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Citation: Kefalis, C.; Skordoulis, C.;
Drigas, A. Digital Simulations in
STEM Education: Insights from
Recent Empirical Studies, a
Systematic Review. Encyclopedia 2025,
5, 10. https://doi.org/10.3390/
encyclopedia5010010
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Systematic Review
Digital Simulations in STEM Education: Insights from Recent
Empirical Studies, a Systematic Review
Chrysovalantis Kefalis 1,2 , Constantine Skordoulis 2and Athanasios Drigas 1, *
1Net Media Lab & Mind & Brain R&D, Institute of Informatics & Telecommunications, National Centre of
Scientific Research ‘Demokritos’, 15341 Athens, Greece; vkefalis@iit.demokritos.gr
2Department of Primary Education, National and Kapodistrian University of Athens, 10680 Athens, Greece;
kskordul@primedu.uoa.gr
*Correspondence: dr@iit.demokritos.gr
Abstract: This study explores the use of digital simulations in STEM education, addressing
the gap in systematic reviews synthesizing recent advancements and their implications
for teaching and learning by focusing on their impact on learning outcomes and student
engagement across general and special education settings. The review includes 31 peer-
reviewed empirical studies published in the last five years, sourced from ERIC, Scopus, and
Web of Science, and adheres to the PRISMA methodology to ensure transparency and rigor.
The findings reveal that interactive simulations are the most widely used type of digital
tool, accounting for 25 of the 31 studies, followed by game-based simulations and virtual
labs. Quasi-experimental designs dominate the research landscape, often employing pre-
and post-tests to evaluate intervention effectiveness. While inquiry-based learning emerges
as the most frequently implemented instructional strategy, hybrid and simulation-based
approaches also feature prominently. Despite the evident benefits of digital simulations in
enhancing conceptual understanding, engagement, and problem-solving skills, research
gaps remain, particularly regarding their application in primary and special education
contexts. This review underscores the need for diverse research methodologies and broader
population studies to maximize the potential of digital simulations in STEM education.
Keywords: STEM education; digital simulations; inquiry-based learning; learning outcomes;
student engagement
1. Introduction
The integration of digital tools and applications in education has revolutionized learn-
ing experiences across general and special education contexts. Research has highlighted
the significant role of mobile applications in supporting students with specific needs, such
as those on the autism spectrum in secondary education, by enhancing their learning
processes and engagement levels [
1
]. Similarly, mental imagery applications have demon-
strated potential for improving learning disabilities and mental health, showcasing the
transformative impact of technology on education [
2
]. Innovations in speech and language
therapy through ICTs have also opened up new avenues for intervention, providing tai-
lored solutions for diverse learner populations [
3
]. Furthermore, STEM education, coupled
with metacognitive strategies, has emerged as a critical area for supporting students with
specific learning disabilities. Online learning tools for coding and robotics are another
example of how digital technologies empower learners by enabling access to practical
STEM-related activities, fostering both engagement and skill development [4–6].
Encyclopedia 2025,5, 10 https://doi.org/10.3390/encyclopedia5010010
Encyclopedia 2025,5, 10 2 of 18
In recent years, educational simulations have become increasingly prominent in STEM
education, providing dynamic, interactive environments that support experiential learn-
ing. Digital simulations, encompassing virtual labs, interactive models, and AR-based
applications, are computer-based tools designed to replicate real-world processes or sys-
tems. These simulations enable learners to interact with and manipulate variables within a
virtual setting, facilitating experiential learning and a deeper understanding of scientific
concepts [
7
,
8
]. Digital simulations, including virtual labs and interactive models, enable stu-
dents to explore complex scientific phenomena, practice problem-solving skills, and deepen
their understanding of core concepts in ways that traditional methods may not facilitate.
The effectiveness of these tools in fostering engagement and enhancing learning outcomes
has sparked substantial interest among educators and researchers alike, particularly for
both general and special education contexts.
Despite the increasing use of digital simulations in STEM education, there is no
systematic review that provides a comprehensive understanding of recent advancements
and synthesizes findings across different scientific disciplines. This study fills this gap by
offering an integrated perspective on modern approaches and the educational impacts of
simulations. Previous reviews on AR in science education [
9
] have explored specific aspects
of digital simulations. However, these studies focused narrowly on AR applications, leaving
a broader examination of diverse simulation tools across STEM disciplines unaddressed.
However, despite the growing body of literature, there remains a need for a compre-
hensive understanding of how digital simulations contribute to measurable educational
outcomes across various student populations and educational levels. This systematic
review seeks to analyze recent empirical studies that examine the impact of digital simula-
tions on learning outcomes and student engagement in STEM education, thereby offering
insights into best practices and highlighting areas for further investigation.
Research Questions
1.
What are the predominant research designs employed in studies examining the effec-
tiveness of digital simulations in STEM education?
2.
What types of digital simulations are most commonly employed in STEM education?
3.
What intervention categories are most commonly implemented in studies utilizing
digital simulations in STEM education?
These questions will guide a systematic exploration of recent empirical evidence,
focusing on the educational implications and methodological rigor of digital simulations
within the STEM domain.
2. Methodology
In order to create a robust foundation for the review, three major academic databases
were selected: ERIC, Scopus, and Web of Science. The reason why these three databases
were selected was that they were suitable for the focus of the research. The ERIC database
mainly focusses on educational research, and provides access to a wide range of studies on
educational technology in general and special education. Scopus and Web of Science were
used in order to expand our research, since they provide access to high-impact journals in
the fields of science, technology, engineering, and mathematics (STEM). By incorporating
these three databases, the search approach sought to find a wide range of research that was
pertinent to both general and special education, with a focus on using digital simulations
to improve engagement and learning outcomes. This review focuses on studies published
within the last five years (2019–2024) to ensure the inclusion of recent advancements in
digital simulation technologies and their applications in STEM education. This time frame
aligns with rapid technological development in educational tools and methodologies.
Encyclopedia 2025,5, 10 3 of 18
To ensure a transparent and systematic review process, the PRISMA (Preferred Re-
porting Items for Systematic Reviews and Meta-Analyses) methodology was followed. The
PRISMA guidelines provided a structured approach for documenting each stage of the
study selection, from identification through to screening and final inclusion.
The first step to identify studies best suited to our research questions involved creating
a comprehensive set of search terms and filtering criteria tailored to each database. For
ERIC, the search string was as follows: (‘STEM education’ OR ‘science education’ OR
‘technology education’ OR ‘engineering education’ OR ‘mathematics education’) AND
(‘simulations’ OR ‘virtual simulations’ OR ‘virtual labs’ OR ‘interactive simulations’) AND
(‘learning outcomes’ OR ‘student engagement’) NOT (‘literature review’ OR ‘systematic
review’) AND (‘experimental study’ OR ‘case study’ OR ‘quasi-experimental’). Filters
included peer-reviewed journal articles published in the last five years. For Scopus, we
used a similar string: (‘STEM education’ OR ‘science education’ OR ‘technology education’
OR ‘engineering education’ OR ‘mathematics education’) AND (‘simulations’ OR ‘virtual
simulations’ OR ‘virtual labs’ OR ‘interactive simulations’) AND (‘learning outcomes’ OR
‘student engagement’) AND (‘experimental study’ OR ‘case study’ OR ‘quasi-experimental’)
AND NOT (‘higher education’ OR ‘adult learners’); in addition, we limited the results to
articles published from 2019 to 2024. For Web of Science, the following string was used:
(‘STEM education’ OR ‘science education’ OR ‘technology education’ OR ‘engineering
education’ OR ‘mathematics education’) AND (‘simulations’ OR ‘virtual simulations’ OR
‘virtual labs’ OR ‘interactive simulations’) AND (‘learning outcomes’ OR ‘student engage-
ment’) AND (‘experimental study’ OR ‘case study’ OR ‘quasi-experimental’). We also
excluded reviews and limited the results to the last five years.
2.1. Inclusion Criteria
•Studies focusing on general and special education across various educational levels.
•
Studies specifically involving digital simulations (e.g., virtual simulations, virtual labs,
interactive simulations).
•Studies reporting measurable outcomes related to learning or student engagement.
•
Empirical studies with a clear research design (e.g., experimental, case study, quasi-
experimental).
•Peer-reviewed journal articles published within the last five years.
2.2. Exclusion Criteria
•
Studies involving digital tools other than simulations (e.g., general digital technology
or non-simulation-based tools).
•Studies not focusing on STEM education.
•Studies targeting professional education as the main population.
•Non-empirical studies (e.g., theoretical papers, literature reviews).
Out of the 57 studies in our original records, we retrieved 48, after manually removing
4 duplicate studies and excluding 1 review article. The titles and abstracts of the remaining
studies were screened by two independent reviewers (Authors 1 and 2) to ensure accuracy,
with disagreements resolved through discussion or a third reviewer (Author 3). Following
this process, 5 studies were excluded because they did not use digital simulations, but
used other digital tools; 5 were excluded because they did not focus on STEM education;
2 were excluded because their target group was professional education; and 5 were ex-
cluded because they were not empirical studies. While a formal risk of bias framework
(e.g., ROBIS) was not used, potential biases were minimized through a consistent screening
process conducted independently by the two reviewers (Authors 1 and 2). This included
Encyclopedia 2025,5, 10 4 of 18
evaluating the study design, methodology, and reporting to ensure reliability and relevance
to the research questions.
The PRISMA flow diagram [
10
] shown in Figure 1illustrates this process, detailing
the initial number of articles identified in the database search, the application of exclusion
criteria, and the resulting set of 38 studies included in the review.
Encyclopedia 2025, 5, x FOR PEER REVIEW 4 of 18
framework (e.g., ROBIS) was not used, potential biases were minimized through a con-
sistent screening process conducted independently by the two reviewers (Authors 1 and
2). This included evaluating the study design, methodology, and reporting to ensure reli-
ability and relevance to the research questions.
The PRISMA flow diagram [10] shown in Figure 1 illustrates this process, detailing
the initial number of articles identified in the database search, the application of exclusion
criteria, and the resulting set of 38 studies included in the review.
Figure 1. PRISMA flow diagram.
3. Results
This section presents the results of the systematic review, organized around the re-
search questions. Table 1 summarizes the characteristics of the included studies, including
their methodologies, target populations, and key findings.
Table 1. Summary of studies included in review.
Author(s) Year Study Design STEM Subject Intervention Category Level
AYASRAH, Firas
Tayseer Moham-
mad et al. [11]
2024 Quasi-experimental Physics Direct instruction with simu-
lations Secondary
Hüseyin Ateş, Mus-
tafa Köroğlu [12] 2024 Quasi-experimental Science Hybrid/blended learning with
simulations Secondary
Demelash, M., An-
dargie, D., and Be-
lachew, W. [13]
2024 Quasi-experimental Chemistry Project-based learning with
simulations Upper secondary
Yu-Chen Chiang,
Shao-Chieh Liu [14] 2023 Quasi-experimental Engineering Inquiry-based learning with
simulations Secondary
ALARABI, Khaleel
et al. [15] 2022 Quasi-experimental Physics Hybrid/blended learning with
simulations Secondary
Kari Kleine, Elena
Pessot [16] 2024 Case study Engineering Simulation-based assessment Upper secondary
Badarudin, R., and
Husna, A. F. [17] 2024 Quasi-experimental Engineering Simulation-based assessment Upper secondary
Figure 1. PRISMA flow diagram.
3. Results
This section presents the results of the systematic review, organized around the re-
search questions. Table 1summarizes the characteristics of the included studies, including
their methodologies, target populations, and key findings.
Table 1. Summary of studies included in review.
Author(s) Year Study Design STEM Subject Intervention
Category Level
AYASRAH, Firas
Tayseer Mohammad
et al. [11]
2024 Quasi-
experimental Physics Direct instruction
with simulations Secondary
Hüseyin Ate¸s, Mustafa
Köro˘glu [12]2024 Quasi-
experimental Science
Hybrid/blended
learning with
simulations
Secondary
Demelash, M.,
Andargie, D., and
Belachew, W. [13]
2024 Quasi-
experimental Chemistry
Project-based
learning with
simulations
Upper secondary
Yu-Chen Chiang,
Shao-Chieh Liu [14]2023 Quasi-
experimental Engineering
Inquiry-based
learning with
simulations
Secondary
ALARABI,
Khaleel et al. [15]2022 Quasi-
experimental Physics
Hybrid/blended
learning with
simulations
Secondary
Encyclopedia 2025,5, 10 5 of 18
Table 1. Cont.
Author(s) Year Study Design STEM Subject Intervention
Category Level
Kari Kleine, Elena
Pessot [16]2024 Case study Engineering Simulation-based
assessment
Upper secondary
Badarudin, R., and
Husna, A. F. [17]2024 Quasi-
experimental Engineering Simulation-based
assessment
Upper secondary
Victoria Olubola
Adeyele [18]2024 Quasi-
experimental Science
Hybrid/blended
learning with
simulations
Primary
Cottone, Amanda
M. et al. [19]2021 Case study Science
Inquiry-based
learning with
simulations
Primary
Turki Alqarni [20] 2021 Quasi-
experimental Science
Hybrid/blended
learning with
simulations
Secondary
Yang Wang [21] 2022 Quasi-
experimental Physics Direct instruction
with simulations Secondary
YAN, Shenzhong
et al. [22]2023 Quasi-
experimental Chemistry Simulation-based
assessment
Upper secondary
WENG, Cathy
et al. [23]2023 Quasi-
experimental Engineering
Hybrid/blended
learning with
simulations
Upper secondary
Zaher, A.A., Hussain,
G.A., and
Altabbakh [24]
2023 Case study Engineering
Inquiry-based
learning with
simulations
Upper secondary
Sui, C.J., Chen, H.C.,
Cheng, P.H., and
Chang, C.Y. [25]
2023 Quasi-
experimental Science
Inquiry-based
learning with
simulations
Secondary
DAM-O, Punsiri
et al. [26]2024 Quasi-
experimental Physics
Inquiry-based
learning with
simulations
Secondary
Michal Dvir, Dani
Ben-Zvi [27]2022 Case study Science
Inquiry-based
learning with
simulations
Secondary
Li, M.,
Donnelly-Hermosillo,
D.F., and Click, J. [28]
2022 Quasi-
experimental Chemistry
Project-based
learning with
simulations
Secondary
Yuli Deng, Zhen Zeng,
Kritshekhar Jha, Dijiang
Huang [29]
2022 Case study Engineering
Problem-based
learning (PBL)
with simulations
Upper secondary
Khadija El Kharki,
Khalid Berrada, Daniel
Burgos [30]
2021 Case study Physics
Hybrid/blended
learning with
simulations
Upper secondary
Jaakkola, T., Nurmi, S.,
and Veermans, K. [31]
Quasi-
experimental Physics
Inquiry-based
learning with
simulations
Primary
Hua-Huei Chiou [32] 2021 Quasi-
experimental Science Direct instruction
with simulations Secondary
Encyclopedia 2025,5, 10 6 of 18
Table 1. Cont.
Author(s) Year Study Design STEM Subject Intervention
Category Level
Nicholas O. Awuor,
Cathy Weng, Isaac M.
Matere, Jeng-Hu Chen,
Dani Puspitasari,
Khanh Nguyen Phuong
Tran [33]
2024 Quasi-
experimental Engineering Direct instruction
with simulations Secondary
Moch Rifai, Siti
Masitoh, Bachtiar S.
Bachri, Wawan H.
Setyawan,
Nurdyansyah, Hesty
Puspitasari [34]
2020 Quasi-
experimental Engineering
Inquiry-based
learning with
simulations
Upper secondary
Muhammad
Rashid [35]2020 Case study Engineering
Problem-based
learning (PBL)
with simulations
Upper secondary
Cathy Weng, Khanh
Nguyen Phuong Tran,
Chi-Chuan Yang,
Hsuan-I. Huang, Hsuan
Chen [36]
2024 Quasi-
experimental Engineering
Hybrid/blended
learning with
simulations
Secondary
Wang Yang et al. [37] 2023 Quasi-
experimental Science
Hybrid/blended
learning with
simulations
Secondary
Amélie Chevalier,
Kevin Dekemele, Jasper
Juchem, Mia
Loccufier [38]
2021 Case study Engineering Direct instruction
with simulations
Upper secondary
Debarati Basu, Vinod K.
Lohani [39]2023 Quasi-
experimental Engineering Simulation-based
assessment
Upper secondary
Paul N. McDaniel [40] 2022 Case study Science
Inquiry-based
learning with
Simulations
Upper secondary
Sertaç Arabacıo˘glu,
Hasan Zühtü
Okulu [41]
2021 Case study Science
Inquiry-based
learning with
simulations
Upper secondary
3.1. Predominant Research Designs
The studies reviewed in this analysis showcased a range of methodological approaches,
primarily split between quasi-experimental and case study designs. Of the 31 studies in-
cluded, 21 utilized a quasi-experimental design, which allowed for structured comparisons
to assess the impact of digital simulations. Among these, nine studies incorporated a
control group, enabling direct comparisons between traditional teaching methods and
digital simulations. The remaining 12 quasi-experimental studies did not include a control
group, and instead relied on alternative methods, such as pre- and post-test assessments,
to evaluate learning gains.
In addition to the quasi-experimental studies, 10 studies adopted a case study ap-
proach. These case studies provided in-depth qualitative insights, often focusing on specific
Encyclopedia 2025,5, 10 7 of 18
educational settings or unique student populations, highlighting the practical aspects of
implementing digital simulations in STEM education (Figure 2).
Encyclopedia 2025, 5, x FOR PEER REVIEW 7 of 18
Figure 2. Research designs.
The use of pre- and post-test designs was common across many studies, with 14 stud-
ies employing this approach to capture changes in learning outcomes before and after the
intervention. In contrast, seven studies opted for alternative assessment methods, such as
interviews, observations, or single-time assessments, to gather insights into student learn-
ing and engagement with digital simulations (See Table 1 and Figure 3).
Figure 3. With or without control group.
3.2. Level of Education
The 31 studies included in this review spanned various educational levels, with a
focus primarily on secondary and upper secondary education. Specifically, 3 studies were
conducted at the primary level, 14 studies at the secondary level, and 14 studies at the
upper secondary level. This distribution reflects a stronger research focus on older stu-
dents, particularly in secondary and upper secondary seings, where digital simulations
are often applied to complex STEM subjects (See Table 1).
Figure 2. Research designs.
The use of pre- and post-test designs was common across many studies, with 14 studies
employing this approach to capture changes in learning outcomes before and after the
intervention. In contrast, seven studies opted for alternative assessment methods, such
as interviews, observations, or single-time assessments, to gather insights into student
learning and engagement with digital simulations (See Table 1and Figure 3).
Encyclopedia 2025, 5, x FOR PEER REVIEW 7 of 18
Figure 2. Research designs.
The use of pre- and post-test designs was common across many studies, with 14 stud-
ies employing this approach to capture changes in learning outcomes before and after the
intervention. In contrast, seven studies opted for alternative assessment methods, such as
interviews, observations, or single-time assessments, to gather insights into student learn-
ing and engagement with digital simulations (See Table 1 and Figure 3).
Figure 3. With or without control group.
3.2. Level of Education
The 31 studies included in this review spanned various educational levels, with a
focus primarily on secondary and upper secondary education. Specifically, 3 studies were
conducted at the primary level, 14 studies at the secondary level, and 14 studies at the
upper secondary level. This distribution reflects a stronger research focus on older stu-
dents, particularly in secondary and upper secondary seings, where digital simulations
are often applied to complex STEM subjects (See Table 1).
Figure 3. With or without control group.
3.2. Level of Education
The 31 studies included in this review spanned various educational levels, with a
focus primarily on secondary and upper secondary education. Specifically, 3 studies were
conducted at the primary level, 14 studies at the secondary level, and 14 studies at the
upper secondary level. This distribution reflects a stronger research focus on older students,
particularly in secondary and upper secondary settings, where digital simulations are often
applied to complex STEM subjects (See Table 1).
Encyclopedia 2025,5, 10 8 of 18
3.3. STEM Field
The studies reviewed covered a range of STEM subjects, with the majority focused on
general science and engineering. Specifically, 3 studies were conducted in chemistry, 12 in
engineering, 6 in physics, and 10 in general science. This distribution indicates a particular
emphasis on broader science education and engineering applications, in which contexts
digital simulations have been frequently explored for enhancing learning outcomes (See
Table 1).
3.4. Type of Digital Simulations
The types of digital simulations used in the studies varied.
3.4.1. Game-Based
Game-based simulations incorporate gaming elements to engage students in learn-
ing while pursuing educational goals. In STEM education, these simulations often use
challenges, rewards, and interactive tasks to make abstract concepts more tangible and
enjoyable. This type of simulation can be particularly effective for enhancing student
motivation and engagement through immersive, goal-oriented experiences [24].
In a study by Zaher et al. [
24
], the authors presented a STEAMeD-based active learning
approach, aimed at improving learning outcomes and engagement among undergraduate
engineering students across various programs at the German International University in
Cairo and the American University of Kuwait. Through a case study design, the study
incorporated simulations and project-based learning (PBL) activities emphasizing art, en-
trepreneurship, and design to prepare students for industry demands and the ABET accredi-
tation standards. The findings suggested that STEAMeD components like entrepreneurship
and design enhanced students’ analytical and business skills. Additionally, integrating
arts into engineering curricula fostered creativity and critical thinking, better preparing
students for complex, real-world problem-solving.
Another study by Adeyele [
18
] assessed the effectiveness of simulation games, blended
learning, and interactive multimedia for teaching basic science to pupils of varying abilities
in south-west Nigeria. Conducted in eight schools (four mainstream and four special
education schools), the study utilized a pre-test–post-test quasi-experimental design across
six experimental groups (three mainstream and three special education) and two control
groups. The experimental groups engaged with interactive simulation-based teaching
methods, while the control groups used traditional teaching methods. The results indi-
cated that interactive multimedia were the most effective in mainstream schools, while
blended learning proved more beneficial in special schools. Simulation games, though
effective, were less impactful than the other methods. The study concluded that integrating
technology-enhanced strategies tailored to different abilities significantly improved science
learning outcomes.
In another study [
21
], researchers evaluated the effects of Augmented Reality Game-
Based Learning (ARGSL) on engagement, learning performance, and satisfaction in physics
among seventh-grade students at a middle school in eastern China. Conducted over three
weeks, the quasi-experimental design included two experimental groups (ARGSL and
game-based) and a control group (book-based learning), with a total of 155 students. The
findings indicated that ARGSL significantly enhanced students’ behavioral, cognitive, and
emotional engagement compared to other methods. Semi-structured interviews revealed
that students found ARGSL engaging and effective for helping them to understand complex
concepts like the magnetic field.
Encyclopedia 2025,5, 10 9 of 18
3.4.2. Virtual Labs
Virtual labs simulate a laboratory setting where students can perform experiments
digitally. They provide a safe and resource-efficient alternative to physical labs, so are
especially useful in cases where resources or safety may be a concern. In STEM fields
like chemistry and biology, virtual labs allow students to conduct experiments, observe
reactions, and analyze results in a controlled, virtual space [14].
A study by Chiang and Liu [
14
] carried out in a Taiwanese university explored the
impact of extended reality (XR) on student engagement and learning performance within a
STEM curriculum. Conducted on 102 first-year engineering students at a public university
in Taiwan, the study used a quasi-experimental design with control and experimental
groups. The experimental group engaged with an extended reality STEM curriculum,
while the control group followed conventional computer-based learning materials. Using
a learning response questionnaire and a performance assessment, the study measured
various response dimensions, including organization, concentration, and teaching aid
effectiveness. Analysis revealed that the XR-integrated STEM group demonstrated higher
engagement across all response dimensions and significantly outperformed the control
group in learning performance. The study suggests that XR’s immersive environment
supports active engagement and enhances understanding of STEM concepts, making it a
valuable tool in promoting students’ enthusiasm and performance.
In a study by Alarabi et al. [
15
], the authors investigated the impact of computer
simulations (CSs) on understanding of Newton’s Second Law of Motion (NSLOM) among
grade 11 students in the UAE. Conducted in two high schools, the research used a quasi-
experimental design with pre- and post-tests to compare traditional face-to-face teaching
with CS-based instruction. The study included 90 students (45 boys and 45 girls), assigned
to either an experimental group using PhET simulations or a control group following
conventional methods. The results indicated that students exposed to simulations outper-
formed those in the control group, with higher post-test scores and improved comprehen-
sion of NSLOM. Both male and female students benefited from the intervention, showing
that simulations could effectively enhance physics understanding across genders. The
findings support the use of CSs to improve engagement and comprehension in physics
education, particularly for complex concepts.
3.4.3. Interactive Simulations
Interactive simulations are widely used in STEM education, allowing students to
manipulate variables and observe outcomes in real time. These simulations provide an
interactive environment where students can experiment with scientific concepts, such as
adjusting forces in physics or variables in mathematical models. Interactive simulations are
known for supporting conceptual understanding by enabling students to actively explore
and test hypotheses [20].
Researchers from Ethiopian universities [
13
] conducted a study to examine the effec-
tiveness of 7E context-based instructional strategy integrated with simulations in boosting
secondary students’ engagement in chemistry. Prompted by the low engagement levels
often associated with abstract teaching methods that lack real-world application, the study
involved 229 grade 10 students from various public schools in Ethiopia’s Oromia Region.
Using a quasi-experimental pre-/post-test design, the study compared four teaching meth-
ods: the 7E model with simulations, the 7E model without simulations, conventional
teaching with simulations, and standard teaching. A 15-item chemistry engagement scale
(CES) and semi-structured interviews were used to assess engagement across behavioral,
cognitive, and emotional dimensions. Quantitative analysis (ANCOVA, MANCOVA, and
DFA) demonstrated that the simulation-integrated 7E strategy significantly enhanced over-
Encyclopedia 2025,5, 10 10 of 18
all engagement and engagement in all three dimensions compared to the other methods.
Qualitative student feedback indicated increased understanding, relevance, and interest in
chemistry, with academic concepts linked to everyday contexts. These findings suggest
that integrating simulations with the 7E framework can substantially improve student
engagement in STEM education.
Lastly, a study assessed the impact of theodolite 3D augmented reality (AR) on learn-
ing outcomes and satisfaction among vocational high school civil engineering students
in Taiwan [
23
]. Conducted on 197 students from three schools, the study used a quasi-
experimental pre-test–post-test design with both control and experimental groups. The
experimental group used AR to supplement standard digital teaching materials on measure-
ment error, while the control group used only digital materials. ANCOVA analysis showed
that AR-enhanced instruction significantly improved learning outcomes and satisfaction
levels. Interviews indicated that students found AR helpful for grasping abstract concepts
in angle measurement.
Out of the 31 studies, 25 studies utilized interactive simulations [
11
–
13
,
16
,
17
,
19
–
23
,
26
–
36
,
38
–
41
], 3 studies employed game-based simulations [
18
,
21
,
24
] and 3 studies used virtual
labs [
14
,
15
,
25
]. This distribution highlights the prominence of interactive simulations in en-
hancing STEM education (Figure 4).
Encyclopedia 2025, 5, x FOR PEER REVIEW 10 of 18
other methods. Qualitative student feedback indicated increased understanding, rele-
vance, and interest in chemistry, with academic concepts linked to everyday contexts.
These findings suggest that integrating simulations with the 7E framework can substan-
tially improve student engagement in STEM education.
Lastly, a study assessed the impact of theodolite 3D augmented reality (AR) on learn-
ing outcomes and satisfaction among vocational high school civil engineering students in
Taiwan [23]. Conducted on 197 students from three schools, the study used a quasi-exper-
imental pre-test–post-test design with both control and experimental groups. The experi-
mental group used AR to supplement standard digital teaching materials on measure-
ment error, while the control group used only digital materials. ANCOVA analysis
showed that AR-enhanced instruction significantly improved learning outcomes and sat-
isfaction levels. Interviews indicated that students found AR helpful for grasping abstract
concepts in angle measurement.
Out of the 31 studies, 25 studies utilized interactive simulations [11–13,16,17,19–
23,26–36,38–41], 3 studies employed game-based simulations [18,21,24] and 3 studies used
virtual labs [14,15,25]. This distribution highlights the prominence of interactive simula-
tions in enhancing STEM education (Figure 4).
Figure 4. Types of digital simulations.
3.5. Intervention Categories
3.5.1. Direct Instruction with Simulations
Direct instruction with simulations integrates traditional teaching methods with dig-
ital simulations, providing structured guidance while allowing students to engage with
interactive tools. In this approach, simulations serve as supplementary tools that reinforce
lecture-based content, often simplifying complex concepts through visual and interactive
elements. Direct instruction offers the advantage of efficiently delivering information and
guiding students through new material, while simulations enhance comprehension by
enabling students to observe and interact with modeled scenarios in a controlled environ-
ment. This method is particularly beneficial when time constraints limit the depth of ex-
ploratory learning, allowing students to engage with digital tools while following a
teacher-led structure that emphasizes clear, organized content delivery [32].
3.5.2. Hybrid/Blended Learning with Simulations
Figure 4. Types of digital simulations.
3.5. Intervention Categories
3.5.1. Direct Instruction with Simulations
Direct instruction with simulations integrates traditional teaching methods with dig-
ital simulations, providing structured guidance while allowing students to engage with
interactive tools. In this approach, simulations serve as supplementary tools that reinforce
lecture-based content, often simplifying complex concepts through visual and interactive
elements. Direct instruction offers the advantage of efficiently delivering information and
guiding students through new material, while simulations enhance comprehension by
enabling students to observe and interact with modeled scenarios in a controlled envi-
ronment. This method is particularly beneficial when time constraints limit the depth
of exploratory learning, allowing students to engage with digital tools while following a
teacher-led structure that emphasizes clear, organized content delivery [32].
3.5.2. Hybrid/Blended Learning with Simulations
Hybrid or blended learning with simulations combines face-to-face instruction with
online simulation activities, creating a flexible learning environment that enhances students’
Encyclopedia 2025,5, 10 11 of 18
access to resources. This approach integrates traditional classroom teaching with digital
simulations, allowing students to benefit from both direct teacher guidance and self-paced,
interactive online experiences. In STEM education, blended learning with simulations
supports hands-on learning by enabling students to experiment with virtual models and
observe real-time results outside the physical lab. By merging in-person and online ele-
ments, this approach aims to foster engagement and improve learning outcomes through
dynamic, multimodal experiences that adapt to individual learning paces and needs [30].
3.5.3. Inquiry-Based Learning with Simulations
Inquiry-based learning with simulations is a student-centered approach that encour-
ages learners to explore and experiment within a guided environment. Rather than follow-
ing step-by-step instructions, students are prompted to investigate scientific or engineering
problems through simulations, allowing them to actively construct knowledge and develop
critical thinking skills. In this model, simulations serve as virtual environments where
students can manipulate variables, test hypotheses, and observe outcomes, fostering deeper
engagement and understanding. This approach aligns well with STEM education goals, as
it supports hands-on learning and enables students to tackle complex, real-world challenges
through inquiry and discovery [24].
3.5.4. Problem-Based Learning (PBL) with Simulations
Problem-based learning (PBL) with simulations is a hands-on, student-centered ap-
proach in which learners engage in solving complex, real-world problems using simulated
environments. In this model, students are presented with practical challenges rather than
traditional lectures, encouraging them to actively apply critical thinking and problem-
solving skills. Simulations in PBL settings allow students to experiment and test solutions
in a controlled, realistic environment that mimics real-world conditions. The instructor acts
as a facilitator, guiding students through problem identification and resolution without
directly providing solutions. This approach fosters independent learning, deeper engage-
ment, and the development of skills that are relevant to real-world applications, making it
particularly effective in fields requiring technical expertise and practical knowledge [29].
3.5.5. Simulation-Based Assessment
Simulation-based assessment utilizes digital simulations to evaluate students’ knowl-
edge, skills, and engagement in a controlled, virtual environment. By replicating real-world
scenarios, this approach allows students to demonstrate their understanding and apply
concepts practically, which is particularly valuable in STEM fields where hands-on expe-
rience is critical. In simulation-based assessments, student interactions are tracked, and
metrics such as task completion, engagement, and accuracy provide insights into both
learning outcomes and skill proficiency. This form of assessment is especially effective for
complex tasks, offering an authentic, data-rich method to measure learning progress and
competence without the need for physical resources or direct observation [39].
In total, the studies reviewed included a range of approaches to incorporating simula-
tions in STEM education. Direct instruction with simulations was utilized in five studies,
focusing on reinforcing teacher-led instruction with digital tools. Hybrid or blended learn-
ing with simulations was present in eight studies, integrating simulations with mixed
instructional formats to enhance accessibility and engagement. The most common ap-
proach was inquiry-based learning with simulations, used in ten studies, encouraging
students to explore and experiment independently. Problem-based learning (PBL) with
simulations appeared in four studies, leveraging simulations to support hands-on problem-
solving activities. Finally, four studies focused on simulation-based assessment, using
Encyclopedia 2025,5, 10 12 of 18
simulations to measure students’ understanding in realistic scenarios (See Table 1and
Figure 5).
Encyclopedia 2025, 5, x FOR PEER REVIEW 12 of 18
was inquiry-based learning with simulations, used in ten studies, encouraging students
to explore and experiment independently. Problem-based learning (PBL) with simula-
tions appeared in four studies, leveraging simulations to support hands-on problem-solv-
ing activities. Finally, four studies focused on simulation-based assessment, using simu-
lations to measure students’ understanding in realistic scenarios (See Table 1 and Figure
5).
Figure 5. Intervention categories.
3.6. Population
The studies included in this review primarily focused on general education seings,
with 31 studies targeting this population. In contrast, one study focused exclusively on
special education, while another included both general and special education popula-
tions. This distribution highlights the predominant emphasis on general education in re-
search on digital simulations, with limited exploration of its applications in specialized
educational contexts (See Table 1).
3.7. Outcome Measures
The studies reviewed explored a wide range of outcome measures, primarily focus-
ing on learning outcomes, student engagement, and practical skill acquisition in STEM
education. A significant number of studies assessed learning outcomes in various do-
mains, including conceptual understanding, knowledge retention, and practical applica-
tion skills. This emphasis reflects a common objective among researchers to gauge the
effectiveness of simulations in enhancing both foundational knowledge and applied skills
[12,14,23,25,26,28,30–36,38].
Student engagement was another prominent focus, often broken down into behav-
ioral, cognitive, and emotional dimensions. Many studies aimed to understand how sim-
ulations impacted students’ motivation, interest, and participation in STEM subjects, in-
dicating a strong interest in simulations as a tool for increasing student involvement
[13,16,19,21,28,38–40].
Figure 5. Intervention categories.
3.6. Population
The studies included in this review primarily focused on general education settings,
with 31 studies targeting this population. In contrast, one study focused exclusively on
special education, while another included both general and special education populations.
This distribution highlights the predominant emphasis on general education in research on
digital simulations, with limited exploration of its applications in specialized educational
contexts (See Table 1).
3.7. Outcome Measures
The studies reviewed explored a wide range of outcome measures, primarily focus-
ing on learning outcomes, student engagement, and practical skill acquisition in STEM
education. A significant number of studies assessed learning outcomes in various do-
mains, including conceptual understanding, knowledge retention, and practical appli-
cation skills. This emphasis reflects a common objective among researchers to gauge
the effectiveness of simulations in enhancing both foundational knowledge and applied
skills [12,14,23,25,26,28,30–36,38].
Student engagement was another prominent focus, often broken down into behavioral,
cognitive, and emotional dimensions. Many studies aimed to understand how simulations
impacted students’ motivation, interest, and participation in STEM subjects, indicating a strong
interest in simulations as a tool for increasing student involvement [13,16,19,21,28,38–40].
Additionally, several studies targeted specific skill sets, such as problem-solving,
analytical skills, and reflective thinking. For example, simulations were often used to
promote critical thinking and the understanding of complex systems, highlighting the role
of digital tools in developing higher-order cognitive skills [22,24,25,29,36].
A few studies focused on attitudinal changes toward science and inquiry, such as
students’ enjoyment of science lessons, career interest in STEM fields, and general attitudes
Encyclopedia 2025,5, 10 13 of 18
toward scientific inquiry. This suggests that simulations may also play a role in shaping
students’ perspectives on STEM disciplines [11,31].
Lastly, some studies extended beyond academic outcomes, measuring competencies
related to instructional practices and teacher effectiveness in designing activities involving
simulations. This broader scope reflects an awareness of the systemic impact of simulations,
not only on students, but also on educators’ ability to integrate technology effectively [
41
].
Table 2presents a summary of the outcome categories, highlighting the distribution of
focus across the reviewed studies.
Table 2. Summary of outcome measures.
Outcome
Category Description NS References
Learning
outcomes
Conceptual understanding,
knowledge retention, and practical
application skills
14 [12,14,23,25,26,28,30–36,38]
Student
engagement
Behavioral, cognitive, and emotional
dimensions, including motivation
and interest
8 [13,16,19,21,28,38–40]
Skill
development
Problem-solving, analytical skills,
and reflective thinking 5 [22,24,25,29,36]
Attitudinal changes
Enjoyment of science lessons, career
interest in STEM, and attitudes
toward inquiry
2 [11,31]
Teacher
competencies
Effectiveness in designing
simulation-based activities 1 [41]
3.8. Key Findings
The studies included in this review revealed several significant trends in the effective-
ness and impact of simulations across various educational settings. A common finding was
the positive influence of simulations on student attitudes and engagement. Simulations
consistently enhanced students’ interest and motivation in STEM subjects, often making
abstract concepts more accessible and engaging, especially in fields like physics and en-
gineering. Several studies noted improved student participation and enthusiasm, with
certain methods, like augmented reality and online collaboration tools, leading to high
levels of engagement.
In terms of learning outcomes, simulations often led to better comprehension and
knowledge retention compared to traditional methods. For example, students using
computer-based simulations to study physics concepts, such as Newton’s Second Law,
showed a deeper understanding of these topics [
15
]. The integration of simulations with
guided inquiry or interactive frameworks, like the KWL approach, further promoted critical
thinking, reflective learning, and conceptual understanding [22].
Some studies also highlighted simulations’ role in enhancing practical skills and
problem-solving abilities, with students benefiting from simulated real-world applica-
tions [
17
,
29
]. However, their findings suggested that for certain complex skills, such as
hands-on lab work, simulations should complement rather than replace physical labs [
23
].
This was especially evident in studies in which virtual labs were found to be effective sub-
stitutes, but faced limitations in replicating hands-on skills in disciplines like engineering
and data science [30].
Additionally, several studies focused on skill-based competencies beyond traditional
academic outcomes. For instance, simulations supported the development of analytical,
Encyclopedia 2025,5, 10 14 of 18
entrepreneurial [
24
], and spatial visualization skills, particularly when tailored to students’
unique strengths, such as high spatial ability [
32
]. The use of simulations in fields like
environmental monitoring and geography also helped students to build data literacy [
19
,
39
],
spatial awareness, and system comprehension [40].
Lastly, a few studies explored the impact of simulations on educator competencies,
noting that tools like virtual museums and augmented reality could enhance teachers’
ability to design engaging, inquiry-based learning activities [
41
]. This finding under-
scores the broader role of simulations, not only in student learning, but also in supporting
instructional innovation and teacher development [38].
4. Discussion
This review indicates a strong preference for quasi-experimental designs in the studies
analyzed, reflecting the challenges faced in conducting fully randomized experiments in
educational settings. Quasi-experimental studies often employ pre- and post-test measures
to assess changes in learning outcomes, providing a practical yet rigorous approach to
evaluating simulation effectiveness. This design has proven useful for capturing both
cognitive gains and shifts in student engagement, especially in settings where control
groups are challenging to implement. Other commonly used methodologies include
case studies, which offer in-depth insights into specific educational contexts and allow
researchers to observe the nuanced effects of simulations on individual or small groups
of students. While these studies lack the generalizability of experimental designs, they
contribute valuable qualitative data, especially in special education contexts. Some studies
also employ hybrid methodologies, combining quantitative and qualitative methods to
gain a comprehensive view of how simulations impact learning. However, the review
highlights the need for more standardized approaches and a greater diversity in research
designs, as current studies often lack the longitudinal perspective needed to understand
the lasting effects of simulations on STEM education outcomes.
The overwhelming preference for interactive simulations suggests their effectiveness
in catering to a broad range of educational levels and STEM disciplines. However, the
limited use of game-based simulations and virtual labs points to potential areas for further
exploration. Future research could investigate the comparative effectiveness of these sim-
ulation types and explore the barriers to adopting less commonly used formats, such as
virtual labs, in diverse educational contexts. This distribution of simulation types high-
lights the centrality of interactivity in engaging students and supporting STEM education,
while also signaling opportunities for broader integration of diverse simulation tools. The
preference for interactive simulations aligns with previous findings highlighting their role
in fostering student engagement and conceptual understanding [
42
]. However, the limited
use of game-based simulations and virtual labs, as identified in this review, contrasts with
studies that suggest a growing interest in augmented reality (AR) applications in science ed-
ucation [
9
]. Future research could explore barriers to adopting virtual labs and investigate
the potential of integrating AR-based simulations in diverse educational contexts.
The analysis reveals a growing preference for inquiry-based learning, aligning with
trends in STEM education that prioritize active and student-centered learning. However,
the review also highlights the limited exploration of simulations in combination with other
innovative approaches, such as flipped classrooms or gamified learning. Additionally,
the use of simulations in special education contexts remains underexplored across all
intervention categories, indicating a need for further research into their effectiveness in
supporting diverse learning needs. These findings underscore the adaptability of digital
simulations to various pedagogical approaches, providing educators with tools to enhance
STEM education at multiple levels and contexts. The diversity of intervention categories
Encyclopedia 2025,5, 10 15 of 18
highlights the importance of aligning simulation-based strategies with specific learning
objectives and student needs. The emphasis on inquiry-based learning approaches found in
this review supports trends in STEM education that prioritize student-centered learning [
43
].
Nonetheless, the limited exploration of gamification and flipped classroom strategies
contrasts with findings from AR-focused studies, which report an increased use of gamified
methods to enhance engagement [
9
]. This highlights the need to further investigate the
synergy between digital simulations and innovative teaching strategies.
This review underscores the need for long-term studies to evaluate the lasting impact
of simulations on STEM education outcomes, aligning with broader calls for longitudinal
research in this area [
43
]. Additionally, the exploration of combined VR and AR technologies
presents a promising avenue for advancing simulation-based learning environments [42].
5. Conclusions
This systematic review aimed to assess the role of simulations in STEM education,
focusing on their effectiveness in enhancing learning outcomes and engagement across
general and special education settings. Emphasizing empirical research, this review in-
cluded 31 studies that employed simulation-based interventions across various instruc-
tional methods, including direct instruction, hybrid/blended learning, inquiry-based learn-
ing, problem-based learning, and simulation-based assessment. These studies covered
primary, secondary, and upper secondary education levels, with the majority targeting
general education, and only minimal inclusion of special education contexts. Inquiry-based
learning was the most frequently used approach, particularly at the secondary and upper
secondary levels, while engineering and science were the most represented subjects. The
prevalence of quasi-experimental designs, especially those utilizing pre- and post-tests,
reflects a methodological focus on assessing the impact of simulations within controlled
educational settings.
The findings indicate that simulations are adaptable educational tools, providing
benefits across various instructional strategies and educational stages. For instance, the
prominence of inquiry-based learning with simulations suggests that hands-on, exploratory
approaches may be especially beneficial in engaging students and fostering deeper under-
standing in subjects like physics and science at the secondary and upper secondary levels.
Similarly, the focus on hybrid/blended learning with simulations implies an emerging
trend of integrating digital resources within flexible learning environments, especially for
STEM education. Despite their versatility, simulations were under-represented in primary
education, which might point to a missed opportunity for early engagement in STEM
through interactive digital tools. The sparse representation of special education within
these studies also highlights a significant gap. Given the potential of simulations to provide
customized, adaptive learning experiences, expanding research to include more diverse
learner populations, particularly students with special needs, is crucial.
This systematic review highlights the significant role of digital simulations in im-
proving educational outcomes and student engagement in STEM education. Practical
implications include guiding educators in selecting and implementing effective digital
tools to enhance interactive learning experiences. Theoretically, this study contributes
to an understanding of how diverse simulation types support learning objectives across
different STEM disciplines. Policy-makers are encouraged to support the development and
integration of such tools in education systems, ensuring equitable access for all learners.
Finally, future research should examine the intersection of digital simulations with ad-
vanced technologies such as AI and VR to explore their untapped potential in transforming
STEM education.
Encyclopedia 2025,5, 10 16 of 18
While this review provides insights into the application of simulations in STEM edu-
cation, certain limitations should be acknowledged. The focus on studies published in the
last five years and limited to peer-reviewed journals may have excluded relevant research
available in other formats, such as technical reports or conference proceedings. Further-
more, while quasi-experimental designs dominated, only a subset of these included control
groups or long-term follow-ups, potentially limiting the robustness of causal claims about
the efficacy of simulations. Additionally, the studies reviewed primarily addressed sec-
ondary and upper secondary education, with fewer studies at the primary level. The focus
on general education over special education means that conclusions regarding simulations’
utility for students with special needs are tentative. Expanding research to include a broader
range of educational levels, more diverse learner populations, and varied instructional
designs will improve the generalizability and applicability of these findings.
Author Contributions: Conceptualization, C.K., C.S. and A.D.; methodology, C.K., C.S. and A.D.;
validation, C.K., C.S. and A.D.; formal analysis, C.K., C.S. and A.D.; investigation, C.K., C.S. and A.D.;
resources, C.K., C.S. and A.D.; writing—original draft preparation, C.K., C.S. and A.D.; writing—
review and editing, C.K., C.S. and A.D. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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