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.