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DOI: https://doi.org/10.34069/AI/2024.73.01.16
How to Cite:
Zavalevskyi, Y., Kyrilenko, S., Kijan, O., Bessarab, N., & Mosyakova, I. (2024). The role of AI in individualizing learning and
creating personalized programs. Amazonia Investiga, 13(73), 200-208. https://doi.org/10.34069/AI/2024.73.01.16
The role of AI in individualizing learning and creating personalized
programs
El papel de la IA en la individualización del aprendizaje y la creación de programas personalizados
Received: October 16, 2023 Accepted: January 3, 2024
Written by:
Yury Zavalevskyi1
https://orcid.org/0000-0003-1904-6642
Svitlana Kyrilenko2
https://orcid.org/0000-0002-2701-1303
Olga Kijan3
https://orcid.org/0000-0002-0482-8898
Nataliya Bessarab4
https://orcid.org/0000-0001-7930-2404
Irina Mosyakova5
https://orcid.org/0000-0002-8932-3759
Abstract
This article analyses the technical and
methodological aspects of implementing
individualised curricula using artificial intelligence
in the educational process. The study deals in detail
with the issues of technological infrastructure, data
collection, and processing, as well as the integration
of individualised programmes with existing
educational platforms. The methodological aspect
of the article includes an analysis of methods for
determining the needs and capabilities of each
student and the development of a methodology for
assessing the success of individualised
programmes. The study aims to uncover the
potential and benefits associated with the utilization
of personalized programs in contemporary
education. This is done with the intention of
enhancing the overall learning experience and
attaining superior outcomes for every individual
student and pupil. Future areas of research include
further development of technical solutions for
individualised programmes, studying
1
Doctor of Pedagogical Sciences, Professor, First deputy of DNU «Institute of Modernization of the Content of Education», Kyiv,
Ukraine. WoS Researcher ID: JQJ-2685-2023
2
PhD in Pedagogy, Head of the Department of Innovation, Research and Experimental Work, State Scientific Institution «Institute of
Education Content Modernization», Kyiv, Ukraine. WoS Researcher ID: JQJ-3568-2023
3
PhD in Pedagogy, Head of the Sector of Experimental Pedagogy, Department of Innovation Activity and Experimental Work, State
Scientific Institution «Institute of Education Content Modernization», Kyiv, Ukraine. WoS Researcher ID: JQJ-3087-2023
4
PhD in Pedagogy, Researcher of the pedagogical innovations and author’s sector of the Department of Innovation, Research and
Experimental Work State Scientific Institution «Institute of Education Content Modernization», Kyiv, Ukraine. WoS Researcher
ID: JQJ-3620-2023
5
PhD in Pedagogy, Director of a communal organization Children and youth creativity center «Shevchenkovets», Kyiv, Ukraine.
WoS Researcher ID: JQJ-2805-2023
Zavalevskyi, Y., Kyrilenko, S., Kijan, O., Bessarab, N., Mosyakova, I. / Volume 13 - Issue 73: 200-208 / January, 2024
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methodological approaches to adapting
programmes to the needs of different categories of
student, and developing ethical standards for
protecting personal data in education. This article
will be useful for teachers, higher education
institutions, researchers, and anyone interested in
using artificial intelligence to individualise learning
and improve education. It offers important
discoveries and practical recommendations for
implementing individualised programmes in the
educational process.
Keywords: individualisation of learning,
personalised educational programmes, artificial
intelligence, machine learning, data analysis.
Introduction
The significance of the selected subject arises
from the necessity to implement creative
methods in the field of education. Due to the
constant changes in technology and social
requirements, an individual approach to learning
is becoming a key element in improving
education. Today, most educational systems face
the challenge of providing an effective learning
process for each student, taking into account their
unique needs, learning styles, and pace of
learning.
In this context, the use of artificial intelligence
(AI) provides an opportunity to transform and
individualise this process, taking into account the
specific needs and ability level of each student.
Machine learning and data analytics technologies
allow for the creation of personalised
programmes that are adapted to the specific
educational goals and needs of students
(Chanysheva et al., 2023).
The term “artificial intelligence” or “AI” is
commonly used, but it can be very difficult to
define and explain to the average person. In the
perception presented mainly by cinema and
literature, AI takes the form of a fantasy rather
than a real understanding of the technological
aspects behind the concept. The reality, however,
differs significantly from the images that can be
found in contemporary cultural discourse
(Etzrodt et al., 2022).
For more than eighty years, humanity has come
a long way, experiencing mistakes and dead ends
in the development of AI, each of which ended in
the “winter of AI,” accompanied by
disappointment in the potential of this
technology. But since the early 2010s, the world
has been experiencing a “warming” in the field
of AI again. That is why large corporations and
governments of leading countries are investing
billions of dollars in the development of AI, as it
is currently transforming every aspect of our
lives, from scientific research to everyday things
(Alawi, 2023).
The use of AI in the educational process provides
an opportunity to individualise learning, which
makes education more effective. This means that
programmes can adapt to each student's level and
learning style by providing them with
individualised tasks and materials. It helps to
create personalised learning programmes that
take into account strengths, weaknesses,
interests, and needs. This contributes to
improved learning outcomes and learner
motivation, as they are more interested in
materials that meet their needs (Flindt et al.,
2021).
This research has pinpointed various challenges
confronting the field of educational science.
Primarily, the matter of safeguarding personal
data and ensuring privacy becomes paramount,
given that the incorporation of artificial
intelligence in education involves the gathering
and analysis of personal information. It is
imperative to delve deeply into ethical concerns
associated with the acquisition and utilization of
data in educational practices.
It is important to solve the technical and
methodological challenges associated with the
introduction of AI in education. Developing
programmes and infrastructure for individualised
learning requires financial and technical
resources. In addition, effective methods for
evaluating the success and outcomes of such
systems need to be developed (Chen et al., 2020).
The research focuses on the role of AI in
individualising learning and developing
personalised educational programmes. The main
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aspect of the research is to analyse how AI
technologies, in particular machine learning and
data analytics, can be used to create
individualised learning paths (Kumar Basak,
Wotto, & Bélanger, 2018).
It addresses the question of how AI can take into
account the unique needs and abilities of each
individual by providing personalised tasks,
materials, and teaching methods. Significant
attention is given to the influence of personalized
learning on both academic achievements and
motivation, highlighting the potential to enhance
the overall quality of education through this
methodology (Adiguzel et al., 2023).
Particular attention should be paid to ethical and
privacy issues related to the collection and
processing of students' personal data in the
context of personalised educational programmes.
In addition, the study focuses on the technical
aspects of introducing AI into the educational
process and developing methods for assessing
the results and effectiveness of individualised
learning (Salnyk et al., 2023).
The aim of the study is to thoroughly analyse and
define the role of AI in individualising learning
and creating personalised educational
programmes. Specifically, the study aims at the
following objectives:
1) To consider current trends in education that
require individualisation of learning and
adaptation of curricula to the needs of
students and students.
2) Explore how artificial intelligence
technologies, such as ML and data analytics,
can be used to create individualised learning
approaches.
3) To consider the ethical issues related to the
use of artificial intelligence in education and
the collection of personal data of pupils and
students.
4) To study the technical and methodological
aspects of implementing individualised AI
applications.
Theoretical framework or literature review
Today, the study of artificial intelligence (AI) is
becoming an extremely relevant phenomenon in
the field of education. This is due to the
challenges that have emerged in society in the
context of the integration of information
technology into various aspects of our lives.
Therefore, basic knowledge of AI is becoming a
necessity for everyone. The importance of
incorporating AI elements into the educational
process is now supported at the state level.
In examining the research conducted by Ryan &
Deci (2020), it is crucial to highlight their
emphasis on applying the connectivity theory
within the framework of contemporary
educational settings. This theory, rooted in the
concept that knowledge should be easily
accessible through networks and online
resources, has the potential to create fresh
possibilities for students, enhancing both their
comprehension and practical application of
learning materials. The study by Xie et al. (2022)
focuses on the impact of social participation on
social inclusion. The researchers carefully
analyse the relationship between active
participation in social interactions and the level
of social integration and conclude that this
relationship is important. The article by Chen et
al. (2020) emphasises the importance of
educational big data for modern education. They
thoroughly explore methods for extracting
meaning from educational data and analysing it
further to develop intelligent educational
approaches. The work of Cheng & Tsai (2019) is
worth noting as significant in the context of using
immersive virtual tours in primary school. The
authors thoroughly analyse students' learning
experiences and teacher-student interaction when
using virtual fields for learning, emphasising
their effectiveness in the pedagogical process.
Cutumisu & Guo's (2019) study focuses on the
use of case study methods to extract students'
understanding of computational thinking from
their own reflections during programming. The
authors demonstrate how this method can be
useful for analysing students' understandings and
their learning. Daniel (2019) provides a critical
analysis of issues related to big data and data
science for educational research. The researcher
discusses current issues and approaches to the
use of big data in education. The study by Gierl
& Lai (2018) concerns the use of automatic task
generation to create solutions and rationales in a
computerised format for formative testing. They
highlight methods and approaches to creating
such tasks. The researchers also point out the
importance of current and future challenges and
opportunities in the field of artificial intelligence
in education, as well as the prospects for the
development of this area. This work provides an
important contribution to the understanding and
development of the use of artificial intelligence
in education. Goksel & Bozkurt (2019) in their
book focuses on the role of AI in the educational
process. They explore the impact of artificial
intelligence on contemporary education,
presenting a valuable framework for further
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exploration in this domain. The researchers’
examination of current patterns in the integration
of artificial intelligence in education highlights
its potential as a crucial element in shaping
innovative teaching and assessment
methodologies. The study conducted by Hew et
al. (2019) prompts a contemplation on the
presence of a coherent “theory” within the realm
of educational technology research. The authors
scrutinize the utilization of conceptual theories in
educational technology and assess the existence
of well-structured theoretical frameworks. In a
study by Huang et al. (2020), the focus shifts
towards the prediction of students’ academic
performance using extensive educational data
and the analysis of learning activities. The
authors assess various approaches for classifying
and scrutinizing learning logs with the aim of
forecasting academic achievements.
Insufficiently explored aspects within the realm
of educational technology and the utilization of
big data encompass several crucial facets
demanding increased attention and investigation.
Among these, there is a need for the formulation
of conceptual and theoretical frameworks to
enhance comprehension and elucidation of the
impact of big data on learning and education.
Existing theoretical approaches, thus far, exhibit
limitations and warrant further refinement.
Another area that remains under-researched
pertains to the ethical considerations surrounding
the use of big data in education. Specifically, it is
imperative to address issues related to the
confidentiality and privacy of student data while
establishing ethical parameters governing the
collection and utilization of educational data.
This is particularly relevant in the context of the
growing amount of information collected and
processed in educational systems.
Methodology
The research methods used contributed to
solving the tasks set by the authors, including
analysing the essence of the use of AI in
education, which defines the standards in the
field of education and science, identifying the
main areas of its application, and identifying the
problems of implementing AI in the educational
environment. The research was conducted using
the dialectical method, which was used to
analyse AI on both a general and practical basis.
This method helps to resolve the issue of the
concept of using AI to build individual learning
paths, as it contributes to the development of
scientific knowledge by moving from concrete to
abstract aspects of the problem, abstracting from
details. This phase holds considerable
importance in the generation of novel scientific
insights, achievable through the examination of
problem elements and the identification of
emerging patterns through abstraction from
specific details.
Data from open sources were used to study the
dynamics of academic performance. They were
obtained through publicly available information
resources, such as websites of educational
institutions, national education databases, or
other documents and reports that are regularly
published. In particular, the data on academic
performance included average student grades for
the 2021/2022 academic year, the dynamics of
changes in these grades, as well as other
indicators that reflect the qualitative and
quantitative aspects of academic performance.
The results were evaluated using standard
methods of mathematical statistics. The results of
the analysis were interpreted with reference to
the specific objectives of the study, which
allowed us to draw conclusions about the impact
of AI on individual learning trajectories and
identify possible difficulties in their
implementation.
Results and discussion
Today, there are trends in the educational process
that require individualisation of learning and
adaptation of curricula to the needs of students
and pupils. One of them is diversity in learning
styles and pace. Each consumer of educational
services is unique, and it is important to create
curricula that take into account their individual
needs and capabilities. This requires appropriate
strategies for adaptation and personalisation of
learning (Rakhimov, & Mukhamediev, 2022).
Another trend is the growing role of technology
in learning. The Internet, mobile applications,
and other innovative tools allow for individual
learning paths and approaches. Pupils and
students can access a variety of learning
resources with convenience and efficiency. It is
important to take into account the diversity of
cultures and languages of popular educational
programmes that are freely available online.
Globalisation and intercultural interaction create
a need to develop intercultural learning and
include multilingual pupils and students in
educational processes.
In addition, the role of lifelong learning is
growing. Rapidly changing technologies and
economic realities require continuous learning
and retraining. Curricula need to be flexible and
individualised to meet these challenges.
Contemporary developments in the field of
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education emphasize the necessity for
personalizing learning experiences and adjusting
curricula to enhance the efficiency and
advancement of the educational process. Table 1
shows the main trends in the development of
modern education.
Table 1.
The main trends in the development of modern education
Education trend
Contents of the main provisions
Individualisation of
learning
Contemporary education aims to develop curricula and methodologies that consider
the unique requirements of students and their learning speeds. This fosters enhanced
and more efficient learning experiences.
Use of technology
The role of technology in learning is growing. The Internet, mobile applications, and
online resources allow access to learning from anywhere and at any time.
Intercultural learning
Globalisation creates a need for intercultural learning and the inclusion of different
cultures and languages in the educational process. Such approaches promote
understanding and respect for diversity.
Lifelong learning
Rapid changes in technology and economic conditions create a need for continuous
learning and retraining. Educational programmes are becoming more flexible and
accessible throughout life.
Source: Created by the authors based on Ali (2022).
This table provides information on the key trends
in modern education and helps to understand how
the educational process is adapting to modern
challenges. Based on the above, it can be stated
that these trends have a significant impact on the
requirements for the implementation of AI.
Individualised learning implies the unique needs
of each student, and in this context, AI can be
used to adapt curricula and approaches to meet
these needs.
AI is now playing a key role in creating
individualised educational trajectories for each
pupil and student. It can analyse and process
large amounts of data, taking into account
individual needs and abilities (Razaulla et al.,
2022). By analysing data on learning progress,
learning style, pace of learning, and other
parameters, AI can recommend personalised
learning materials and tasks that meet specific
needs. With the help of specialised AI systems,
individualised curricula can be created, taking
into account the strengths and weaknesses of
students, their goals, and interests. Teachers and
educators are provided with tools to create
unique curricula that help develop individual
skills and learning achievements for each
student. AI can also provide continuous
monitoring of learning progress and adapt
programmes in real-time. This helps to avoid
pupils and students falling behind or being
overwhelmed, ensuring an optimal learning
trajectory for each (Luan et al., 2020).
Individualizing the education of pupils and
students involves developing distinctive learning
methods that consider the unique attributes of
each learner. Tasks related to individualization
encompass crafting personalized learning plans
that consider the strengths, weaknesses, interests,
and objectives of students. Additionally, this
process involves tracking the progress of
learning and adjusting the curriculum as needed
(Hendradi et al., 2020).
Individualized instruction empowers educators
and educational establishments to more
effectively address the requirements of pupils
and students, fostering their educational and
developmental achievements. It facilitates
enhanced and cooperative learning, encourages
personal growth, and unleashes the unique
potential within each person/
Advancements in technology, exemplified by the
utilization of online resources and mobile
applications, have facilitated the enhancement of
accessibility and interactivity in learning. AI is a
pivotal element in this progression. The
increasing global intercultural engagement
necessitates strategies that acknowledge cultural
diversity, and AI emerges as a valuable tool for
translating and customizing learning materials to
accommodate these diverse cultural contexts.
Due to the need for lifelong learning, AI can
become an important tool for providing
individualised educational opportunities at
different stages of personal development.
AI technologies, such as machine learning and
data analytics, can be used to create
individualised learning approaches due to their
ability to analyse large amounts of information
and highlight the unique personal characteristics
of each student. Machine learning allows special
education systems to learn from accumulated
data and develop models that predict which
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learning approaches work best for each student
(Bahri, & Lestari, 2021).
By analysing the data, you can identify the
advantages and disadvantages of its users, their
individual learning style, the speed of learning,
and other parameters necessary to create a
favourable learning environment. Based on this
data, you can create personalised learning plans
that take into account the specific needs and
goals of each student.
Learning recommender systems (LRSs) are
important tools in the education sector that use
machine learning technologies to provide
individualised recommendations for learning
materials and learning approaches. Their purpose
is to enhance the learning experience and
enhance the overall quality of education while
promoting a more effective utilization of
educational resources. The operation of Learning
Data Centers (LDCs) relies on analyzing
extensive data, including students' learning
records, responses to assignments, past
advancements, and various parameters. Machine
learning allows LDCs to create individualised
profiles for each student and anticipate their
learning needs (Adjerid & Kelley, 2018). One of
the key functions of an LDC is to recommend
learning materials that best meet a student's
specific needs and goals. For example, the
system can recommend specific courses,
textbooks, video lectures, or assignments that
will help a student improve their knowledge and
skills. In addition, LDCs can serve to monitor
learning progress and provide students and
teachers with information about achievements
and possible areas of improvement. They help to
create more effective curricula and provide the
opportunity to individually tailor learning to the
needs of each student and learner (Türkmen,
2023). Table 2 shows the recommendation
systems of the main popular MOOC accelerators.
Table 2.
Recommendation systems of the main popular MOOC accelerators
System
Functionality
Paid/free of charge
Coursera
Coursera uses colourimetry algorithms to recommend courses that are
similar to courses that the user has already viewed. For example, if a user
is watching a course on programming, they may be recommended other
courses on programming. Coursera also uses filter-based algorithms to
recommend courses that match the user's interests. For example, a user
who is interested in machine learning can be recommended courses
about machine learning.
Free, paid
edX
edX uses algorithms based on filters to recommend courses that match
the user's interests. For example, a user who is interested in business may
receive recommendations for courses about business. edX also uses
ratings-based algorithms to recommend courses that are highly rated by
other users.
Free, paid
Udemy
Udemy uses rating-based algorithms to recommend courses that are
highly rated by other users. For example, if a user rates a course highly,
they may be recommended other courses that are also highly rated by
other users. Udemy also uses algorithms based on user feedback to
recommend courses that users are likely to find useful.
Free, paid
LinkedIn
Learning
LinkedIn Learning uses algorithms based on career data to recommend
courses that may be useful to a user in their professional development.
For example, if a user works in the IT industry, they may be
recommended courses about IT.
Paid
Khan
Academy
Khan Academy uses algorithms based on the user's browsing history to
recommend courses that may be useful to the user in their learning
process. For example, if a user watches a course on mathematics
Free of charge
YouTube
YouTube uses algorithms based on a user's browsing history to
recommend videos that may be of interest to the user. For example, if a
user watches a video about programming, other videos about
programming may be recommended. YouTube also uses algorithms
based on user feedback to recommend videos that users are likely to find
useful.
Free of charge
TED-Ed
TED-Ed uses algorithms based on user feedback to recommend videos
that users are likely to find useful. For example, if a user rates a video
highly, they may be recommended other videos that are also highly rated
by other users.
Free of charge
Source: Created by the authors based on Aldowah, Al-Samarraie & Fauzy (2019).
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Learning recommender systems have a number
of benefits. They can help users find courses that
match their preferences and needs. They can also
help save time and resources by recommending
courses that are likely to be useful. However,
learning recommender systems are not without
their drawbacks. They may not be as accurate
because they are based on data about users' past
activity. There is also the possibility of bias, as
recommendations may take into account the
interests of the majority rather than the individual
user (Abed Ibrahim & Fekete, 2019).
These technologies also allow for real-time
monitoring of learning progress and adaptation
of curricula to meet the needs of each student.
They help to create opportunities for more
effective and individualised learning,
contributing to the success of each student.
When considering the ethical aspects of using AI
in education and collecting personal data of
students and learners, it becomes clear that there
is a need to pay special attention to this issue. On
the one hand, the use of AI can greatly facilitate
learning by creating individualised learning
trajectories and recommendations, but on the
other hand, it raises questions about privacy and
personal data protection. When AI is used to
analyse learning progress, systems often have
access to various types of personal information,
including academic achievement, learning style,
and even emotional state (Niemi, Manhica,
Gunnarsson, Stahle, & Larsson, 2019). As a
result, this raises questions about who has access
to this data and how it will be used. It is also
important to consider the bias and relevance of
the recommendations provided by the AI. If the
system is based on other users' data, it may result
in recommendations that take into account the
interests of the majority rather than the individual
needs of each pupil or student (Alzain, 2019).
When considering the technical aspects of
implementing personalised programmes using
AI in education, it is important to examine the
technological infrastructure. This includes
researching the available platforms, software,
and hardware that can be used to create and run
personalised learning programmes. Technical
aspects also include data collection and
processing. Effective personalised learning
requires the collection and analysis of a large
amount of data about each learner or student. The
study should include an analysis of how this data
is collected and stored, as well as its security and
privacy.
They require consideration of how to integrate
personalised programmes with existing
educational platforms and systems. How do these
new solutions interact with other components of
education, and how can individualised
programmes be made to work seamlessly within
the wider context of learning? Analysing the
technical aspects of implementing individualised
learning programmes using artificial intelligence
requires studying the technological capabilities,
data collection and processing tools, and
integration aspects for the effective
implementation of these programmes in the
educational sphere.
This study has practical and theoretical
implications that are important for the further
development of education and the use of
technology in learning. Practical implications
include the possibility of creating and
implementing individualised curricula that can
adapt to the needs of each pupil or student. This
can improve the quality of learning and ensure
greater student success in the learning process.
Practical implications also include the
identification of optimal technical solutions for
the implementation of individualised
programmes, which will help educational
institutions and teachers find the most effective
ways to apply artificial intelligence. The
theoretical implications are to broaden the
understanding of how AI technologies can be
used in education. This helps to improve
theoretical models of learning and develop new
approaches to individualised learning. Also, this
research can contribute to the development of
ethical standards and policies for the protection
of personal data in education, which is of
important theoretical importance.
Conclusions
This study has thoroughly analysed the technical
and methodological aspects of implementing
individualised learning programmes using AI in
the educational process. The findings emphasise
the importance and potential of such programmes
for the further development of education.
The study's technical dimensions suggest that
existing technologies and platforms provide the
capability to develop personalized programs
customized to the requirements of every student.
It is crucial to investigate how these technical
solutions can be integrated into the educational
process and assess their compatibility with
established MOOCs.
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Methodological considerations highlight the
importance of examining how to recognize the
requirements and capabilities of each learner, as
well as developing a methodology to assess the
effectiveness of personalized programs. Taking
into account different learning approaches, age,
and individual characteristics of each student, it
is important to develop a methodology that
would help achieve the best results. From a
practical perspective, the introduction of
individualised learning programmes using AI can
improve the quality of learning and help each
student achieve greater academic success.
However, it is also important to consider ethical
issues and protect users' personal data.
The study found that individualised programmes
create more incentives for students through a
personalised approach to learning. The
introduction of such programmes can help
increase students' motivation and engagement in
learning. It is noted that individualised
programmes allow more accurate consideration
of individual needs and level of learning for each
student. Attention is focused on the fact that
individualised programmes allow students to
receive personalised support according to their
specific challenges and strengths. This can lead
to more effective training and skill development.
Overall, the study emphasises the importance of
individualised programmes in modern education
and points to the potential of artificial
intelligence to achieve this goal. Taking into
account technical and methodological aspects,
effective individualised curricula can be
developed that meet the needs of each pupil and
student.
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