Artificial intelligent based teaching and learning approaches: A comprehensive review

International Journal of Evaluation and Research in Education (IJERE)
Vol. 12, No. 4, December 2023, pp. 2387~2400
ISSN: 2252-8822, DOI: 10.11591/ijere.v12i4.26623  2387
Journal homepage: http://ijere.iaescore.com
Artificial intelligent based teaching and learning approaches: A
comprehensive review
Thuong T. K. Nguyen1
, Minh T. Nguyen2
, Hoang T. Tran3
1
Faculty of International Training, Thai Nguyen University of Technology, Thai Nguyen City, Viet Nam
2
International Training and Cooperation Center, Thai Nguyen University of Technology, Thai Nguyen City, Viet Nam
3
Center of Electrical Engineering, Duy Tan University, Da Nang City, Viet Nam
Article Info ABSTRACT
Article history:
Received Jan 25, 2023
Revised Jul 6, 2023
Accepted Sep 6, 2023
The goal of this study is to investigate the potential effects that artificial
intelligence (AI) could have on education. The narrative and framework for
investigating AI that emerged from the preliminary research served as the
basis for the study’s emphasis, which was narrowed down to the use of AI
and its effects on administration, instruction, and student learning.
According to the findings, artificial intelligence has seen widespread
adoption and use in education, particularly by educational institutions and in
various contexts and applications. The development of AI began with
computers and technologies related to computers; it then progressed to web-
based and online intelligent education systems; and finally, it applied
embedded computer systems in conjunction with other technologies,
humanoid robots, and web-based chatbots to execute instructor tasks and
functions either independently or in partnership with instructors. By utilizing
these platforms, educators have been able to accomplish a variety of
administrative tasks. In addition, because the systems rely on machine
learning and flexibility, the curriculum and content have been modified to
match the needs of students. This has led to improved learning outcomes in
the form of higher uptake and retention rates.
Keywords:
Artificial intelligence
Data mining
Deep learning
Education
Educational management
Learning analytics
Machine learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hoang T. Tran
Center of Electrical Engineering, Duy Tan University
Danang, 550000, Vietnam
Email: tranthuanhoang@duytan.edu.vn
1. INTRODUCTION
Because of advancements in technology, many industries are transforming to meet the needs of a
diverse array of professions, including both civilian and military applications [1]. The internet and mobile
phones are related technologies that considerably impact our daily lives [2]. There is a vigorous argument
among psychologists, educators, and parents regarding the appropriate amount of screen time children should
have. Another fast-spreading innovation can profoundly revolutionize the educational system.
The term “artificial intelligence” (AI) refers to a well-known technology or field of study. Some
science fiction authors and novelists predicted the meteoric rise to fame that it would one day achieve in the
past. Technology is here to stay and is causing changes all over the place, although things have not yet gone
exactly as planned. It is not very often that a piece of technology is developed that can be applied across a
wide range of fields, including education [3], [4].
Between 2017 and 2021, there will be a 47.5% growth in the use of artificial intelligence in
education in the United States [5]. Current projections indicate that this growth will take place. A survey
conducted on the market for artificial intelligence within the education sector of the United States found this

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to be accurate. Even though many professionals in the field of education believe that technology will not
eliminate the need for teachers, they acknowledge that it will impact how teachers carry out their
responsibilities and the educational methods that are advised. It is more than just modifying how teachers
carry out their responsibilities. In addition to this, it is altering how pupils are educated. This expansion is
taking place in countries other merely the United States. According to the market research engine [6], the
application of AI in educational settings is expected to increase on a global scale at a rate of 45% yearly,
reaching $5.80 billion by 2025.
This study aimed to evaluate the applications of artificial intelligence (AI) and investigate how AI
has influenced the administrative, instructional, and learning aspects of educational settings. AI in education
first manifested itself in computers and other computer-related systems, but it has since progressed into web-
based and online learning platforms. Due to the development of embedded systems, it is now feasible to
employ robots or humanoid robots as teacher coworkers or independent instructors. Additionally, chatbots
may now be programmed to assume teacher- or instructor-like tasks. The utilization of various platforms and
technologies has increased educators' efficacy and efficiency, resulting in a more robust or superior level of
instructional quality. AI has improved students' learning experiences by making it possible to customize and
personalize educational materials based on the requirements of individual students and their unique talents.
The field of education has been significantly influenced by AI, particularly in administration, instruction, and
learning within the educational system as a whole or inside specialized educational institutions.
The educational system is highly reliant on methods that have now become obsolete. Even if there
are knowledge gaps in the existing grading systems, an injection of AI techniques and technologies can usher
in a new era of automation. The application of AI in educational settings is anticipated to generate revenue of
$3.68 billion by 2023, as reported by marketsandmarkets.com, representing a compound annual growth rate
(CAGR) of 47% between the years 2018 and 2023 [7]. Educators should consider building a digital
transformation for their field that incorporates the appropriate AI technologies and produces the desired
results. Let's look at some of the most critical applications of AI in the educational sector [8].
It can be challenging to ensure that all children, regardless of their geography or race, receive an
education of the same high quality. On the other hand, technology powered by AI has the potential to
drastically reduce the distance that separates students from their teachers, peers, and other educators. AI
technology enables the collection of intelligent data, the creation of individualized schedules, the creation of
one-of-a-kind jobs, and access to educational resources around the clock. They can also develop language
translations, subtitles, and many plug-and-play apps that may be spread throughout different locations to
foster universal learning and tear down barriers between outmoded and ineffective educational techniques
[9]. With the capacity to incorporate the cloud, AI-powered systems can analyze large volumes of data in
real-time. This enables any firm with branches worldwide to carry out swift administrative, testing, and
instructional duties in a standardized manner across all of their locations.
To eliminate the need for manual administrative tasks in educational institutions such as scheduling,
rescheduling classes, marking attendance, grading papers, finance and accounting, and record-keeping [10].
This frees the employee from having to complete the monotonous, routine tasks that had been required of
them. Artificial intelligent systems can assist with a wide variety of tasks, including the following:
i) Eliminating truancy warnings; ii) Automatically send report cards and other correspondence to parents;
iii) Schedule and plan meetings; iv) Send normal student forms, enrollments, and other documentation to the
appropriate department via automation; v) Reduce the amount of time spent on progress reports; and
vi) Make any other record-keeping chores more efficient. This may free up professors and teachers to focus
on enhancing the quality of education rather than reducing the burden of laborious paperwork and easing the
stress of their jobs.
After gathering essential data on metrics for scoring assignments from papers that teachers or
professors have graded, AI-powered grading software will employ machine learning to construct calculating
systems [11]. These metrics will be derived from the papers themselves. These technologies are designed to
comprehend and simulate the human process of grading students that teachers carry out. Essays, papers, and
tests can be promptly assessed in various languages using the input of professors and artificial intelligence. It
is possible for them to swiftly incorporate them into an already existing cloud-based platform or virtual
environment. They are helpful in situations where there are many papers to grade, allowing teachers to focus
on other vital duties rather than spending time grading the papers.
Voice assistants are an engaging and helpful tool that may benefit users in various ways while
bringing education into the home. The use of voice assistants in educational settings has several benefits,
including the following [12]: i) Efficient time saving for students and teachers; ii) Community learning
opportunities; iii) Personalized instruction in seconds. Voice assistants powered by artificial intelligence can
be utilized within mobile applications even if the user does not own a smart speaker.

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AI systems play a critical part in the educational process by creating individualized study plans and
adjusting course content to meet the requirements of each student. They identify areas in which students'
knowledge could be improved and provide educational materials, evaluation tools, and feedback systems for
students of all ages, from preschool to college. Students can study at their own pace if they use software
powered by AI, games, and tools that can design a strategy for people to learn at their own pace, time, and
requirements for repetitive repetition. This machine-assisted classroom setting can considerably enhance
diversified and adaptive learning, offering all students a firm foundation [13]. It can also help teachers create
unique lesson plans based on the needs of individual students.
The term “smart content” can refer to a wide variety of mediums, such as digital textbooks,
manuals, instructional snippets, and films, as well as AI systems that construct personalized settings for
educational institutions based on the strategies and goals of those institutions. Finding areas in which
artificial intelligence tools might be helpful is one way to actualize education's personalization, which is
currently a global trend. For example, educational institutions can develop augmented reality (AR)/virtual
reality (VR)-based learning environments that are complemented by web-based instruction [14].
Technologies for AI Monitoring and Evaluation can simplify content for various learning styles and mix up
learning curves and speed. AI and machine learning algorithms can identify areas of the curriculum that can
be improved to close the gaps in inaccurate or ineffective knowledge and support teachers in resolving them
when a substantial percentage of students provide incorrect responses. This helps teachers close the gaps in
inaccurate or ineffective knowledge more quickly.
Tutoring programs based on artificial intelligence, often known as intelligent tutoring systems (ITS),
can manage tailored feedback and instructions for one-on-one education [15]. However, because they need
more intelligence to learn in the same way humans can, they will never be able to take the position of
teachers. They are helpful when human instructors are not accessible to teach and evaluate short lessons that
may be done online and can be taught by the computer. In e-learning environments, it may be utilized to
instruct various courses, including linguistics, geography, circuitry, medical diagnostics, computer
programming, mathematics, physics, genetics, and chemistry, amongst others. They are developed with
engagement, grading standards, and comprehension in mind while they are being made.
Students can connect directly to their mobile devices or laptops using virtual reality technologies,
allowing them to interact with content. An immersive learning experience can be enhanced with the use of
virtual learning environments, which can also provide educational experiences for groups of students and
student counseling services. Virtual reality headsets can help kids who have attention deficit hyperactivity
disorder (ADHD) by eliminating distractions and increasing their ability to focus [16]. Learners can also
benefit from interactive virtual simulations concerning the coaching of soft skills, the development of life
skills, and personal growth.
The remaining parts of this work are structured as described. The background of AI and its technical
applications in education are discussed in section 3, which demonstrates the various technical components
that can be utilized in teaching. The many applications of AI in educational settings, such as teaching,
learning, and administrative tasks, and discussions of their effects, are presented in sections 3, 4, and 5,
respectively. In section 6, conclusions and further research are discussed.
2. AI BACKGROUNDS AND TECHNICAL ASPECTS IN EDUCATION
2.1. The background of AI and approaches in education
The term “artificial intelligence” (AI) is widely used in connection with computers. A comparison
of the numerous articles reveals a tendency away from the computer alone, the hardware and software, or the
equipment, as artificial intelligence. This is true even though computers may have served as the foundation
for the development of artificial intelligence. It is now much simpler to include artificial intelligence in
machines and other things, such as robots and buildings, due to the proliferation of embedded computers,
sensors, and other developing technologies [17]. The research presents two distinct definitions and accounts
of AI. They distinguish between AI as a theory and a field when defining it. According to their definition, AI
is a subfield of computer science that aims to solve various cognitive difficulties commonly associated with
human intellect. These tasks include learning, comprehending problems, identifying patterns, and adapting
[18]. In the article, AI was defined as a theoretical framework for directing the creation and application of
computer systems with intelligence and the capacity to carry out activities that demand human intellect, such
as speech recognition, visual perception, language translation, and decision-making.
The definitions of AI proposed by numerous experts and in other studies highlight nearly
comparable characteristics or qualities of AI. Other studies have also looked at this topic. AI is defined by
Verma et al. as “machines that can resemble human reasoning” [19]. In a manner parallel to this, it was
mentioned that the creation of AI required several decades of research and development. System designers,
data scientists, product designers, statisticians, linguists, cognitive scientists, psychologists, education

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experts, and many other professionals worked together to develop education systems that possessed some
level of intelligence and could carry out a variety of tasks, such as supporting teachers and students [20].
According to the author, artificial intelligence is founded on enhanced software and programming skills, such
as algorithmic machine learning, which enables computers to carry out a variety of jobs that need intelligence
equivalent to that of humans and environmental adaptability [21]. A similar set of observations was made
about artificial intelligence, which may be described as the ability of a machine or computer to imitate human
intellect and conduct [22].
According to these requirements and descriptions, artificial intelligence is imbuing computers with
intelligence and the potential to engage in human-like behaviors, such as cognition, learning, decision-
making, and environment adaptation. As a result, some features and principles are vital for AI. This
definition and analysis of AI lead to the conclusion that intelligence, or the potential of a machine to
demonstrate some level of intellect and perform a variety of activities that need human-like abilities, is a
crucial component of AI.
Artificial intelligence and machine learning have recently received much attention for mobile
devices to improve computing quality and enable new applications like face unlocking, speech recognition,
natural language translation, and virtual reality. However, machine learning needs a lot of computer power to
do complex training and learning. Some systems that are effective at computation have been created to solve
this issue. Qualcomm introduced the Snapdragon Neural Processing Engine in 2016 to aid GPU processors in
accelerating the execution of neural networks. HiSilicon suggested the HiAI platform for running neural
networks. To efficiently execute machine learning models on portable devices, the Android Neural Networks
application programming interface (API) was developed [23]. This API offers mobile consumers much value
by reducing network complexity and latency. Mobile learning networks for AI are well-developed and
include SqueezeNet, MobileNet, and Shufflenet [24]. Thanks to the advancement of AI technology in mobile
devices, mobile education has reached a new level. This technology provides ease by rapidly assisting
students and enabling interactive and personalized learning. Because AI can link students to the virtual
classroom, virtual reality, for example, enables the learning process to extend outside of the learning
environment to establish a global classroom. Additionally, AI-powered chatbots deliver individualized online
education and translate teacher interactions into chat conversations. With this technology's aid, students'
comprehension levels can be assessed.
2.2. Technical aspects in education
Table 1 demonstrates numerous scenarios in which AI methods might help the educational industry.
Intelligent teaching, cutting-edge virtual learning, and data analysis and prediction are a few examples of AI-
assisted education. Table 1 lists the most important uses of AI in education and supporting technologies. It is
crucial to remember that AI-enabled education is becoming more crucial as learning requirements increase
[25]. Teachers and students receive timely, tailored training and feedback from intelligent educational
systems. To boost the value and efficacy of learning, they mostly use machine learning-related technologies
[26], closely tied to statistical models and cognitive learning theory.
Table 1. AI techniques with scenarios in teaching and learning
No AI education scenarios AI possible techniques
1 Intelligent schools Virtual labs; A/R; V/R; hearing and sensing technologies;
2 Online and mobility of remote education Virtual personalized assistants; Edge computing; Real-time analysis;
3 Personalized intelligent teaching Intelligent teaching systems; Data mining or Bayesian knowledge interference;
Learning analytics;
4 Grading and evaluation Computer vision; Image recognition; Prediction systems;
5 Students and Schools assessment Academic analytics; Adaptive learning methods; Personalized learning approaches;
6 Building curriculums Big data analysis; Clustering algorithms; Machine Learning or Deep Learning
technology;
In AI systems, several strategies for learning analysis, recommendation, knowledge understanding,
and acquisition are included [27]. These strategies are based on machine learning, data mining, and
knowledge models. An AI education system comprises teaching content, data, and intelligent algorithms.
This system is divided into two parts: the system model (which includes the learner model, teaching model,
and knowledge model) and intelligent technology [28]. As shown in Figure 1, a model’s contribution to
creating a data map, which builds structures and association rules for the acquired education data [29], is
essential for enhancing the amount of learning that occurs. The model operates as the AI system’s central
processing unit (CPU), while several technologies provide the system with its driving force.

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Improving the learner model utilized in AI learning systems is necessary to expand autonomous
learning capabilities. The information gathered regarding the conduct of students while they were engaged in
the learning process is used to determine it. The ideas and capabilities of the learners are evaluated so that
their learning capacities can be determined. Following this, the knowledge analysis findings are mapped to
determine the level of knowledge mastery possessed by the learners. Learner modeling establishes linkages
between learning goals and various components, such as instructional styles, learning resources, and
technologies [30]. The knowledge model generates a knowledge structure map with specific learning
components. These learning components usually include technical information and guidance to avoid
common errors and misunderstandings [31]. The teaching model is responsible for determining the rules of
the knowledge field. This model, a combination of the learner and the knowledge field models, allows
instructors to customize their lessons.
Students are more likely to exhibit positive behaviors, take action, or seek support as they go
through their schooling. Artificial intelligence systems can always rely on the built-in teaching ideas
provided by the tutoring model. The user interface provides feedback and explanations regarding the learners'
performance using various input mediums, including speech, typing, and clicking (texts, figures, cartoons,
and agencies). The enhanced human-machine interface will be able to do AI-related activities such as natural
language interaction, speech recognition, and emotion detection in the future.
2.2.1. Machine learning
The basis of machine learning is the process of parsing, which generates meaningful patterns and
ordered data based on a sample of data called “training data”. By generating recommendations, machine
learning can assist students in selecting classes and universities, for instance. Using data on student
accomplishments, aspirations, and preferences, it “matches” students to institutions where they can flourish
the greatest. Moreover, this technology can assist teachers in comprehending how students assimilate each
concept [32]. In response to cumulative student performance data, teachers might change their teaching
tactics to assist students in the better comprehending course material. For example, picture recognition and
machine learning predictions can be used to grade student examinations and homework more quickly and
accurately than humans. Deep learning, a machine learning subfield, has recently attracted much media
interest. The most common techniques are decision tree learning, inductive logic programming, clustering,
reinforcement learning, and Bayesian networks. Deep learning emphasizes the learning of successive layers
that create progressively meaningful representations from a technological standpoint. These layer features are
extracted using neural networks, which are arranged in literal layers piled on top of one another.
2.2.2. Learning analytics
Learning analytics focuses on data obtained from student attributes and knowledge objects derived
from the learner model and the knowledge field model. Learning analytics is a concept that applies newly
developed technology, specifically machine learning, to a field traditionally considered non-technical, such
as education. The objective is to modify educational practices so that they are better suited to the capabilities
and requirements of particular students. This may involve intervening with students who are at risk or
providing feedback and instructional content [33]. Machine learning, data visualization, learning sciences,
and semantics are implemented here. For instance, AI-based competency learning can successfully detect
insights about students and foresee the critical competencies they can pursue, enabling institutions to take
preventative action. The field of learning analytics uses the varied learning capabilities of AI and
competency-based education. If students drop out of school, AI might consider many aspects before labeling
prospective students as likely to drop out. This would provide schools with early warning systems and data
that they could use. The next stage for learning analytics is to widen its reach to encompass, among other
things, interpersonal skills, the arts, and literature. This will bring an entirely new degree of complexity to
measuring and analyzing learning outcomes and competencies. Learning analytics are in a catch-22 situation
because they need to be specialized enough to be used in specific learning contexts while still being general
enough to be used in various educational settings and classes. The application of learning analytics, which
uses innovative methods to assist students, teachers, administrators, and institutions in their respective
learning processes, will become increasingly widespread.
2.2.3. Data mining
Educational data mining aims to give students automated, organized answers. AI-based educational
data mining aims to make rules of natural association and give students knowledge that fits their needs. For
example, a small number of writing assignments can be used to evaluate students’ grades and demographic
information [34]. This can be done with machine learning regression, which can also be used to predict how a
student will do in the future. Data mining is also becoming a meaningful way to improve how we learn and
how much we know. It helps us learn more about learning settings and students.

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The technique of retrieving hidden knowledge by pattern recognition and predictive modeling, or to
put it another way, is known as data mining. It enables educators to modify curriculum creation in
educational institutions. The ability of data mining-based AI to achieve individualized learning from
knowledge field data, allowing students to learn at their own pace and choose their preferred learning style
with the help of AI, is one of the essential uses. With personalized learning, students choose the subjects that
interest them, and teachers modify their pedagogical approaches to reflect the students' preferences [35]. AI
can create its intelligence more precisely and with more dependable outcomes using data mining (e.g., via
machine learning). Figure 1 shows the structure of the technology used in AI education. This structure creates
a data map that shows the structures and rules for how educational data is linked.
Figure 1. An illustration of the AI education technological structure
3. AI BASED TEACHING APPROACH
According to the research of the articles obtained and included in the analysis, teaching or
instruction is one of the essential domains where AI systems have invaded. AI has contributed to the
development and implementation of systems that are demonstrably effective educational tools [36]. As
depicted in Figure 2, these technologies have led to an improvement in education quality. The image depicts
several AI applications utilized by educators to perform their responsibilities. Numerous papers were
examined to examine and highlight the many platforms and applications of AI as a teaching tool. This article
explores various technologies, such as virtual reality, to communicate concepts to pupils or show information
effectively. Various AI uses as pedagogical tools or instructional platforms, such as simulation-based
instruction, are also explored. Other researchers have studied a similar concept of using virtual reality
elements as an AI component in education. Several studies, for instance, highlight virtual reality, 3-D
technology, and highly interactive simulation as teaching tools that facilitate student comprehension of the
subject matter [37]. Similarly, the results suggest that AI is utilized in medical education through virtual
reality and simulation, which educate medical students through practical aspects of their education, such as
procedures and human anatomy, among other topics.
Other research has been done on how to put AI into machines or robots, as well as how to make
powerful educational tools and improve the quality of teaching methods. Some research says that putting AI
in education principles into robots and using robots as teacher assistants and colleagues, called cobots, is
another important use of AI in education as a teaching tool. Cobots can be used to do basic and even
advanced teaching tasks, like teaching students how to read and say words [38]. The study found that using
AI in education, mainly as a teaching tool and in combination with other technologies, has led to the
developing and using better teaching tools [39]. On the other hand, Pokrivcakova emphasizes making and
using chatbots, which are online robots that can talk and have conversations, as well as putting AI into
Knowledge
structure,
metadata
Content
map
Learning
map
Course Target Student profile Personality Instructor data
Knowledge
Inference
Machine
learning
Reinforcement
learning
Computer
vision
Natural language
processing
Course
assessment
Adaptive
learning
Learning analysis Customized
teaching
Knowledge
acquirement

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computer programs. Chatbots can answer common questions from students and, in some cases, send out
learning materials [40]. AI makes it possible for humanoids and other robots to think, make decisions, talk,
and have conversations. This makes them useful as teaching and learning tools.
Figure 2. Contents of AI technologies to support teaching roles in educational areas
As a result of the article study, other ways AI can be used in education have been found. Intelligent
tutoring systems, for example, are talked about in different ways in another research. For example, animated
conversational agents like chatbots or robots, as well as intelligent tutoring systems (ITS) that can talk and
have conversations, have helped make teaching more effective [41], [42]. Pokrivcakova’s AI applications for
education, such as computer-assisted language learning (CALL), which gives students or learners
individualized instructions, and language learning writing and translation tools [42], all focus on the same
topics. There are also talks about other ways AI can be used in education, especially when giving instructions
on how to do a teacher or instructor's job. With the use of AI in web-based education, especially the use of
AIWBES in teaching and the addition of teacher-like functions, the platform has become a powerful tool for
helping teachers. This is looked at, especially the use of AIWBES in teaching and the addition of teacher-like
functions, which makes the platform a powerful tool for helping teachers [43].
It is likely that discussions on IWBE, also known as intelligent and adaptive web-based systems,
will take place to ensure that the technology used in education, web-based education, is an effective and
methodical way to enhance the learner experience. In these discussions, teachers will be studied and
presented as social agents in this system. After that, the system attempts to comprehend teachers and provide
them with help so that they can fulfill their mandates and give students instructions and guidance [44]. It has
been utilized either on its own as an independent educational tool or to assist teachers in the performance of
their duties. Several different technologies and methods have been adapted to make use of AI.
3.1. Discussion of the impacts
Figure 2 depicts an additional component of education examined in this study: the usage of AI by
teachers or in instruction. An examination of multiple studies indicates that educators are rapidly adopting
and employing AI in various ways as a teaching tool or for educational purposes. The use of artificial
intelligence (AI) in instruction or as a pedagogical aid has considerably impacted this facet of education.
Numerous examined and studied investigations demonstrated that it had increased teachers' job effectiveness,
efficiency, and caliber. In this context, effectiveness is measured by delivering relevant content following the
curriculum and the learner's specific needs and abilities. Simultaneously, students evaluate the effectiveness
and quality of the knowledge assimilation and retention of the pupils. Considering these operational
definitions and descriptions of efficiency, quality, and effectiveness, the study's findings suggest that AI has
contributed to achieving quality, effectiveness, and efficiency in education.
Thanks to AI, teaching has become more effective. Previous researchers [41] stated that using
evidence-based or empirically supported methods, like the wide use of cognition and learning models [45],
has ensured that students learn the most from the content and remember it best. Learner-centered systems like
DeepTutor and AutoTutor encourage customization and personalized information based on the learner's skills
AI
Applications
Pedagogical tools
Roles of
Teachers
Planning
(Receive student background, identify
students’ need, plan activities)
Implementation
(Give feedbacks, optimize activities, track
progress, manage time.)
Assessment
(Automated evaluation, predict
performance, provide feedbacks.)
Simulation-based instruction
Advanced teaching tasks
Online computer-based robots
Intelligent tutoring systems
Web-based instructors
Virtual reality

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and needs. This makes the learning experience better for the learner and helps them reach their learning
goals. Some more arguments show that AI has improved the quality and effectiveness of teaching because
modern systems are technology-based adaptive systems. This means that the materials or information offered
are based on the learners' needs, making for the best learning experience [46]. AI ensures that course content
is delivered better, especially in online and web-based learning platforms. This starts with the planning of the
curriculum and ends with the actual delivery of information or instructions.
The development and usage of AI, particularly its incorporation in online and web-based learning
platforms, has improved education because AI has made it possible to construct and utilize better teaching
tools for these platforms [47]. Other researched research lists the same benefits or boosts to learning. A study
[48] addressed the significance of adaptive IWEBS and instructions based on observed and learned learner
behavior. Due to the capacity to personalize the pedagogical strategies employed, these capabilities enable
the platforms to improve the quality of learning and instructional efficacy. It has been demonstrated that
computer-aided learning (CAL) and computer-based training (CBT) utilize a “put everything on the web”
strategy. Unlike intelligent tutoring systems (ITS), which customizes, individualizes, and personalizes
learning [49], they may not align with the student's learning objectives. It was noticed that AI significantly
impacts education, notably enhancing instructional efficacy and efficiency. AI systems, particularly tutoring
or instructional systems, were created to address the challenges inherent in one-on-one teacher-student
tutoring, improving the overall quality of instructors' work [50].
Because of the analysis, more essential themes or ways that AI has changed the quality of teachers'
work were found. Several studies [51]–[53] have shown that technology, especially artificial intelligence
(AI), can help maintain academic integrity through tools like Grammarly, Turnitin, and White Smoke that
check for plagiarism, proctor students, and keep an eye on how they act online. Other studies [54], [55]
talked about gamification, which uses AI for educational purposes and significantly impacts instruction
quality. Gamification is also a part of or uses virtual reality and 3-D technologies differently. These studies
also talked about the benefits of simulation, team-viewer applications, and gamification, which are all closely
related to VR and 3-D or even used to improve the effectiveness and efficiency of instruction. Other studies
[56]–[59] have looked at how expressive humanoid robots with dialogue and conversational skills can
improve the quality of instruction by getting students to interact with each other. This is because these robots
have more human-like abilities and look like people.
4. AI BASED LEARNING APPROACH
Learning, an integral part of education, is another aspect of education that is included in the
research. Artificial intelligence may help students learn more quickly and efficiently when combined with
top-notch learning materials and instruction. Artificial intelligence (AI) technology can also help students get
back on track more quickly by alerting teachers to problems that a human eye cannot see. Figure 3 illustrates
the different ways that AI has been adopted, utilized, or exploited to help students' learning after reviewing
and analyzing the numerous articles included in the study. There have also been discoveries of software or
programs that employ AI to help student learning. Customizing and personalizing lessons and materials
following students' needs, abilities, and talents is one of the most significant ways AI has been used to
improve student learning [60]. An individual student's learning route is “adapted” in real-time by adaptive
learning software using AI or machine learning techniques. There are various strategies to increase students'
knowledge intake and retention, which is the cornerstone of learning [61], [62]. For instance, AI in education
has removed national and international borders, allowing global access to learning through online and web-
based platforms [63], [64].
The writings led to the discovery of numerous platforms and apps. Some platforms will promote
content personalization and customization, enhancing learners’ learning experiences by promoting
information intake and retention. For instance, a program like Knewton gives students real-time suggestions
based on learning styles identified by machine learning algorithms. Then it adapts the course materials or
content to their requirements [65]. Other platforms with comparable features, such as Cerego, Immersive
Reader, and CALL, can improve students’ educational experiences at all levels, from kindergarten through
undergraduate and graduate programs at universities [65], [66]. According to the authors, using chatbots and
integrating AI enhances students’ learning experiences since they use machine learning algorithms to deliver
content customized to their unique learning needs and abilities [67]. The author also looks at how machine
translation tools, adaptive educational systems, and intelligent tutoring systems are being used in education to
help students. AI ensures that material is tailored and personalized to learners’ needs and skills in various
ways.

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Figure 3. Contents of AI techniques to support learners
On the other hand, as earlier papers have shown, AI applications have been found to affect how
learners learn significantly. Deep learning is a way to improve how students learn, and it has been shown that
intelligent tutoring systems (ITS) and simulation-based learning can help with it [68]. Mitropoulos brings up
the same idea when he says that simulation and virtual reality make learning better for students [69]. Using
simulation, virtual reality, and other AI-based learning tools helps students prepare for the future and keep up
with how AI is used in the business world [70]. AIWBES is another way that AI could help students learn
more. AIWBES is more flexible and can make content that fits the learner's needs [71]. Interactive problem-
solving, a part of AIWBES, will help students do these tasks by giving them help from people who know
what they are doing at every step [72]. Online learning acknowledges and talks about the same AI skills. In
IWBE or intelligent and adaptive web-based systems, especially multi-agent systems (MAS), the learner is
included as a social agent. The system tries to figure out how the learner acts and responds by giving them
information that meets their needs [73], [74]. AI has had a significant impact on other parts of education.
However, the main goal of integrating, adopting, and using it in education has been to improve the learning
experience for students.
The student's educational experiences are yet another aspect of education that is covered by the
scope of this research and has been dramatically impacted by the incorporation and application of AI in
educational settings. When discussing the influence that AI has on education, it facilitates deep learning
because the conversational agents that are an essential part of the system will question and challenge students
until they can successfully explain themselves in detail, including the reasoning that lies behind their stance
[75]. This improves the amount of information the learner takes in and retains.
Several research shed light on the myriad ways that AI can assist students in their educational
pursuits. With the assistance of AI, learning progress can be monitored, including knowledge and
understanding, and the results are used to enhance the system's capacity to tailor information to the
requirements and talents of the students, motivating them and making use of their unique skills to increase
retention [76], [77]. For instance, Pokrivcakova asserts that artificial intelligence has made it possible to
design and implement intelligent learning systems and adaptable content tailored to each student's learning
needs and capabilities. One example of this would be intelligent virtual reality and its use in simulation
teaching and learning, which has been shown to improve learning [78]. The pedagogical benefits of
simulation and other technologies in a similar vein are also discussed in this article. It has been observed that
simulation and other related technologies provide students with practical exposure and experiential learning,
improving learning quality. The researchers in this article reviewed studies that highlighted the key benefits
of virtual reality and three-dimensional technology in learning, such as usability, enjoyment, learner
enthusiasm, motivation, and increased student interests [79].
Learning Analysis
Deep learning, machine learning, intelligent tutoring systems,
simulation-based learning, virtual reality.
AI techniques
Adaptive Learning
Monitor students’ learning habits
and level of knowledge
Customize curriculum and
materials for learning
Enhance students’ learning
experience
Personalize learning to adapt
learning paths for students
Advice/recommend students in
their study
Support self-study and self-
evaluation
AI based support
The outcomes

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Subsequent research focusing on web-based systems uncovered additional AI benefits and their
impact on the quality of learning. Kahraman noticed, for instance, that crucial AIWBES concepts or aspects,
such as adaptive hypermedia, information filtering, class monitoring, and collaborative learning, promote
student engagement, interactions, and learning [80]. Previous research who highlighted the link between
AIWBE and improved learning quality, emphasized the benefits of web-based platforms because the system
adapts and customizes instructions and information to detect and assess learner behaviors. StudentTracker
middleware, for example, will utilize online data about the learner, such as completed activities, learning
tracking, time, and other factors, to design pedagogical tactics for the AIWBE [81]. Two further benefits of
web-based platforms that have been found to facilitate learning are their affordability and their capacity to
expand worldwide access to education [82]. These platforms have enhanced the learning experience overall.
Another study has emphasized the advantages of AI for learning and how it affects learning [83].
For instance, AI has been applied to improve learning through writing and revision tools like Turnitin and
Pearson's Write-to-Learn tools [84], [85]. On the other side, many studies have highlighted AI's potential
harm or negative consequences on learning. A study discovered that AI might promote dishonesty and
compromise academic integrity by making it easier for students to use paper mills and paper-churning
websites or platforms [86]. However, as shown by several earlier studies, the benefits of AI for learning
generally outweigh the disadvantages [87].
5. AI BASED EDUCATIONAL MANAGEMENT
This section presents an overview of the study on the use of artificial intelligence in education,
focusing on administrative chores. Completing multiple administrative duties in the educational process, such
as evaluating student assignments and papers, grading them, and providing students with feedback, is one of
the main areas of education that AI is projected to impact. According to a study [88], the application of AI in
education has enhanced productivity in institutional and administrative operations, particularly for online
learning at a distance. Specific tools, such as Knewton, enable teachers to provide feedback to students based
on how they utilize the program. Other studies and publications that analyze administrative support systems
follow similar perspectives.
For example, intelligent tutoring systems (ITSs) have been proposed to perform diverse functions,
such as grading and providing students with feedback on their work [89]. In addition to their significant
responsibilities, which include directing and instructing students to ensure their academic success, instructors
who collaborate with ITS are more efficient in various administrative activities. The research by Mikropoulos
and Natsis validates the findings and conclusions of these investigations; the utilization and application of AI
in education have boosted the efficacy and efficiency of administrative duties such as grading student work
[89]. A study of the current online learning environment reveals systems such as Turnitin and Ecree, which
provide suggestive grading, verify student work for plagiarism, and enable instructors to carry out other
administrative tasks. Several administrative tasks that would have taken considerable time to complete
without AI are now performed more efficiently.
The utilization of artificial intelligence (AI) in different forms and for diverse purposes has had a
considerable impact on how administrative and management activities are implemented in education. It has
made administrative tasks easier, such as grading assignments and giving students comments to lecturers or
instructors. The grading aid elements in the AIWBE applications make it easier for instructors to assess and
remark on students' work [90]. On websites like Knewton, which enable teachers to assess student work,
grade it, and provide feedback on enhancing learning progress, similar tools and capabilities are accessible.
AI has improved administrative procedures and enhanced teachers' and instructors' ability to instruct and
guide students. Teachers can use the many features of intelligent tutoring systems to perform administrative
duties, such as grading and giving feedback [91].
Other AI-powered apps give teachers the tools they need to do various administrative tasks, like
finding plagiarism, grading papers, and giving students feedback on where they can improve. Apps like
Grammarly, Ecree, PaperRater, and Turnitin are examples of these apps. AI has made a big difference in how
much paperwork and work teachers have to do, especially when it comes to administrative tasks. This lets
them focus on their primary job, which is to teach and spread information in line with the institution's or
country's curriculum [92]. Even though this area of education wasn't the main focus of many of the articles
that were looked at, the ones that were looked at showed signs of improvement in the quality of
administrative tasks and processes as well as the instructors' or educators' efficiency and productivity in
doing different administrative tasks.

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6. CONCLUSION
This study aimed to determine how artificial intelligence will impact schooling. A literature review
served as a qualitative investigation’s research plan and method. To accomplish the purpose of the study,
journal papers, professional publications, and conference reports from experts were selected and analyzed.
Due to the invention and extensive usage of computers and computer-related technology, artificial
intelligence (AI) was developed and is now utilized in numerous industries. It has been demonstrated that the
use of artificial intelligence, which has been shown to have a significant impact on the industries in which it
is employed, was made possible in part by the development of personal computers and subsequent
advancements that have increased processing and computing capabilities. AI has been widely embraced and
utilized in the education sector, notably in the educational institutions examined in this study. There are
numerous possible applications of AI in education technology domains. Curriculum development in certain
subjects necessitates AI analysis of comprehension challenges, teacher backgrounds, and school teaching
infrastructures. AI approaches also assist teachers, students, and administrators with their study and teaching
strategies. Field researchers may encounter several difficulties due to the emergence of new problems.
ACKNOWLEDGEMENTS
The authors would like to thank Thai Nguyen University of Technology (TNUT), Ministry of
Education and Training (MOET), Viet Nam for the support.
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BIOGRAPHIES OF AUTHORS
Thuong TK. Nguyen is a Senior lecturer in English at Faculty of International
Training, Thai Nguyen University of Technology, Vietnam. She holds a BA in English
Language Teaching at Thai Nguyen University of Education, Vietnam and a MS. (Master of
Science in Education) in Curriculum and Instruction at Oklahoma State University, USA. She
is interested in language teaching methodologies, classroom research, learning autonomy,
authentic assessment and ESL/EFL curriculum development. She can be contacted at email:
nguyenthikimthuong@tnut.edu.vn.
Minh T. Nguyen is currently the director of International Training and
Cooperation center at Thai Nguyen University of Technology, Vietnam, and the director of
Advanced Wireless Communication Networks (AWCN) Lab. He has interest and expertise in
a variety of research topics in the communications, networking, and signal processing areas,
especially compressive sensing, and wireless/mobile sensor networks. He serves as technical
reviewers for several prestigious journals and international conferences. He also serves as an
Editor for Wireless Communication and Mobile Computing journal and an Editor in Chief for
ICSES Transactions on Computer Networks and Communications. He can be contacted at
email: nguyentuanminh@tnut.edu.vn.
Hoang T. Tran received an engineering degree in system measurement and
control from Danang University of Science and Technology in 1998, received a master's
degree in network and electrical systems in 2009 from the University of Danang, Defending
the PhD thesis majoring in Electronics and Communication Technology in 2016 at the
University of Technology, Vietnam National University, Hanoi. A guide to synthesizing data
sensors, wireless network transformation and the fields of control automation. He is currently
the Director of The Center of Electrical Engineering, Duy Tan University, Da Nang, Vietnam,
and the Lecture of the Faculty of Electronic Engineering, Duy Tan University, Da Nang,
Vietnam. He can be contacted at email: tranthuanhoang@duytan.edu.vn.

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