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Digital Twin Technology: Necessity of the Future in Education and Beyond

Robertas Damasevicius1* and Ligita Zailskaite-Jakste2

1Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania

2Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania

Abstract

This chapter provides an overview of digital twin technology (DTT) and its applications in various industries including education. DTT involves the creation of exact digital replicas of physical entities that can reflect real-time changes in the underlying entity. The chapter explores the potential of DTT for enhancing the learning experience and improving the educational outcomes through immersive hands-on learning, replication of real-world scenarios for scientific inquiry and problem-solving, and personalization of the learning process. The chapter also examines the challenges of implementing DTT in education, including technical, pedagogical, ethical, and privacy concerns. It also discusses the potential of DTT for shaping the future of education and beyond as well as future research directions.

Keywords: Digital twin technology, education, virtual reality, avatar, immersive learning, personalized learning, real-world scenarios

1.1 Introduction

Digital twin technology (DTT) allows the creation of an exact digital replica of a physical entity and continuously feeds it with real-time data from integrated sensors and other devices [6]. The foundation of DTT is the ability to simulate the behavior of a physical entity in a virtual environment [62]. By gathering data from sensors and other devices, the digital twin is able to mirror the actions, interactions, and changes of the underlying physical entity in cyberspace [61]. This real-time data flow creates a bridge between the physical and digital worlds, allowing us to understand and predict the behavior of the physical entity in ways that were never before possible [72]. However, the true power of DTT lies in its ability to go beyond mere observation and prediction. With the use of advanced algorithms and simulations, the digital twin can be used to improve the performance of the physical entity and even to control it in real time. This opens up new possibilities for innovation and optimization in fields such as manufacturing, transportation, healthcare, etc. [10]. It can change the way we design, build, and operate the systems that shape our world, and it is a necessary component for the Industry 4.0 Revolution [35], the future of education, and beyond. DTT has a wide range of applications across various industries. Some of the most notable applications include the following:

• Manufacturing: DTT can be used to optimize the performance of production lines and machines by simulating and predicting their behavior under different conditions. This can help to identify and resolve bottlenecks, reduce downtime, and improve overall efficiency [18].
• Transportation: DTT can optimize the performance of transportation systems, such as trains, buses, and logistics networks [52]. By simulating and predicting the behavior of these systems, it is possible to identify and resolve issues, improve efficiency, and reduce costs.
• Healthcare: DTT can be used to optimize the performance of medical equipment, such as MRI scanners and X-ray machines, and to simulate and predict the behavior of patients [31], which can reduce costs and improve patient outcomes.
• Aerospace: DTT can be used in the aerospace industry to optimize the performance of aircraft and spacecraft by simulating and predicting their behavior under different conditions [41]. This can help to improve safety and increase efficiency.
• Infrastructure design: DTT can be used to optimize the performance of buildings, bridges, and other structures by simulating and predicting their behavior under different conditions [5]. This can help to reduce costs and improve safety.
• Smart cities: DTT can be used to optimize the performance of smart cities by simulating and predicting the behavior of infrastructure, transportation systems, and other key components of the city [64]. This can help to reduce costs, improve safety, and increase efficiency.
• Agriculture: DTT can be used to improve the efficiency and productivity of agricultural operations by optimizing planting, irrigation, and fertilization schedules, monitoring the health and well-being of livestock, monitoring and optimizing the performance of agricultural equipment, creating accurate weather forecasts, creating predictive models of crop yields, and monitoring the farm remotely. This can help farmers in making more informed decisions and can improve the overall efficiency and productivity of their work [46].
• Energy and utilities: DTT can be used to optimize the performance of energy and utility systems, such as power plants, water treatment facilities, and pipelines (see "Teaching Factory" [44]). By simulating and predicting their behavior under different conditions, one can identify and resolve issues, improve efficiency, and reduce costs.
• Retail: DTT can be used to optimize the performance of retail operations, such as stores, warehouses, and logistics networks [19]. By simulating and predicting their behavior under different conditions, it is possible to identify and resolve issues, improve efficiency, and reduce costs.

These are just a few examples of the many different applications of DTT across various industries. As technology continues to advance and the data generated by digital twin systems becomes more accurate, the potential for DTT to improve business processes, reduce costs, and increase efficiency is expected to continue to grow [55].

The aim of this chapter is to examine the role and impact of DTT in shaping the future of education and beyond. It aims to provide an overview of DTT and its applications in various industries and delve into the specific ways in which DTT can be utilized in education. The chapter explores the potential of DTT to support the evolution of intelligence and personalization in education and discusses the challenges and limitations that must be considered in the implementation of DTT in education.

The novelty of this chapter is that it provides a comprehensive examination of DTT and its potential impact on education, which has not been fully explored before. This chapter brings together the latest research and developments in DTT and education to present a holistic view of the current state of the field. The chapter also discusses the challenges and limitations that must be considered in the implementation of DTT in education, which is an important aspect that is rarely highlighted in other studies.

The contribution of this chapter is that it highlights the potential of DTT to shape the future of education and beyond. It argues that DTT is a necessary component for enhancing the learning experience and improving outcomes and that its potential must be fully realized in order to achieve this. The chapter contributes to the understanding of the challenges and limitations that must be considered in the implementation of DTT in education. By highlighting these challenges, the chapter aims to help researchers and practitioners navigate them and ensures that DTT is implemented in the most effective way possible.

1.2 Digital Twins in Education

1.2.1 Virtual Reality for Immersive Learning

Virtual reality (VR) and computer vision (CV) are two technologies that can be used to enhance the learning experience through immersive hands-on learning when combined with DTT. VR is an artificial simulation of a 3D environment which a person can explore and interact with [51]. VR can be used to create immersive and interactive learning environments that allow students to experience and interact with virtual objects and scenarios as if they were real [36]. This allows students to have hands-on experience in simulations and helps them to better understand and retain the teaching material [66]. CV is a field of artificial intelligence (AI) that deals with the development of systems that can interpret and understand visual information [23]. CV can be used to enhance the realism of virtual environments and to track the movement of students in VR. This allows students to interact with virtual environments in a more natural and intuitive way and enhances the sense of immersion in the learning experience [65].

When combined with DTT, VR and CV can be used to create immersive learning environments that replicate real-world scenarios [58]. This allows students to practice and experience the material in a realistic way and helps them to better assimilate and retain the material-for example, in a medical education context, students can practice performing surgeries in a VR environment that replicates a real-world scenario, which can help them to better understand and retain the material and also to be better prepared for real-world scenarios. The use of VR and CV in combination with DTT also allows for the creation of personalized learning experiences-for example, using CV methods, the system can track and analyze the student's movements and interactions in the virtual environment and provide feedback and guidance accordingly. This allows for more individualized instruction and support and can help students to learn at their own pace.

Another advantage of...