Abstract. Production systems are being expanded to include Digital Twins (DTs)

as part of increased industrial digitalization. DTs can bring benefits e.g., increase

visibility, safety, and accessibility of the system. Further, digital experimentation

can reduce time and cost. Though, application of DT technologies involves

challenges i.e., model accuracy or errors in transferring data or codes between the

DT and the physical twin. Many studies on DTs focus on industrial applications.

However, DT technology has potential for implementation of digital labs in

education. This aspect of DTs is of rising importance as distance education has

increased over the last decade and access to physical laboratories can be restricted

due to factors such as the Covid-19 pandemic. Thus, there is a need to study the use

of DT technology in higher education. To address this, we investigate possibilities

and challenges of applying DT technology in education to conduct industrial-like

labs virtually. A case of an automation line, with full scale industrial equipment,

based at a research center, is focused. Results emphasize that the application of DT

technologies require multi-domain expertise to understand the consequences of

every single decision in the design process on every piece of equipment involved,

making the modelling process complex and time consuming. Thus, when applied in

education, test procedures need to be designed to focus on students’ motivation,

improved learning and understanding of production systems. DTs are considered

enabling technologies supporting the concept of Industry 5.0, thus stressing the

human-centric aspects of advancing Industry 4.0. The predicted application of DTs

emphasizes the need for educational curricula that include laboratory applications

and theoretic understanding of DT technologies. This study focusses the application

of DT technologies in higher education curricula, but the result of the study can

contribute to other areas such as automation and virtual commissioning towards

smarter manufacturing