Volume 13 - Issue 73
/ January 2024
9
http:// www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2024.73.01.1
How to Cite:
Polián, P., Kopotun, I., & Polián, P. (2024). Development of virtual learning systems based on artificial intelligence: International
experience. Amazonia Investiga, 13(73), 9-13. https://doi.org/10.34069/AI/2024.73.01.1
Development of virtual learning systems based on artificial
intelligence: International experience
Vývoj virtuálních výukových systémů založených na umělé inteligenci: Mezinárodní zkušenosti
Received: December 20, 2023 Accepted: January 28, 2024
Written by:
Pavel Polián1
https://orcid.org/0000-0002-3258-0340
Igor Kopotun2
https://orcid.org/0000-0002-2947-8599
Petr Polián3
https://orcid.org/0000-0002-4009-0819
Abstract
The aim of the article is to study and improve the
use of artificial intelligence in education by
analysing international experience. The main
methods used were general scientific methods,
documentary analysis, standard statistics, and
factor analysis. The study's main results
demonstrate the rapid growth in the popularity of
artificial intelligence in virtual learning systems
in all the countries under consideration. The
article reveals a tendency to increase the demand
for these technologies. The study concludes that
AI has an important role in the educational
process and that future research should focus on
evaluating its effectiveness in training specific
specialists.
Keywords: education, innovative learning,
students, development, university.
Introduction
Artificial intelligence (AI) has changed how we
perceive technology and its capabilities in
various areas of life. According to a UNESCO
policy brief, using AI in education will “reduce
the time spent preparing for classes, develop
creative and innovative methods to improve the
level of knowledge acquisition and select
individual educational trajectories for students”
(Duggan, 2020).
1
Magister Degree, Rector, Academy HUSPOL s.r.o., Kunovice, Czech Republic. WoS Researcher ID: FVL-4715-2022
2
Doctor of Legal Sciences, Vice-Rector, Academy HUSPOL s.r.o., Kunovice, Czech Republic. WoS Researcher ID:
DVG-2297-2022
3
Magister Degree, Rector, Academy HUSPOL s.r.o., Kunovice, Czech Republic. WoS Researcher ID: HLO-8602-2023
The study aims to understand and improve AI-
based learning systems by analysing
international experience.
Based on the goal, the following tasks can be
identified:
1. Study the dynamics of demand for virtual
learning systems based on AI.
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2. Study of the development of virtual learning
systems based on AI in 2022.
The object of research is innovative virtual
learning systems based on AI, considering
international experience in this field.
The article includes an introduction and a
literature review covering the latest research. The
Methodology section will provide details of the
procedure and methods, while the Results and
Discussion will provide an understanding of
what the study’s findings are based on.
The main focus of the research is to study best
practices, trends and innovations in the use of AI
in educational systems. By analysing experiences
globally to design virtual learning systems that
address modern educational needs and leverage
best practices in AI.
Literature review
The study by Salas-Pilco & Yang (2022)
systematically examines the use of artificial
intelligence in Latin American educational
institutions using a meta-analysis of various
implementation cases. They found significant
interest in using AI to support educational
processes but emphasized the existing
infrastructure and access to resources that hinder
widespread adoption. This study also points to a
lack of empirical data on the impact of AI on
educational outcomes, emphasizing the need for
further research in this area.
On the other hand, Rios-Campos et al. (2023)
explore the challenges and prospects of using AI
in South Florida educational institutions, with a
focus on the potential for personalizing learning
and improving pedagogical methods. They
identify key barriers, such as high cost, ethical
issues, and data privacy concerns, that require
strategies to be developed for effective AI
adoption.
Chen, Chen & Lin (2020) emphasize the rapid
development of AI and its potential to improve
virtual learning systems. They point out that new
machine learning and natural language
processing algorithms allow for the creation of
intelligent, personalized, and effective
educational systems.
The experience of the HUSPOL Academy's
teachers demonstrates the ability of AI to solve
numerous problems in education, ensuring the
creation of personalized curricula that take into
account the needs and abilities of each student.
According to HolonIQ (2022), this approach
makes learning more effective and aligns with
students' personal goals.
Alam (2022) emphasizes the use of intelligent
analytical tools to assess student performance,
identify their strengths and weaknesses, and
provide recommendations for improving the
learning process. This helps automate
administrative tasks such as course registration
and grading, freeing up staff time to work more
effectively with students.
The identified trends include a growing interest
in integrating AI into educational processes to
personalize learning, optimize administrative
tasks, and improve teaching efficiency.
However, current gaps, such as limited research
on the impact of AI on educational outcomes,
ethical and privacy concerns, and infrastructure
and access issues, require additional attention.
The need for this research stems from the need to
understand how AI can be effectively integrated
into curricula while addressing these challenges.
This includes developing strategies to overcome
existing barriers and exploit the potential of AI to
improve educational practices.
Methodology
The study was conducted in several stages. The
stages are shown in Figure 1. The study was
based on the following sources: Research and
Markets (2022), HolonIQ (2022), European
Commission (2022), and the OECD (2023).
These sources made it possible to analyse the
problem under consideration in the dynamics of
its development and draw conclusions. The study
uses general scientific research methods:
analysis, synthesis, and documentary analysis.
Standard statistics and factor analysis were used.
The Alpha-Cronbach reliability coefficient was
used to examine the internal consistency of the
data obtained. Tools such as Microsoft Excel and
Google Sheets were used for statistical
calculations. All the results and conclusions
obtained meet the requirements of academic
integrity, validity, and reliability. The study’s
authors did not receive funding from
stakeholders or declare any conflict of interest.
Polián, P., Kopotun, I., Polián, P. / Volume 13 - Issue 73: 9-13 / January, 2024
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Figure 1. Stages of the study.
Results
At the beginning of the study, we should pay
attention to the dynamics of demand for AI-based
VLSs in the Czech Republic, the UK, the US, and
Japan by the number of users. The results of the
study are presented in Table 1.
Table 1.
Dynamics of demand for virtual learning systems based on artificial intelligence (2017-2022) in users
Country
2017
2018
2019
2020
2021
2022
Forecast for 2023
Czech Republic
10 К
20 К
30 К
50 К
75 К
100 К
150 К
United Kingdom
100 К
200 К
300 К
400 К
600 К
500 К
750 К
USA
1 mln
2 mln
3 mln
5 mln
7 mln
10 mln
15 mln
Japan
100 К
200 К
300 К
400 К
600 К
1 mln
1.5 mln
Source: Developed based on sources: Research and Markets (2022), HolonIQ (2022), European
Commission (2022) and OECD (2023)
Table 1 shows that the demand for VLSs grew
significantly between 2017 and 2022 in all the
countries considered. The trend is clear: demand
is growing. In all countries, there has been a
steady and significant increase in users during the
period under review.
Next, we should pay attention to the development
of AI-based VLSs by considering such indicators
as the number of users, investments, number of
startups, and the most common areas of use. The
research results are presented in Table 2.
Table 2.
Development of virtual learning systems based on artificial intelligence in 2022
Country
Number of users
Investments
Number of startups
Czech Republic
100 000
€10 mln
20
United Kingdom
500 000
£50 mln
50
USA
Ten mln
$1 bln
100
Japan
1 mln
¥10 bln
30
Source: Developed based on sources: Research and Markets (2022), HolonIQ (2022), European
Commission (2022) and OECD (2023)
The analytical Table 2 shows that in 2022, we can
observe a general trend towards a significant
development of VLSs in all the countries under
consideration. Each has a substantial number of
users and significant investments in this area.
There is an active development of startups
working in virtual learning. Table 3 shows the
results of the factor analysis.
Setting the goal and objectives of the study. Studying
the peculiarities of development of AI-based VLSs
The sample consisted of statistical data for 2017-2022.
The sample included data from four countries: the
Czech Republic, the United Kingdom, the United States,
and Japan, which made it possible to follow a certain
trend.
Control over the work,
analysis and processing of
the results, factor analysis
Statistical data processing
was carried out using
SPSS
Systematisation and
generalisation of the
results and
formulation of the
research conclusions
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Table 3.
Factor analysis of the development of virtual learning systems based on artificial intelligence (2017-2022)
Factor
Impact on development
Quantification
Dynamics
Country
Investments
Increased investment stimulates the
development of new systems and
technologies
€1 million -> €10
million (Czech
Republic)
Growth
All countries
Number of
startups
The growth in the number of
startups indicates
5 -> 20 (Czech
Republic)
Research and
development
New research and development
improves existing systems
Growth of publications
Internet
accessibility
Increasing accessibility of the
Internet raises potential audience
70% -> 85% (Czech
Republic)
Source: Developed based on sources: Research and Markets (2022), HolonIQ (2022), European
Commission (2022) and OECD (2023)
Table 3 provides an overview of the factors
influencing the development of AI-based virtual
learning systems from 2017 to 2022. It highlights
the factors driving this process, including
investment, the number of startups, research and
development, internet availability, demand for
online learning, government support, and
competition. It also points to challenges that
could slow the process, such as legal and ethical
issues.
Learning systems based on artificial intelligence
show prospects for increasing learning
effectiveness, personalising the learning process
and engaging students. However, problematic
technical aspects, data confidentiality, and access
to technologies were identified.
Discussion
The study showed that the Czech Republic, as
one of the countries under study, shows a
significant increase in investment, the number of
startups, internet accessibility, and demand for
online learning. As the survey by Kabudi, Pappas
and Olsen (2021) highlights, AI is now essential
in education. The authors emphasise that it opens
up new opportunities for personalising learning,
helping everyone develop at their own pace and
according to their needs and abilities. As Mastan,
Sensuse, Suryono & Kautsarina (2022) highlight,
AI creates innovative teaching methods that
ensure more effective learning. It also helps to
focus on individualised guidance and support. AI
also contributes to developing new assessment
methods and provides access to quality education
anywhere. But, as noted by Cheung, Kwok,
Phusavat & Yang (2021), AI can become a factor
of abuse and academic dishonesty if not used
correctly.
The practical significance of the article lies in
studying and analysing the factors that influence
the development of AI-based VLSs. The
theoretical relevance lies in expanding scientific
knowledge about the use of AI in education.
Limitations of the study include the limited
number of countries studied or the limited data
and methodologies used.
Conclusions
The study analysed the development of AI-based
VLSs and their international application
experience. The results showed a stable and
significant increase in the popularity and demand
for such systems in all the countries under
consideration. Investment, the number of
startups, research and development, internet
accessibility, demand for online learning,
government support and competition have been
vital factors influencing this development.
Recommendations
Recommendations for the future use of AI in
education include the development of ethical
standards, ensuring accessibility and continuous
improvement of systems. Future research should
focus on developing a general methodology for
training teaching staff to use AI in education.
Bibliographic references
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https://doi.org/10.1007/978-981-19-2980-
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Chen, L., Chen, P., & Lin, Z. (2020). Artificial
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/ January 2024
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