. Assessment combined a group report and an oral examination, with the “incubator DT” example serving as the primary reference for implementation.

Both courses share common pedagogical principles: (1) provision of a baseline of DT fundamentals to accommodate diverse backgrounds; (2) use of real‑world case studies and datasets to bridge theory and practice; (3) team‑based projects to foster interdisciplinary collaboration and critical thinking. The authors highlight that the depth and difficulty of content must be tailored to the audience: non‑computational doctoral students benefit from conceptual understanding and evaluation skills, whereas computationally experienced master’s students require deeper technical training and hands‑on system design. Key lessons include the necessity of establishing common baselines, the value of industry‑academic guest seminars for contextual relevance, and the importance of deliberately forming heterogeneous teams to maximize cross‑disciplinary learning. The paper concludes that such structured, audience‑aware curricula can effectively prepare a new generation of DT engineers capable of operating in multidisciplinary environments and advancing the broader adoption of Digital Twin technologies.