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Procedia Computer Science 201 (2022) 445–451
The
The 13th
13th International
International Conference
Conference on
on Ambient
Ambient Systems,
Systems, Networks
Networks and
and Technologies
Technologies (ANT)
(ANT)
March
22
25,
2022,
Porto,
Portugal
March 22 - 25, 2022, Porto, Portugal
Perceptions of Learners and Instructors towards Artificial Intelligence in
Personalized Learning
c
Ali
Al-Badiaa,, Asharul
Khan bb*,
Ali Al-Badi
Asharul Khan
*, Eid-Alotaibi
Eid-Alotaibic
a
aAcademic Affairs and Research, Gulf College, P.O.Box 885, Al Khuwair, Al Mabaila, Muscat, P.C.133, Sultanate of Oman
Academic Affairs and Research, Gulf College, P.O.Box 885, Al Khuwair, Al Mabaila, Muscat, P.C.133, Sultanate of Oman
Oman
Oman Chamber
Chamber of
ofc Commerce
Commerce and
and Industry
Industry and
and Research
Research Chair,
Chair, Sultan
Sultan Qaboos
Qaboos University,
University, Muscat,
Muscat, P.C.123,
P.C.123, Sultanate
Sultanate of
of Oman.
Oman.
cDept. Tourism and Archaeology, University of Hail, P.O.Box 2440, Hail City, Saudi Arabia
Dept. Tourism and Archaeology, University of Hail, P.O.Box 2440, Hail City, Saudi Arabia
b
b
Abstract
Abstract
The goal
goal of
of this
this research
research is
is to
to find
find out
out how
how learners
learners and
and instructors
instructors view
view Artificial
Artificial Intelligence
Intelligence in
in personalized
personalized learning.
learning. To
To
The
uncover variations
variations in
in learners
learners and
and instructors'
instructors' sentiments
sentiments regarding
regarding Artificial
Artificial Intelligence
Intelligence in
in personalized
personalized learning,
learning, the
the primary
primary
uncover
data obtained
obtained from
from learners
learners and
and instructors
instructors was
was analysed
analysed using
using Independent-Samples
Independent-Samples Mann-Whitney
Mann-Whitney U
U Test
Test and
and IndependentIndependentdata
Krushkal-Wallis Test.
Test. In
In the
the 91
91 investigated
investigated samples
samples of
of learners
learners and
and instructors'
instructors' responses,
responses, the
the study
study found
found that
that both
both of
of them
them
Krushkal-Wallis
holds
positive
perceptions
towards
Artificial
Intelligence
implementation
in
personalized
learning
at
Higher
Education
Institution
holds positive perceptions towards Artificial Intelligence implementation in personalized learning at Higher Education Institution
in Oman.
Oman. Furthermore,
Furthermore, the
the positive
positive attitude
attitude is
is not
not statistically
statistically dependent
dependent or
or influenced
influenced by
by the
the profession
profession (learner
(learner and
and instructor),
instructor),
in
gender (male
(male and
and female)
female) category,
category, and
and environment
environment such
such as
as level
level of
of Internet
Internet speed
speed (very
(very slow,
slow, slow,
slow, moderate,
moderate, fast,
fast, and
and very
very
gender
fast).
fast).
©
22 The
by
B.V.
©
2022
TheAuthors.
Authors.Published
Published
by Elsevier
© 202
202
The
Authors.
Published
by Elsevier
Elsevier
B.V.B.V.
This
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
This
is an
anopen
openaccess
accessarticle
article
under
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
This is
is
an
open
access
article
under
thethe
CCCC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review
under
responsibility
of
the
Conference
Program
Chairs.
Peer-review
under
responsibility
of
the
Conference
Program
Peer-review under responsibility of the Conference Program Chairs.Chairs.
Keywords: personalized
Keywords:
personalized learning;
learning; adaptive
adaptive learning;
learning; online
online learning;
learning; computer
computer supported
supported learning;
learning; intelligent
intelligent tutoring;
tutoring; Artificial
Artificial Intelligence;
Intelligence; Machine
Machine learning
learning
1.
1. Introduction
Introduction
Information
Information and
and Communication
Communication Technology
Technology (ICT)
(ICT) investments
investments in
in education
education sector
sector have
have increased
increased worldwide.
worldwide. By
By 2025,
2025,
global
global ICT
ICT education
education investments
investments are
are estimated
estimated to
to exceed
exceed $350
$350 billion
billion [1].
[1]. The
The emerging
emerging focus
focus is
is on
on implementation
implementation and
and
adoption
adoption of
of Artificial
Artificial Intelligence
Intelligence (AI),
(AI), Machine
Machine learning,
learning, and
and Big
Big data
data technologies
technologies in
in the
the education
education sector.
sector. There
There are
are many
many
applications
applications of
of AI
AI including
including accurate
accurate decision
decision making
making [2],
[2], resource
resource optimization
optimization [3],
[3], and
and realization
realization of
of Industry
Industry 4.0
4.0 revolution
revolution
[4].
[4].
The
The emerging
emerging technologies
technologies such
such as
as AI
AI has
has entered
entered into
into the
the traditional
traditional education
education system
system with
with the
the power
power to
to transform
transform it
it
completely.
completely. The
The motto
motto is
is to
to deliver
deliver personalized
personalized learning
learning materials
materials and
and courses.
courses. The
The concept
concept of
of personalized
personalized learning
learning is
is not
not new.
new.
Educators
Educators have
have long
long strived
strived to
to customize
customize education
education by
by adapting
adapting learning
learning opportunities
opportunities and
and instruction
instruction to
to learners'
learners' unique
unique talents
talents
and
and personalities.
personalities. Personalized
Personalized learning
learning practices
practices enables
enables instructors
instructors to
to tailor
tailor lessons
lessons and
and provide
provide them
them to
to individual
individual learners
learners in
in
contrast
contrast to
to traditional
traditional one-size-fits-all
one-size-fits-all learning
learning approaches.
approaches. Personalized
Personalized learning
learning according
according to
to U.S.
U.S. Department
Department of
of Education
Education
relates
relates to
to "instruction
"instruction in
in which
which the
the pace
pace of
of learning
learning and
and the
the instructional
instructional approaches
approaches are
are optimized
optimized for
for the
the needs
needs of
of each
each learner.
learner.
*
* Corresponding
Corresponding author.
author.
E-mail
address: a.khan@squ.edu.om
a.khan@squ.edu.om
E-mail address:
1877-0509
1877-0509 ©
© 2022
2022 The
The Authors.
Authors. Published
Published by
by Elsevier
Elsevier B.V.
B.V.
This is
is an
an open
open
accessThe
article
under the
the
CC BY-NC-ND
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
1877-0509
© 2022
Authors.
Published
by Elsevier
B.V.
This
access
article
under
CC
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review
of
Conference
Chairs.
This
is an under
open responsibility
access article
under
the CCProgram
BY-NC-ND
Peer-review
under
responsibility
of the
the
Conference
Program
Chairs. license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the Conference Program Chairs.
10.1016/j.procs.2022.03.058
446
2
Ali Al-Badi et al. / Procedia Computer Science 201 (2022) 445–451
Author name / Procedia Computer Science 00 (2022) 000–000
Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner
needs." [5] (p.9). Personalized learning is a form of learning that is different from other learning approaches. It relies on the fact
that learners have different cognitive power and abilities, therefore the instructions, assessments, and evaluation should be
different for each of them. In personalized adaptive learning the learners’ data is continuously fed to the system followed by
continuous assessments and real-time feedback [6]. The majority of personalized adaptive learning and evaluation systems also
provide a dashboard to help learners better evaluate their own progress and potential obstacles [7]. Assessment and feedback
systems are viewed as potentially powerful tools for evaluating behavioral traits (e.g., motivation) of learners while they engage
in the learning process because of their personalized and adaptive nature [8]. Personalized learning has the power to improve
learning effectiveness [9].
AI in education offers a lot of potential for personalizing learning and democratizing education around the world. It includes
AI-driven educational systems, intelligent agents, autonomous scoring and assessment, and learner-support Chabot’s for learnerto-learner/instructor communication and collaborative learning [10]. The demand for self-directed, customized, and flexible
learning is ever increasing and interest in AI for the education system is gaining pace due to recent advancements in the
technology [11]. Personalized learning is difficult and uneconomical in the traditional learning setting, however the advancement
in AI is helping to realize this dream [12]. As technology advances, more advanced adaptive technologies become available. It
compiles information such as learners' prior knowledge and academic achievement in order to forecast and improve learning
paths. This personalized approach goes a long way toward bridging the gap between socioeconomic status and special needs
among students [6].
Li and Wong [13] reviewed the features and trends of personalized learning in 203 journal papers from Scopus database
published between 2001 to 2018. It was found that the utilization of technology, such as intelligent learning/tutoring systems,
mobile devices, learning analytics, and augmented/virtual reality applications, was explicitly stressed in the most personalized
learning practices. Additionally, the practices' major goals were to improve learners' motivation, engagement, and satisfaction,
followed by increasing learning effectiveness. Moreover, the researchers suggested need of investigation related to the challenges
as such as learners’ acceptance, engagement, adaptability, and role of instructors. According to several findings, learners and
instructors hold opinions of varying degrees concerning AI technologies in personalized learning. Often they have a narrow
understanding of AI technologies and personalized learning. The difficulties of using the emerging technologies in personalized
learning practices need further investigation [14, 15]. Chang and Lu [16] pointed out the need of understanding learners’
participation and motivation for successful integration of AI in education and thus measuring the outcomes. The most significant
source of inspiration is one's own self. Therefore, there is a need to conduct research in the area inside Oman for successful
implementation and adoption of AI in personalised learning.
The study offers theory and experience that can be applied to better understand the aspects that influences the integration of
AI into the education system and the development of personalized learning. The following is the format of this paper: The
context and literature review are presented in Section 2. The study technique and analysis are presented in Section 3. Section 4 is
devoted to discussion. The findings, limitations, and future research are summarized in the last section.
2. Literature Review and Motivation
There are several types of modern online learning systems. For instance, Virtual learning (V-learning), electronic learning (Elearning), mobile learning (M-learning), and ubiquitous learning (U-learning) [17]. Personalized learning focuses on the learners
in order to adapt to their own requirements and interests while also giving them more control over their own learning.
Personalizing learning materials to learner's learning style enhances motivation and improves learning outcomes. The
personalized learning process takes into account the learners’ historical and current data on activities and performances then
accordingly automate system plan and deliver learning contents. Thus, each learner has his/her own plans depending upon the
circumstances, performances, scores, and grasp over the concepts. Adaptive learning systems provide tailored learning resources
and tools to assist learners attain mastery at their own speed and educators all over the world to promote fairness in their
classrooms are using it [18]. Personalized adaptive learning, unlike traditional learning, which makes it difficult for instructors to
determine each learner's comprehension of the content, offers instructors with specific information about each learner's progress
toward learning objectives on a continual basis.
In and educational settings AI is used as knowledge representation, automated inference engine, natural language processing,
computer vision, and robotics. AI and Big data supports personalized learning [16]. Rodzman et al. [19] developed a
personalized learning system (Intelligent Online Assessment and Revision) applying rule-based Machine learning methods to
improve the learners performance in Malaysia. They reported 60% improvement in the learners’ performance as a consequence
of using the designed system. AI in personalized learning is useful in rendering customized, flexible, engaging contents, and
predicting anxiety levels of learners. Holmes [20] developed a system based on AI and image processing techniques for
analyzing and classification of learners behaviors. Melesko and Kurilovas [21] presented an AI and semantic clustering based
learning analytics software agent for determining learning styles of learners. Figure 1 shows the simplified process of
personalized learning in an AI environment.
Ali Al-Badi et al. / Procedia Computer Science 201 (2022) 445–451
Author name / Procedia Computer Science 00 (2022) 000–000
447
3
Database
(Current and
Past record)
AI/Machine
learning models
Learner/student
Automated recommendation by the system
Recommendation by instructor
Inferences
Instructor/staff
Figure1. Simplified process of personalized learning in an AI environment
Birjali and Erritali [22] designed an adaptive e-learning model for learning and assessment based on Big data and map
reduced-genetic algorithm. Learning analytics include gathering and analyzing learners’ related data in order to better understand
and optimize the learning process [23]. Learning analytics can possibly exploit data to support learners by offering evidencebased knowledge and prediction of personal learning needs through the automated collecting of activity data from learners'
interactions with learning technologies [24, 25]. Adji and Hamda [26] studied the learners’ involvement in the online learning
activities. They concluded that learning analytics are helpful in identifying the number of learner accessing a particular topic,
number of times the access made, learners’ participation in the discussion forum, and how frequently learner and instructor
communicated. Pérez-Ortiz et al [27] provided a human centred AI based platform for engaged teaching and learning. It gives
educators a powerful tool for repurposing, revising, remixing, and redistributing open courseware created by others. It also has a
highly personalized recommendation engine that may optimize learning paths and respond to the user's learning preferences, as
well as a scaffolder and informative interface for learners to select content to watch, read, make notes, and write evaluations. Lim
et al [23] studies the learners’ perceptions and responses towards personalized learning analytics based feedbacks in blended
learning environments. According to the findings of the study, overall, the learner had positive evaluations of their customized
feedback guided by learning analytics. They were motivated as well.
However, educational institutions have yet to completely appreciate the value of AI in the teaching and learning process [28,
29]. Instructors often limit their ability to motivate students for personalized learning [16]. The educational industry should use
AI into their content creation and delivery methods to succeed in a computing world and to meet the need of evolving
educational system. The research to understand the perception of learners and instructors on the use of AI in personalized
learning is scarce in Oman and other GCC countries. Thus, the study outcomes will help the practitioners and researchers in
understanding the existing problem and meet the expectations of forthcoming revolution in the education sector.
3. Research Method and Findings
A survey questionnaire was designed on five point (05) Likert scale (strongly agree =1 to strongly disagree = 5) to investigate
the perceptions and use of AI in personalized learning. The SPSS 26.0 software package was used to analyze the data of 91
collected samples. The normality of the data was checked applying Kolmogorov-Smirnov test [30]. The data was not normally
distributed therefore non-parametric tests such as Independent-Samples Mann-Whitney U Test [31] and Independent- KrushkalWallis Test [32] were selected. In the Independent-Samples Mann-Whitney U Test profession (learner and instructor) were taken
as grouping variables while “AI facilitates personalized learning” as a testing variable.
The sample consisted of 55% female and 45% male. In the sample of 91 there were 20% instructors (Mean = 1.56, SD = .511,
Skewness = -.244, Kurtosis = -2.199) and 80% learners (Mean = 2.19, SD = 1.06, Skewness = .675, Kurtosis = .119). The
descriptive statistics of dependent variable (AI facilitates personalized learning) were, Mean = 2.07, SD = 1.009, Skewness =
.861, and Kurtosis = .578. The majority of the respondents were from Muscat (63%), followed by Al-Batinah North (14%), AlBatinah South (13%), Al- Dakiliyah (6%), Al-Sharqiya North (3%), and Al-Dhahira (1%). Independent-Samples Mann-Whitney
U Test revealed statistically significant differences (U = 882, p = .018) in learners’ and instructors’ responses. The effect size
was calculated as:
Effect size = Z / N1/2
……………………………… (1)
Where Z = Standardized Test Statistic, N = Sample size
Z / N1/2 = .247
….…………………………… (2)
Ali Al-Badi et al. / Procedia Computer Science 201 (2022) 445–451
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Author name / Procedia Computer Science 00 (2022) 000–000
Where Z = 2.359, N = 91
The .247 (Cohen’s classification, 0.1 (small effect), 0.3 (moderate effect), 0.5 (large effect)) [33] value shows moderate effect.
Further, Independent-Samples Mann-Whitney U Test revealed a no statistically significant difference (U = 1105, p = .502) in
male and female responses. Table 1 shows the Independent-Samples Mann-Whitney U Test summary of profession and gender.
Table 1. Independent-Samples Mann-Whitney U Test summary of profession and gender
Tests
Total N
Mann-Whitney U
Wilcoxon W
Test Statistic
Standard Error
Standardized Test Statistic
Asymptotic Sig.(2-sided test)
Profession
Gender
91
882.000
3583.000
882.000
95.371
2.359
.018
91
1105.000
1966.000
1105.000
119.124
.672
.502
The grouping/independent variables (learners/instructors, male/female) were tested against the independent variable. Finally,
mean rank graph was plotted for each of them. Figure 2 shows Independent-Samples Mann-Whitney U Test grouping and testing
variables with mean ranks.
Figure 2. Independent-Samples Mann-Whitney U Test grouping and testing variables with mean ranks
Additionally, another grouping variable Internet speed was checked across dependent variable (AI facilitates personalized
learning). The descriptive statistics of Internet speed were, Mean = 2.10, SD = 1.044, Skewness = -.381, and Kurtosis = -.221.
Ten percent (10%) respondent reported very fast Internet accessibility, 13% fast, 41% moderate, 30% slow, and 7% very slow.
The test showed that different levels of Internet speed do not affect perception towards AI in personalized learning H(4) = 7.668,
p = .105 (p <.05). Krushkal-Wallis Test revealed that there was no statistically significant difference between the mean ranks of
various levels in Internet speed. Figure 3 shows Independent- Krushkal-Wallis Test of grouping and testing variables.
Ali Al-Badi et al. / Procedia Computer Science 201 (2022) 445–451
Author name / Procedia Computer Science 00 (2022) 000–000
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Figure 3. Independent- Krushkal-Wallis Test grouping and testing variables.
4. Discussion
The learners’ attributes or the learning process affects the success of learning. The mean ranks of “AI facilitates personalized
learning” were compared to different groups of profession (learner and instructor). Independent-Samples Mann-Whitney U Test
revealed statistically significant difference (U = 882, p = .018) in learners and instructors perceptions while no significant
difference (U = 1105, p = .502) were reported between male and female perceptions. The effect size in case of profession was
very small (.247), which means the differences in their perception is very small. They do not differ much in their feelings
towards use of AI in personalized learning in Oman. Thus if personalized learning is implemented in the educational institution,
it is more likely to have successful adoption and positive outcomes.
Although, the mean rank distribution of “AI facilitates personalized learning” is slightly different across categories of
profession while it is same across gender. This reflects positive perceptions and importance of AI in the personalized learning,
which the learners and instructor felt. Independent-Samples Kruskal-Wallis Test examined the differences in the mean ranks of
“AI facilitates personalized learning” across levels/categories of Internet speed. The test revealed that there was a no significant
difference between the mean ranks across various levels in the Internet speed H (4) = 7.668, p = .105 (p <.05), which validates
that the Internet infrastructure is not a constraint in personalized learning in Oman. In the survey, the majority of respondent
reported moderate Internet speed.
The learners are heterogeneous and have their own preferred learning paths, which is the foundation of personalized learning.
It is possible to bring personalization in Learning Management Systems and tutoring systems by learning analytics, self-reporting
questionnaires, formative testing or data mining [34]. The technology is very important in personalized learning [35].
Personalized virtual learning settings include enriched digital materials, tutoring software, and collaborating services, all of
which add interactivity to the learning process and provide a variety of ways to adjust to the learner's needs and the context in
which they are studying. The recent studies have demonstrate the role of emerging technologies including AI, machine learning,
augmented/virtual reality in personalized learning [13]. In education, the usage of personalized learning and adaptable learning
has grown, however creating such learning environments has its own set of challenges, including costs [36]. Adaptive learning
makes use of technology and data about learners’ performance to adapt and respond to content and techniques that help learners
achieve a specific learning goal. The built-in algorithms analyze each learner's assessment, interaction, and learning behavior
data to deliver content, activities, feedback, and continuous assessment tailored to the learner's current situation. The
implementation of technology in learning such as massive open online courses (MOOCs) and storage of data in the form of Big
data have opened the opportunity for understanding the learners’ educational behaviors, however the main issue of quality
interpretation has remained intact which require attention [37].
In the current study, the small differences in the feelings of learners and instructors towards AI in personalized learning may
be attributed to the resistance to change, technological incompetency, social and psychological fears. AI modelling and
prediction help in judging the learners preferences, aptitude, career paths, recommendation, and learning plans, although the area
has not been fully explored yet. Personalization based on personality may influence learners' acceptance and use of an intelligent
learning environment, but it does not always influence their learning outcomes, necessitating a combination of personality and
other personal characteristics [38]. The fundamental difficulties for learning analytics to have a long-term influence on learning
and teaching – such as data constraints and a lack of a data-informed decision-making culture, which may inhibit its effective use
for personalized learning [39]. In spite of the fact that AI is taking space in education, however in the end the AI in education is
not replacing the traditional instructors, instead it is making the teaching and learning process more engaging. The existing
Ali Al-Badi et al. / Procedia Computer Science 201 (2022) 445–451
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Author name / Procedia Computer Science 00 (2022) 000–000
learning systems should have personalized (course content), engagement metrics, and integrated learning theories (sociocognitive learning theory, goalsetting (mastery) and personality theory) as well [40].
5. Conclusion
This research has provided insight into profession category (learners and instructors) and gender category (male and female)
perceptions towards AI in personalized learning in Oman. There is statistically significant difference in case of learners and
instructors’ (p < .05) perceptions. However, in the case of profession, the effect size was quite small (.247), indicating that the
variations in their perceptions are very small. In Oman, the learners and instructors have similar positive attitude towards the
implementation of AI in personalized learning. As a result, if Higher Education Institution plans to implement AI in personalized
learning, there are favourable chances of its success. Additionally, no statistically significant difference were observed between
male and female perceptions towards AI in personalized learning. Furthermore, additionally, Independent-Samples KruskalWallis Test found no differences in the mean ranks of AI in personalized learning across levels/categories of Internet speed H(4)
= 7.668, p = .105 (p < .05).
The results of this study contribute to guiding the design and implementation of personalized learning practices by offering
the means to achieve personalized learning, the choice of technology, and the success criteria. The educational administrator and
policy makers should look into the possibilities of personalized learning at Higher Education Institutions. AI and Machine
learning have the potential to bring this change. The personalized learning will help the learners to strengthen their weak areas
and build command over the subjects and courses. This will create a workforce with a higher level of expertise for specific
industries.
Furthermore, the study findings aid in identifying research directions in personalized learning that deserve future exploration.
More research on the use of various technologies and tools to promote personalized learning in various learning and teaching
contexts, such as course disciplines and learning environments, is required. Future research should also look into other aspects
such as accessibility of software and hardware tools, implementation cost, and availability of human resources that may influence
uptake of personalized learning at Higher Education Institutions. Future research in these areas will aid in realizing the potential
of personalized learning in a variety of educational settings.
Acknowledgment
The research leading to these results has received funding from the Ministry of Higher Education, Research & Innovation
(MOHERI) of the Sultanate of Oman, under the Block Funding Program (Block Funding Agreement No:
TRC/BFP/GULF/01/2018, Project Code: BFP/RGP/ICT/18/184).
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