The Role of Digital Twins in Future Skill Development

Abstract

A digital twin is a virtual system designed to replicate the functionality of a physical object. The concept originating from the industrial sector offers promising avenues for creating immersive, interactive, and personalized learning experiences. This paper explores the potential applications of digital twin technology in the field of education. In order to ascertain the context and methodology of the use of digital twins in education, we present the findings of our literature analysis prepared using the PRISMA approach. Furthermore, we explore the opportunities associated with integrating digital twin technology into educational environments and propose future directions for research and development.

Full text

The Role of Digital Twins in Future Skill Development
Marija Kuštelega, Ahmed Shareef, Renata Mekovec
Faculty of Organization and Informatics
University of Zagreb
Pavlinska 2, 42000 Varaždin, Croatia
{marija.kustelega, ahmed.shareef, renata.mekovec}@foi.unizg.hr
Abstract. A digital twin is a virtual system designed to
replicate the functionality of a physical object. The
concept originating from the industrial sector offers
promising avenues for creating immersive, interactive,
and personalized learning experiences. This paper
explores the potential applications of digital twin
technology in the field of education. In order to
ascertain the context and methodology of the use of
digital twins in education, we present the findings of
our literature analysis prepared using the PRISMA
approach. Furthermore, we explore the opportunities
associated with integrating digital twin technology into
educational environments and propose future
directions for research and development.
Keywords. digital twin, education, enhancement
1 Introduction
Digital twins (DTs) have already been used for
distance learning in several fields, including
healthcare, tourism, and engineering education
(Hawkinson, 2022). It is emphasized that COVID 19
technology has accelerated the adoption of digital twin
technology (Hazrat et al., 2023). Namely, the
emergence of the coronavirus pandemic forced
professors and students to use distance learning
methods to ensure continuous education (Itani et al.,
2022).
The rise of the virtual world, or metaverse
technology, has significantly enhanced pedagogical
and technical support for education, expanding
learning opportunities and demonstrating numerous
benefits in distance learning (Tlili et al., 2022).
According to EU Business School (2022), one of the
most promising applications of the metaverse in
education is the "gamification" of learning. This
approach turns the classroom into a virtual world and
encourages students to finish their assignments.
Digital twin technology can help incorporate the
ideas and concepts of the natural or physical world in
metaverse-based education, making education more
realistic and user-friendly (Mitra, 2023).
Some of the benefits of introducing DT into the
educational context are personalized learning, better
collaboration possibilities and content accessibility
(Hawkinson, 2022). It has been shown that blended
learning can significantly improve students’
motivation, interest and learning approach (Kartashova
et al., 2024).
At the same time, it is emphasized that such
education requires investment in professional
development of educators to increase their ability to
teach students (Hazrat et al., 2023).
The statistics of DT in education show that they
will continue to gain popularity due to the shift to
online learning that has occurred. According to the
National Center for Education Statistics (NCES), the
pandemic affected public colleges to enroll
approximately 7.5 million online students in 2022
(U.S. Department of Education, 2023). In 2021, as in-
person learning slowly returned, distance learning
dropped to 59%. Even though online learning rates
decreased slightly in 2022, they were still significantly
higher than in the years prior to the pandemic
(Hamilton and Beagle, 2024).
Moreover, it is also clear that investments in
educational technologies have significantly increased.
Between 2017 and 2021, US educational technology
venture funding jumped from $1 billion to $8 billion,
and this growth is anticipated to continue as more
universities adopt blended learning and advanced
technologies (HolonIQ's Education Intelligence Unit,
2022).
The metaverse is expected to transform cities,
schools, and factories, resulting in a digital twin of
everything. As a result, the impact of these changes on
higher education could be fatal. The Pew Research
Center (2023) predicts that 50% of US colleges could
close due to outdated facilities and inability to adapt to
new technologies. Though some faculties actively use
DT in their teaching.
For example, Quanser's QLabs software has been
used by 2500 academic institutions worldwide to allow
engineering students to manipulate and test 3D models
in a virtual environment (Quasner.com, n.d.). Such
statistics show that digital technology is slowly being
applied in education.
The aim of this paper is to discover the possible
applications and advantages of using DT in an
educational context.
To achieve this aim, two guiding questions drive
this study:
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• RQ1: What are domains/applications of DT usage
in education?
• RQ2: What are the possible enhancements of
introducing DT in education?
The rest of the paper is structured as follows:
Section 2 explains the theoretical framework of DT;
Section 3 outlines utilized research methodology;
Section 4 presents the results about DT applications in
education; Section 5 discusses findings about possible
DT enhancements in learning environment; Section 6
outlines research gaps and future directions to address
the research gaps; and Section 7 concludes the work.
2 Theoretical Framework
2.1 Definition and Conceptualization of
Digital Twins
DTs are virtual representations of physical objects or
systems that simulate their behavior and
characteristics. The concept originated from NASA's
Apollo program, where identical space vehicles were
built for testing purposes (Liljaniemi & Paavilainen,
2020; Andreasyan and Balyakin, 2022). DT consist of
two systems: a physical entity and a virtual model
containing all relevant information about the physical
system (Nikolaev et al., 2018). They are used in
various sectors, offering benefits such as cost
reduction, predictive maintenance, and improved time-
to-market (Tjahyadi et al., 2023; Flaga & Pacholczak,
2022). DT leverage technologies like IoT, machine
learning, and big data to enable seamless interaction
between physical and digital domains, providing
detailed insights for optimization and innovation
(Andreasyan and Balyakin, 2022). In education, DT
can include human factors, such as students and
teachers, requiring specific considerations during
development (Terkaj et al., 2024).
2.2 Key Components and Characteristics
DTs in education involve creating digital replicas of
educational entities like students, teachers, and
institutions, integrating human factors. Key
components include generating digital copies of
educational entities, data collection, and real-time data
transmission (Liljaniemi & Paavilainen, 2020;
Chamorro-Atalaya et al., 2024). Characteristics
encompass the synchronization between physical and
digital systems, visualization of system information,
and integration of technologies like IoT and AI
(Andreasyan and Balyakin, 2022). DTs enable
simulations, virtual environments for testing, and data-
driven applications, enhancing learning experiences by
providing hands-on applications of ICT skills and
exposure to Industry 4.0 technologies (Geuer et al.,
2023; Acker et al., 2023). They offer opportunities for
virtual experiments, preparation for real-world
scenarios, and continuous data flow between physical
objects and DT (Raudmäe et al., 2023; Orsolits et al.,
2022), fostering a deeper understanding of complex
concepts in a safe and interactive manner (Tjahyadi et
al., 2023).
2.3 Types of Digital Twins
DT can be categorized into three main types: Digital
Model, Digital Shadow, and Hybrid Digital Twins. The
Digital Model represents a digital replica of a physical
object without automated data exchange (Kim et al.,
2018). On the other hand, the Digital Shadow involves
an automated one-way data flow between physical and
digital objects (Liljaniemi & Paavilainen, 2020).
Hybrid DTs combine elements of both physical and
cyber representations, allowing for bidirectional data
exchange and synchronization between the physical
entity and its digital counterpart (Andreasyan and
Balyakin, 2022). These different types of DTs play
crucial roles in various industries such as
manufacturing, smart city development, and
healthcare, offering benefits like predictive
maintenance, improved efficiency, and enhanced
decision-making capabilities (Lee et al., 2023).
3 Methodology
For a literature review, the Preferred Reporting Items
for Systematic Reviews and Meta-Analyses
(PRISMA) technique was utilized with defined
eligibility criteria (Moher et al., 2009). The PRISMA
procedure was used to identify publications related to
DTs in education. The search was conducted using
"digital twin" in titles, abstracts, and keywords and
"education" included only in the title of the paper to
narrow the search. All posters, book chapters, reports
and reviews are excluded from the search, also
excluding all publications not written in the English
language. Final search results were obtained on April
10, 2024. A total of 116 papers were found in the initial
search using the Scopus and WoS databases (69 from
Wos and 47 from Scopus). Following the elimination
of duplicates, a brief review of the titles and abstracts
was conducted. As a result, 66 articles (29 from Wos
and 37 from Scopus) that were relevant to the field of
DT in education were chosen. After reading the paper
in its entirety, 37 publications (19 from Wos and 18
from Scopus) that provided examples of usage DT in
education with defined benefits were selected for
analysis. To get the most recent data on their
applications, the investigation only included English
language publications that were released within the last
three years, from 2022 to 2024.
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4 Applications of DT in Education
Table 1 shows the areas and applications of DT, where
most of them were observed specifically in STEM
education (21 articles), online education (9 articles),
vocational education (2 articles) and 1 article each for
special cases were found in areas such as
environmental education, sustainable education, IoT
education, vocal music, and physical education.
Table 1. Main areas and applications of DT in education
No.
Author
Area
Application
1.
Chamorro-Atalaya et al.
(2024)
STEM education
DT in higher education for engineering training and
simulation-based training.
2.
Hagedorn et al. (2023)
STEM education
DT for project-based learning in engineering.
3.
Acker et al. (2023)
STEM education
Low-cost DT for teaching robotics (Industry 4.0).
4.
Raudmäe et al. (2023)
STEM education
DT for omnidirectional mobile robot platform.
5.
Zangl et al. (2023)
STEM education
DT for measurements in robotics and AI.
6.
Čech and Vosáhlo (2022)
STEM education
DT to enhance control education affordability.
7.
Tjahyadi et al. (2023)
STEM education
Digital twin-based laboratory for control engineering.
8.
Bunse et al. (2022)
STEM education
Distance laboratory courses in engineering education.
9.
Boettcher et al. (2023)
STEM education
DT for laboratory experiments in engineering.
10.
Christopoulos et al.
(2022)
STEM education
DT and virtual reality in robotics education.
11.
Kandasamy et al. (2022)
STEM education
DT for cyber security testing, research and education.
12.
Orsolits et al. (2022)
STEM education
DT to facilitate mixed reality-based robotics.
13.
Samak et al. (2023)
STEM education
DT to support simulation for autonomous vehicles.
14.
Hazrat et al. (2023)
STEM education
Enhance engineering education through Industry 4.0.
15.
Mathur (2023)
STEM education
DT to support cybersecurity research, education, and
training for preserving critical infrastructure.
16.
Peshkova et al. (2023)
STEM education
Digital twin usage in medical education.
17.
Goppold et al. (2022)
STEM education
DT for visualizing error consequences in health.
18.
Rovati et al. (2024)
STEM education
Developing a patient DT for critical care education.
19.
Gary et al. (2023)
STEM education
DT in critical care training.
20.
Geuer et al. (2023)
STEM education
Designing smart photometry in Education 4.0.
21.
Lee et al. (2023)
STEM education
DT in math education (enhance gamified learning).
22.
She et al. (2023)
Online education
DT for enhancing credit management in education.
23.
Mourtzis et al. (2023)
Online education
DT hybrid model for personalized education 4.0.
24.
Andreasyan and Balyakin
(2022)
Online education
Implementing DT technology in educational
practices (application of education digitalization).
25.
Hsiao et al. (2022)
Online education
DT to support real-time expert feedback.
26.
Rubtsova et al. (2021)
Online education
DT for optimizing learning effectiveness.
27.
Dhananjaya et al. (2024)
Online education
Digital recommendation system for personalized
learning in online education.
28.
Mitra (2023)
Online education
DT for innovative metaverse-based education.
29.
Xi and Cong (2022)
Online education
DT like a virtual tour in survey education.
30.
Flaga and Pacholczak
(2022)
Online education
DT for education and training purposes (bridge real
and virtual worlds).
31.
Eriksson et al. (2022)
Vocational
education
Applying DT in higher education (example of
industrial-like laboratories).
32.
Vrysouli et al. (2022)
Vocational
education
DT to enhance vocational education in construction
(sustainability and architectural design).
33.
Ruppert et al. (2022)
IoT education
DT of the laboratory (education for Industry 5.0).
34.
Komninos and Tsigkas
(2022)
Environmental
education
Prototyping DT system (combining smart birdhouse
and an electronic DT).
35.
Georgakopoulos et al.
(2023)
Sustainable
education
Remote lab courses for distance education (designed
on the sustainable development principles).
36.
Intelligence (2023)
Vocal Music
Education
Application of the DTs platform for music education
in higher institutions.
37.
Liu and Jiang (2022)
Physical
Education
DT to improve somatosensory recognition efficiency
(visual sensing training system).
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5 Enhancements of Introducing DT
This chapter will highlight five categories in which DT
is believed to contribute to enhancement when utilized
in the educational process.
5.1 STEM Education
By 2030, higher education is expected to undergo
substantial changes. Launching DT will improve
learning effectiveness and open up new research
options (Rubtsova et al., 2022). Increased robot use in
industrial production necessitates adaptation of
education programs. Combining immersive
technologies like mixed reality with robotics education
offers promising opportunities for expanding
knowledge (Orsolits et al., 2022). The use of digital
twin technologies can motivate students to study and
improve learning outcomes (She et al., 2023). In STEM
education, a portable photometric measurement device
called the Smart Education Photometer (SmaEPho)
designed for inquiry-based learning, overcomes the
challenges of limited budgets in purchasing
professional photometric measurement systems (Geuer
et al., 2023). This makes it easier for students to learn
about electrical circuits and photometry, allowing them
to test and build circuits while lecturers use DT as a
demonstrator.
Furthermore, online learning feature analysis
technology improves online education management by
integrating students' cognitive data and teachers'
resources (Intelligence, 2023). Christopoulos et al.
(2022) are proposing an approach which can be utilized
to teach robotics in blended learning scenarios. The
networked virtual world accurately represents
laboratory robots and tools, whereas the digital twin
system simulates actions that occur in the physical
laboratory.
5.2 Virtual Laboratories and Simulations
Virtual labs are extensively used in chemistry, physics,
biology, environmental science, and medical education
to simulate experiments that may be too dangerous,
expensive, or time-consuming to perform in a
traditional lab setting (Geuer et al., 2023; Mourtzis et
al., 2023; Gary et al., 2023). In medical education, DT
can be used to create a database of data that will help
in further research of diseases analysis (Peshkova et al.,
2023). The use of digital twins in engineering
education provides a safe environment for students to
explore the behavior of complex machinery and control
systems without the need for physical prototypes
(Chamorro-Atalaya et al., 2024; Acker et al., 2023;
Andreasyan and Balyakin, 2022; Dhananjaya et al.,
2024; Samak et al., 2023; Terkaj et al., 2024; Tjahyadi
et al., 2023). Chamorro-Atalaya et al (2024) further
described simulations based on DT as advanced
interactive learning environments in engineering
disciplines allowing students to virtually replicate
laboratory equipment or practical teaching stations
(Mourtzis et al., 2023; Samak et al., 2023; Andreasyan
and Balyakin, 2022; Dhananjaya et al., 2024). Virtual
simulations are crucial in medical education
(Chamorro-Atalaya et al., 2024) for providing a risk-
free environment for students to practice surgical
procedures, patient care, and diagnostic skills
(Hagedorn et al., 2023; Rovati et al., 2024). Modeling
using tools like Simscape in a DT based laboratory can
aid in visualizing mathematical models and concepts,
helping students understand abstract concepts through
interactive and engaging visual representations
(Tjahyadi et al., 2023). Such visualizations could also
be used for describing the process of the DT to better
understand the real-time reflection of environmental
changes that can be further used in various
environmental studies.
Hsiao et al. (2022) introduced a cyber-physical co-
existence environment for practical distance learning
activities - CPE: "Co-existing Practical Environment"
which contains a "Holographic Wearable Device" for
behavior sensing and vision sharing, a cloud "Digital
Twin Model" database for expert variable correction in
real time, and a discussion interface.
Georgakopoulos et al. (2023) proposes remote
laboratory courses based on the educational principles
for sustainable development. The benefits of this
strategy include helping students acquire critical
thinking, problem solving, and collaborative
competencies. DT leads to increase student motivation
(Terkaj et al., 2024; Acker et al., 2023; Chamorro-
Atalaya et al., 2024), encourage self-responsibility for
learning (Bunse et al., 2022; Chamorro-Atalaya et al.,
2024), facilitate peer learning (Bunse et al., 2022), and
improve content delivery (Terkaj et al., 2024; Acker et
al., 2023), demonstration ease (Lee et al., 2023), and
student assessment (Lee et al., 2023; Mourtzis et al.,
2023).
5.3 Personalized Learning Environments
Personalized learning is a process in which the method
of learning and instructions are adjusted or
personalized to the learner's requirements (Mitra,
2023). AI-driven technology has the potential to
revolutionize education by providing personalized
learning experiences (Dhananjaya et al., 2024). Digital
twins could contribute to medical education and
improve diagnostics of diverse forms of disease where
there is a need for personalized treatment strategies in
healthcare (Peshkova et al., 2023).
Intelligence (2023) explores the use of DTs
technology in music education, focusing on improving
teaching quality through the integration of real and
virtual teaching spaces and online learning data
analytics. It further suggests that a well-designed DT
system can enhance students’ interest in music learning
by facilitating interaction and providing real-time
feedback. Chatbots and virtual assistants can enhance
student education by providing personalized
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assistance, answering questions, offering feedback,
and suggesting learning resources (Dhananjaya et al.,
2024). This demonstrates how DT can be tailored to
individual students, maximizing their potential.
5.4 Economic Alternatives
In robotic education, the open-source omnidirectional
mobile robot platform (ROBOTONT) can offer
professional tools for robotics education and provide
researchers a portable platform for validating scientific
results (Raudmäe et al., 2023), providing a DT based
economical alternative for conducting research when
access to a physical platform is unavailable (Acker et
al., 2023; Eriksson et al., 2022; Flaga et al., 2022).
Acker et al. (2023) offer a way for secondary school
students to study Industry 4.0 technologies, build ICT
skills, and establish a practical learning environment
by utilizing a low-cost digital twin prototype. The
prototype enables STEM skills development and
critical thinking through simulations, testing, and
implementation of simple algorithms, facilitating real-
time data exchange between simulation environments
and robotic systems.
Zangl et al. (2023) are introducing a low-cost robot
platform that can be used to handle a wide range of
measurement science and sensor subjects, as well as
machine learning, actuators, and mechanics. The 3D
printed chassis may be outfitted with various sensors
for environmental perception while being adaptable to
a variety of embedded computer platforms.
To present principles of robot simulation and
realization approaches in a hands-on manner, the table-
top robot is also available as a digital twin. Xi and Cong
(2022) describe how Tianjin University's field trip for
a measured survey of built heritage was replaced with
remote solutions that included on-site data gathering,
post-processing, online instruction, observation,
modeling, and delivery. Čech and Vosáhlo (2022)
present a cost-effective approach for teaching digital
twins in control engineering education, demonstrating
their application using a gantry crane simulator.
Komninos and Tsigkas (2022) describe usage of
DT to give children the ability to interact with the
natural environment from the classrooms, which
consists of a smart birdhouse and an electronic DT
where the smart birdhouse is placed in a natural setting
to collect atmospheric data, such as humidity and
temperature measurements, as well as record sounds
and images of potential birds. At the same time, the
collected data will be available to the end user via a
dashboard, which plays the acquired music or images.
5.5 Develop Skills
Digital twin technology can promote development of
multidisciplinary skills (Acker et al., 2023; Bunse et
al., 2022; Flaga et al., 2022; Lee et al., 2023; Samak et
al., 2023), thus preparing students to cope with the
future demands of the industry (Hazrat et al., 2023).
Some authors propose a framework to prepare students
for the development and application of Industry 5.0
technologies (Ruppert et al., 2022; Acker et al., 2023;
Eriksson et al., 2022; Lee et al., 2023; Tjahyadi et al.,
2023). To better incorporate the increasing competency
requirements of the working world 4.0 Boettcher et al.
(2023) employed a real-world scenario (RWS) to
address the ambiguous problem-solving assignments
in W4.0. DT concept could also be used for smart grid
security studies, allowing users to gain experience
testing attacks and countermeasures in a controlled
setting (Kandasamy et al., 2022). Mathur (2023)
highlights its utility in research, education, and training
in critical infrastructure defense. It could be used in
various fields to improve not only the mental but also
physical abilities of students. For example, Liu and
Jiang (2022) explore the application of DT in physical
education teaching practice, involving 25 participants
in an exercise detection and analysis experiment,
aiming to design a platform for efficient information
collection and processing.
On the other hand, Vrysouli et al. (2023) is
introducing ideas of DT and sustainability in the
vocational program in the sector of construction works,
structured environment and architectural design.
Goppold et al. (2022) presents a learning system based
on learning from errors. In a technical proof-of-
concept, DTs are used to simulate and see the harmful
consequences of erroneous acts.
6 Future Directions
While existing studies discuss the benefits of digital
twins in various educational contexts, there is a gap in
a comprehensive, comparative analysis of how these
benefits vary across different educational levels
(elementary, secondary, and higher education). In
addition, ethical implications and privacy concerns
associated with the use of digital twins in education are
not thoroughly explored. This includes issues related to
data security, consent, and the potential misuse of
personal data. There is a lack of adequate education
about the digital twin concept, which is why efforts
should be made to develop cost-effective teaching
strategies to bridge the gap between education,
research, and industrial practice (Čech and Vosáhlo,
2022). Acker et al. (2023) and Orsolits et al. (2022)
also agree that cost-effective solutions should be
pursued to improve robotics education, which will be
critical in meeting future industry demand. Not only for
its cost-effectiveness, it is also necessary for
simulations that otherwise would not be possible to
perform like offensive security testing (Kandasamy et
al., 2022). DT has often been associated with the IoT,
but visual sensor technology is now increasingly being
explored to collect data without need for wearable
devices (Liu and Jiang, 2022). This new perspective on
the digital twin will raise various open questions,
including the privacy threat it might bring.
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7 Conclusions
The use of digital twin technology has yielded several
benefits in a wide range of fields, including STEM
education, distance learning, vocational training, and
other fields including music, physical education,
internet of things and environmental education. The
advantages of DT are most evident in STEM education,
particularly in the areas of robotics and engineering,
where it acts as a mold for conducting a variety of
experiments. As an applicable economic alternative for
developing multidisciplinary competencies, it has
gained popularity and is employed for both virtual
laboratory enhancement and diverse simulation
purposes. It helps to create personalized learning
environments in which the instructions and learning
strategy are customized to meet the needs of each
individual student.
While there are educational benefits of digital
twins, they should be taken with caution. The existing
studies do not fully address ethical implications and
privacy concerns, such as data security, permission,
and the use of personal data. Future studies should
therefore examine the responsible use of digital twin
technology, particularly when combined with other
emerging technologies like augmented reality and
artificial intelligence tools. The digital twin could be a
useful tool for creating future learning environments
with a focus on bridging the gap between academics
and industry because of the advantages it provides. For
this reason, digital twin-based virtual environments
may have an impact on future directions for
educational practice and policy.
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