General View about an Artificial Intelligence Technology in

Education Domain

Ghaliya Alfarsi

1,2

, Ragad M. Tawafak

, Abdalla ElDow

, Sohail Iqbal Malik

, Jasiya Jabbar

Abir Al Sideiri

, Roy Mathew

College of Graduate Studies, Universiti Tenaga Nasional, Information Technology department, Malaysia

AlBuraimi University College Buraimi, Oman

Keywords: Game Artificial Intelligence; Education Domain; Higher Education Institutions.

Abstract: This paper discusses the topic of artificial intelligence creation and usage of higher education and research. AI

explores how technologies to train, teach and build new pieces of knowledge from continuous development.

This study illustrates the need to predict the incorporates artificial intelligence in the higher educational

institutions, recent technical advances, and the increased rate of higher education adoption of AI technology

addressed. The method used through the reviewing of existing studies and the type of AI proposed in each

class, what kind of challenges faced. The study came outs with highlights the AI advantages with machine

learning, technologies of educational sector, and enhancing the communication and security of data

transformation among students. The result reveals how can be introducing these teaching, learning, student

support, and management technologies with AI development. This study concludes to identify the challenges

of applying AI in higher education institutions and student learning and discuss more directions for research.

1 INTRODUCTION

The use of the technology of AI in education has a long

tradition history (Abdullahi, 2011). Regarding the

digital technology revolutions, were introduced in the

classroom as a pivotal point to convert the education

with AI from archives digital information records

(Noble, 2017). It began in the 1980s as desk-top

computers implemented into school. All school

capillaries have invaded through digitalization.

Through global networks, integrated machines,

innumerable teaching, and material approaches and

automated student surveillance and other

administration processes have become available for the

student via the internet. This new stage of technology

use needs a lot of controlling applications, also, need

to AI engine to cluster them, organize and be on the

right classification and more other resources to be

existence for HEI (Tawafak, Abir, Ghaliya, Maryam,

Sohail, and Jasiya, 2019).

The amount of data produced by digital devices and

their processing capacity has expanded exponentially

in tandem with the growth of digital technologies and

their improved accessibility (AlFarsi, & ALSinani,

2017; Malik, Al-Emran, Mathew, Tawafak, and

AlFarsi, 2020). It is increasingly contributing to the

introduction of smart systems capable of

understanding trends in large volumes of data and

increasingly capable of imitating human behaviour,

especially human reasoning. Therefore, such programs

may execute tasks individually or assist users in

carrying out assignments.

The impact of AI from a socio-technical

perspective on education explored in this study. First

of all, we review the essential technologies that enable

AI, particularly machine learning. This section

describes the features of schooling AI systems. The

ethical impact of such AI programs is assessed

(ALFarsi, Jabbar,., & ALSinani, 2018).

One of AI advantages: Artificial intelligence (AI),

deep learning, machine learning, and neural networks

represent an incredibly exciting and powerful machine

learning-based techniques used to solve many real-

world problems.

Artificial Intelligence is adopted by several higher

education institutions which improve the learning

development of students by increasing the interests and

motivations of students towards learning goals as per

their interests and field of study (Hurtado, Milem,

Clayton-Pedersen, and Allen, 1999).

120

Alfarsi, G., Tawafak, R., ElDow, A., Malik, S., Jabbar, J., Sideiri, A. and Mathew, R.

General View about an Artiﬁcial Intelligence Technology in Education Domain.

DOI: 10.5220/0010304500003051

In Proceedings of the International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies (CESIT 2020), pages 120-127

ISBN: 978-989-758-501-2

On the other hand, some disadvantages also there.

Along with the benefits of Artificial Intelligence and

its application in students learning some limitations

can be faced by students in this technological mode of

learning (Eldow et al., 2006; Mathew et al., 2019;

Terekhov, 2017). One of the significant limitations of

Artificial Intelligence in students learning that only

manipulates the results from a given set of data, unlike

teacher instructions, it only shows the instructions as

per limited datasets. As teachers can become more

experienced as per the number of years in their field,

but AI-based instructions remain the same according to

their algorithms. Some researches emphasize on

technology addiction in students as they become

addicted to getting the solution on fingertips via

algorithms and AI applications. Hence, they become

dump in self problem-solving matters, in other words

so much use of technology can result as a shrink of

abilities and students will not use the machine as it is

convenient they will use it because they can't do it in

another way (Hayles, 2012). If students cannot

understand the solution of the problem via one method,

there are no alternative methods to make them know

the situation in Artificial Intelligence applications. Due

to the above-stated limitations, it is unclear yet that

weather it is feasible for students to learn via

technology without the presence of a teacher or not.

Figure 1: Artificial Intelligence in General.

2 WHY IS ARTIFICIAL

INTELLIGENCE IMPORTANT?

Artificial Intelligence (AI) is a large-scale computer

science industry that builds intelligent machines

capable of accomplishing tasks that are typically

human intelligence as shown in Figure 2. AI is an

interdisciplinary approach, but machine education and

profound learning developments cause a paradigm

shift across almost every field of the software industry

(Cioffi, Travaglioni, Piscitelli, Petrillo, and De Felice,

2020).

Generally feasible from a societal point of view.

Furthermore, we observe that teachers are

experiencing an increased workload. AI-powered

learning applications could thus not only be beneficial

for students but can also increase the productivity of

teachers (Tawafak et al., 2019; Armstrong, 2009).

The ability of a controlled person or intelligent

person is to perform functions that usually associated

with the intellectual (Clustering, 2019). Artificial

Intelligence (AI) This concept is also used for the

creation of structures of human mental mechanisms

such as the capacity to comprehend, explore meaning,

generalize, or benefit from previous experiences. Since

the optical computer's invention in the 1940s, it has

seen that machines can configure to conduct very

complicated tasks – Proof for mathematical theorems

or chess – with considerable skill – may be found

(Sangalli, 2018). However, the computer processing

speed and memory power are continually improving;

no systems are yet accessible that can equal human

dexterity across broader realms or activities involving

a great deal of everyday expertise. In the other side,

specific algorithms have reached the output standards

of human experts and specialists in conducting such

particular functions, such that in this narrow context.

In areas such as medical treatment, computer search

engines and recognition of language and handwriting,

artificial intelligence used (Tatnall, 2019; Laura R.

Winer, 2002).

Figure 2: AI in Education Industry.

From the past few decades, the involvement of

artificial intelligence in students learning process is

grown speedily and the future of students' learning

virtually connected with these technologies. Presently,

artificial intelligence is increasing the tools for student

learning every day, i.e. spell checkers, text prediction

General View about an Artiﬁcial Intelligence Technology in Education Domain

121

applications, language translators, audio to text, and

text to audio. Artificial Intelligence comprised of

computing systems that can perform human-like tasks

such as correction, learning, extracting, evaluating, and

self-adapting (Tatnall, 2019; Clustering, 2019).

Artificial Intelligence is a way of making a

computer, a computer-controlled robot, or a software

think intelligently, in a similar manner the intelligent

humans think. AI is accomplished by studying how

human brain thinks, and how humans learn, decide,

and work while trying to solve a problem, and then

using the outcomes of this study as a basis, of

developing intelligent software and systems

(Thiraviyam, 2018; Geetha, and Bhanu, 2018). Figure

2 shows the impact of AI in the education sector to

behave as a human style of thinking and solving

problems.

Figure 3: Environment with AI in education

Figure 3 shows the three stages of technology

enhanced learning environment phases. First phase,

started with three factors as institutional, pedagogy and

technology that full influenced to each other. Second

phase, points to the activity of implementation and

evaluation the AI impact moving from design to use

process by all students and teachers. Third phase,

combined among three factors to assess the

institutional perspective, pedagogical perspective, and

technological perspective. The purpose of writing this

study, since its very outset, artificial intelligence

suffered from many difficulties within growing stages

in education as a system, application or service and still

faced the problem in the whole world, because of not

regularly reluctant to technological changes in the

traditional learning or work in the organization (Aoun,

2017; Adizes, 2004). AI continuously used as a part of

the organization's vision, also, is used as a promise to

increase the usability and applications to include the

educational sector. Furthermore, AI used to transform

the educational capabilities to improve personal use.

This promise is starting to unfold as present

technology has begun experimenting with different

models worldwide, bringing many questions to the

field of education.

3 OBJECTIVE

The main objective of this study is to give a general

view of AI applications used to enhance the education

domain. For example, finding a solution to complex

problems required a lot of time if students try to solve

them without using any tool or technology. The

situation just like nowadays due to coronavirus Covid-

19 where along with all aspects of everyday life

educational institutions are also closed so in this case

students need to learn and attend their classes via the

use of technology, i.e. virtual classrooms. Therefore,

advancement in technology considers the learning

perspectives of students to provide them with a way to

learn and conduct research fast and reliable using state

of the art technologies. Universities and higher

education departments are required to keep an eye on

technologies and expert systems nowadays to provide

and quality education with the help of the latest tool

and technologies to enhance the learning outcomes of

their students.

4 ARTIFICIAL INTELLIGENCE

(AI)

The analyzes are focused mainly on (scientific)

research about the AI context, technological

possibilities. Firstly, collected as many materials as

possible were collected around AI applications in

education, after which the information was structured

and framed(Terekhov, 2017; Alzahrani, 2011).

A long time before in 1956 a famous researcher

John McCarthy defines artificial intelligence as "The

machine will simulate the every learning aspect, and

that will be the base of proceedings towards artificial

intelligence study" (Russell, 2010). An example of

usage of AI in learning is complicated algorithms set

powering Apple iPhone Siri (Bostrom, 2011). After

studying the different pieces of literature regarding the

use of artificial intelligence in students learning. We

can define this concept as artificial intelligence

comprised of computing systems that can perform

human-like tasks such as correction, learning,

extracting, evaluating, and self-adapting.

The progression of Artificial Intelligence in

students' learning is much accelerated nowadays. Most

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122

of the higher education department is adopting this

technology advancement to provide their students state

of the art learning environment. Few examples of this

are:

 A supercomputer Watson by IBM is providing

assistance to the students of Deakin University,

Australia regarding predictable and repetitive

tasks of statistics, based on complex algorithms

and methods (Deakin, 2014).

 Many universities are using plagiarism

detection software "Turnitin" is also an example

of artificial intelligence and machine learning

which can help to avoid plagiarism and making

students capable of working at their own hence,

increasing the learning process of students

(Graham-Matheson, 2013; Turner).

 A dedicated search engine provided by Google's

name as "Google Scholar" is providing

advanced topics and papers to students

precisely as per their field of research. Hence,

this Artificial Intelligence field is increasing the

student's knowledge in their respective areas by

exploring research articles within friction of

seconds (Noruzi, 2005; Alfarsi et al., 2020).

 Many universities are adopting the Artificial

Intelligence-based program, Virtual Mentor for

a quick assessment, and instant feedback about

student progress and interest and providing

assistance are per result to enhance the learning

capabilities (Keengwe, 2012).

 Alexa, a voice assistance platform is using by

Arizona State University, which is capable of

answering the questions related to student's

schedules. So, by this student remain to engage

in study processes interestingly (Cooper, and

Garner, 2012; Tawafak et al., 2018).

5 LITERATURE REVIEW

This section will discuss some selected and related

studies with the topic of this study of general types od

AI and its impacts on the educational sector.

AL Farsi et al., (2017) proposed an academic

artificial intelligence, "A rule-based system for

advising undergraduate students" it is a prototype

student advising expert system. The system includes an

easy user interface to use by the students it created by

clips program language that used in an expert system.

The system's main objective is to get an easy model

with the high expert method used in artificial

intelligence algorithms. The technique works through

an application applied with IT BUC "Al-Buraimi

University College" student (Alfarsi et al., 2019). The

system includes the information required for student

registration and login successfully to the portal system

of the university in Oman. Many categories and

comparisons used to identify the selected course for

each student based on their degree and department

(Alfarsi et al., 2020).

Daptio is an organization created in 2013 as a South

African solution. This organization used AI and deep

analytics to provide in-depth learning, understanding,

enhancement of the learning process for all students.

Also, it offers personal education and specialist for

both teachers and students. This service gives through

the use of the internet and online connections. This

study from the organization used to understand the

proficiency level of each student and then match the

relevant content.

The smart classroom is an Artificial Intelligence-

based project which is adopted by McGill University,

and the primary outcomes of this project include

automatically audio capturing. Besides, video and

lecture are capturing, slides and presentations

capturing during live classes, and provide them access

to students online (Laura R. Winer, 2002).

(Tawafak et al., 2019; Tawafak, Romli, Arshah,

2018) proposed a model called UCOM "University

Communication Model", used as a type of expert

service system, especially for e-learning advising

learners. The main objective was to enhance student

learning and their feedback on the whole e-learning

process. The analysis selected the applications where

the critical function and the primary purpose is to get

student satisfaction on assessment services. This model

used a method based on distributing surveys related to

all vital factors used in education and reflected on

student understanding, learning, and performance. The

model shows significant positive influence s among

most of the relationships between elements. However,

this model worked as an e-learning system more than

working as an AI application. The model results

worked as an application to motivate the students. At

the same time, the other models used basically to apply

the AI technology application.

Academic research in the field of AI is not

necessarily fundamental. Practical knowledge also

gained by experimenting with various applications of

AI in education. We also see that schools and teachers

have started to experiment with AI themselves. IT

teachers who have experience with AI apply this to

simplify their work. Especially teachers with a

business background that have gained experience with

machine learning can use these skills to education.

Another study from China, also, developed a new

generation of AI applications, plans, and uses, which

was produced in 2017 and going next year. This study

General View about an Artiﬁcial Intelligence Technology in Education Domain

123

tries to set out the vision for China to become the new

Centre in AI innovation by 2030. The study objective

based on education to play a massive part in this

innovation. The method starts by the advertising of

Master scholarships to improve and develop the use of

AI in the educational system and use, also in the daily

life use as the internet of things that completely use AI

to get professional work and satisfaction of service

(Lu, Li, Chen, Kim, and Serikawa, 2018).

Generally speaking, AI in education can be used to

transfer knowledge and skills, to assess knowledge and

skills, to inform instructors of pupils' progress and

achievements. (Devedžić, V., 2004) determined in his

thesis that AI is not a dream; it can use efficiently to

improve learner achievements based on acquiring

knowledge. The AI outcomes of applications affect the

next generation related to the system services and

activities during the learning process as shown in

Figure 4.

Figure 4: Setting of AI with Web-based Education.

At the end of all the above-explained studies, these

models experimented with exceptional use of AI

applications. For this reason, this study tries to

highlight the continuous need to enhance more and

more AI applications, especially in the education

domain. However, most of these systems were result in

positive acceptance and essential feedback from the

student's side to improve their performance.

6 METHODOLOGY

This study wants to encourage the interest of

developing AI applications, especially after the

COVID-19 pandemic (Ahmed, Allaf, and Elghazaly,

2020; Viner et al., 2020). Where the whole world of

learning strategies moved and changed to use online

learning. The method used in this study is to search

with keyword of articles used AI in education. Also,

the method shows the analyzed papers highlights the

advantages of AI in education. The main method tries

to focus on related topics with keywords like "AI in

education", "AI. Application with education" and "AI

Technology advantages". Most of the analyzed studies

used survey to collect data and evaluate the feedback

of technology acceptance and use by undergraduate

students. This type of education recommended using

more of artificial intelligence applications designed for

every kind of specification field.

7 PROGRAM CODE

The following segment program shows the

programming code of activating student learning

outcomes through an active assessment model.

Figure 5: Programming code of activating student learning

outcomes.

8 DISCUSSIONS

AI applications generally implement sophisticated

statistical models. Moreover, a forecast is almost ever

100 per cent correct (which would not, of course, mean

a human being should perform better). Sometimes

there is a trade-off between model precision and model

interpretability. It is harder to understand, but much

more efficient, more complex AI-algorithms,

especially the recently available profound learning

algorithms because the need for interpretative models

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is growing because of the increasing use of machine

learning to simplify human functionalities. Questions

what is a suitable model in, what is a flawed model in,

and how does a prediction come? If a model used in

practice, the reply should give.

Game-based helped develop ways of testing

complex models of machine learning. It illustrates the

features of the configuration in an option and how also

with the most complex ones. Throughout the field of

evolutionary neural networks, substantial progress has

made to provide an insight into these models. The

function visualization can make clear when individual

layers of a neural network are triggered (Olay et al.,

2017). The studies show that the earlier layers of the

system contain basic geometric shapes, including

corners and edges. We see more complex

combinations of these geometric patterns as we

examine the network further. To create more complex

features, each layer of the system combines the designs

from the previous layers. Such observations can use to

assess what the network pays attention to while making

a forecast.

9 CONCLUSION

In the end, this study tried to produce a general view of

artificial intelligence use in the education domain. The

study attempts to highlight some of the advantages and

disadvantages of keep using AI applications. Besides,

the course illustrates the literature review of many

tasks related to the education sector. The method of the

works used the search for similar studies related to AI

usage in education sector and its advantages of use.

The discussion shows an improvement of student's

acceptance, and it motivates them to increase their use

of AI in their continuous learning process. However,

with the new world case of triable coronavirus

(COVID-19) pandemic spreading out, life needs more

and more deep use of AI. The education domain was

one of these sectors affected by this situation. The

recommendation of this study is to keep the increase of

using AI for the benefits of technology development,

student's confidence, enhancing academic

performance, etc.

REFERENCES

Abdullahi, A. A., 2011. Trends and challenges of traditional

medicine in Africa. African journal of traditional,

complementary and alternative medicines, 8(5S).

Alfarsi, G., Jabbar, J., Tawafak, R.M., Malik, S.I., Alsidiri,

A. and Alsinani, M., Using Cisco Packet Tracer to

simulate Smart Home. International Journal of

Engineering Research & Technology (IJERT), Vol. 8

Issue 12, December-2019

Ahmed, H., Allaf, M. and Elghazaly, H., 2020. COVID-19

and medical education. The Lancet Infectious Diseases.

ALFarsi, G. and ALSinani, M., 2017. Developing a mobile

notification system for al Buraimi University College

students. International Journal of Information

Technology, 1(1), pp.10-16.

Alzahrani, A.I., 2011. Web-based e-Government services

acceptance for G2C: A structural equation modelling

approach.

ALFarsi, G.M., Jabbar, J. and ALSinani, M., 2018.

Implementing a Mobile Application News Tool for

Disseminating Messages and Events of AlBuraimi

University College. International Journal of Interactive

Mobile Technologies (iJIM), 12(7), pp.129-138.

ALFarsi, G. and ALSinani, M., 2017. Developing a mobile

notification system for al Buraimi University College

students. International Journal of Information

Technology, 1(1), pp.10-16.

Aoun, JE, 2017. Robot-proof: higher education in the age of

artificial intelligence. MIT press.

ALFARSI, GMS, OMAR, KAM and Alsinani, M.J., 2017.

A rule-based system for advising undergraduate

students. Journal of Theoretical & Applied Information

Technology, 95(11).

Adizes, I., 2004. Managing corporate lifecycles. The adizes

institute publishing.

Alfarsi, G., Jabbar, J., Tawafak, R.M., Alsidiri, A. and

Alsinani, M., 2019, December. Techniques For Face

Verification: Literature Review. In 2019 International

Arab Conference on Information Technology

(ACIT) (pp. 107-112). IEEE.

Armstrong, T., 2009. Multiple intelligences in the

classroom. Ascd.

Al Farsi, G., Jabbar, J., Tawafak, R.M., Malik, S.I., Alsidiri,

A. and Alsinani, M., 2020. Mobile Application System

Supported BUC Students Services and

Learning. International Journal of Interactive Mobile

Technologies, 14(9).

Bostrom, N. and Cirkovic, M.M. eds., 2011. Global

catastrophic risks. Oxford University Press.

Brown, V.A., Harris, J.A. and Russell, J.Y. eds.,

2010. Tackling wicked problems through the

transdisciplinary imagination. Earthscan.

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A. and De

Felice, F., 2020. Artificial intelligence and machine

learning applications in smart production: Progress,

trends, and directions. Sustainability, 12(2), p.492.

Clustering, AS, 2019 International Arab Conference on

Information Technology (ACIT). Higher Education,

165, p.170.

Cooper, N. and Garner, B.K., 2012. Developing a learning

classroom: Moving beyond management through

relationships, relevance, and rigor. Corwin Press.

Deakin, H. and Wakefield, K., 2014. Skype interviewing:

Reflections of two PhD researchers. Qualitative

research, 14(5), pp.603-616.

General View about an Artiﬁcial Intelligence Technology in Education Domain

125

Devedžić, V., 2004. Web intelligence and artificial

intelligence in education. Journal of Educational

Technology & Society, 7(4), pp.29-39.

Eldow, A., Shakir, M., Talab, M.A., Muttar, A.K. and

TAWAFAK, RM, 2006. LITERATURE REVIEW OF

AUTHENTICATION LAYER FOR PUBLIC CLOUD

COMPUTING: A META-ANALYSIS.

Geetha, R. and Bhanu, SRD, 2018. Recruitment through

artificial intelligence: a conceptual study. International

Journal of Mechanical Engineering and Technology,

9(7), pp.63-70.

Graham-Matheson, L. and Starr, S., 2013. Is it cheating–or

learning the craft of writing? Using Turnitin to help

students avoid plagiarism. Research in Learning

Technology, 21.

Hayles, N.K., 2012. How we think: Digital media and

contemporary technogenesis. University of Chicago

Press.

Hurtado, S., Milem, J., Clayton-Pedersen, A. and Allen, W.,

1999. Enacting Diverse Learning Environments:

Improving the Climate for Racial/Ethnic Diversity in

Higher Education. ASHE-ERIC Higher Education

Report, Vol. 26, No. 8. ERIC Clearinghouse on Higher

Education, One Dupont Circle, NW, Suite 630,

Washington, DC 20036-1181.

Jabbar, J., Malik, S.I., AlFarsi, G. and Tawafak, R.M., The

Impact of WhatsApp on Employees in Higher

Education. In Recent Advances in Intelligent Systems

and Smart Applications (pp. 639-651). Springer, Cham.

Keengwe, J., Schnellert, G. and Mills, C., 2012. Laptop

initiative: Impact on instructional technology

integration and student learning. Education and

Information Technologies, 17(2), pp.137-146.

Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018.

Brain intelligence: go beyond artificial

intelligence. Mobile Networks and Applications, 23(2),

pp.368-375.

Mathew, R., Malik, S.I. and Tawafak, R.M., 2019. Teaching

Problem Solving Skills using an Educational Game in a

Computer Programming Course. Informatics in

education, 18(2), pp.359-373.

Malik, S.I., Mathew, R., Tawafak, R.M. and Khan, I.,

GENDER DIFFERENCE IN PERCEIVING

ALGORITHMIC THINKING IN AN

INTRODUCTORY PROGRAMMING COURSE.

Proceedings of EDULEARN19 Conference 1st-3rd July

2019, Palma, Mallorca, Spain, p.8246-8254.

Malik, S., Al-Emran, M., Mathew, R., Tawafak, R. and

AlFarsi, G., 2020. Comparison of E-Learning, M-

Learning and Game-based Learning in Programming

Education–A Gendered Analysis. International Journal

of Emerging Technologies in Learning (iJET), 15(15),

pp.133-146.

Noble, D. D. (2017). The classroom arsenal: Military

research, information technology and public education.

Routledge.

Noruzi, A., 2005. Google Scholar: The new generation of

citation indexes. Libri, 55(4), pp.170-180.

Sangalli, A., 2018. The importance of being fuzzy: and other

insights from the border between math and computers.

Princeton University Press.

Tawafak, R.M., Romli, A.M. and Alsinani, M.J., 2019.

Student assessment feedback effectiveness model for

enhancing teaching method and developing academic

performance. International Journal of Information and

Communication Technology Education

(IJICTE), 15(3), pp.75-88.

Tatnall, A., 2020. Editorial for EAIT issue 2,

2020. Education and Information Technologies, 25(2),

pp.647-657.

Tawafak, R.M., Romli, A.B. and Alsinani, M., 2019. E-

learning system of UCOM for improving student

assessment feedback in Oman higher

education. Education and Information

Technologies, 24(2), pp.1311-1335.

The Economist Intelligence Unit (2018). The Automation

Readiness Index: Who Is Ready for the Coming Wave

of Automation? Available at:

https://www.automationreadiness.eiu.com/

Tawafak, R.M., AlSideir, A., Alfarsi, G., Al-Nuaimi, M.N.,

Malik, S.I. and Jabbar, J., 2019. E-learning Vs.

Traditional Learning for Learners Satisfaction. E-

learning, 29(3), pp.388-397.

Terekhov, A.I., 2017. Bibliometric spectroscopy of Russia’s

nanotechnology: 2000–2014. Scientometrics, 110(3),

pp.1217-1242.

Tawafak, Ragad M., Awanis BT Romli, Ruzaini bin

Abdullah Arshah, and Sohail Iqbal Malik. "Framework

design of university communication model (UCOM) to

enhance continuous intentions in teaching and e-

learning process." Education and Information

Technologies 25, no. 2 (2020): 817-843.

Teece, DJ, 2018. Profiting from innovation in the digital

economy: Enabling technologies, standards, and

licensing models in the wireless world. Research Policy,

47(8), pp.1367-1387.

Tawafak, R.M., Romli, A.B. and Arshah, R.B.A., 2018.

Continued intention to use UCOM: Four factors for

integrating with a technology acceptance model to

moderate the satisfaction of learning. IEEE Access, 6,

pp.66481-66498.

Tawafak, R.M., Romli, A. and Arshah, R.A., 2019, August.

E-learning prospect on improving academic

performance in Omani Universities. In IOP Conference

Series: Materials Science and Engineering (Vol. 551,

No. 1, p. 012033). IOP Publishing.

Tawafak, R.M., Romli, A., Malik, S.I., Shakir, M. and

Alfarsi, G.M., 2019. A systematic review of

personalized learning: Comparison between E-learning

and learning by coursework program in

Oman. International Journal of Emerging Technologies

in Learning (iJET), 14(09), pp.93-104.

Turner, J., A Deep Learning Dive: Objectives for Educating

in a Digital Age.

Tawafak, R.M., Mohammed, M.N., Arshah, R.B.A., Shakir,

M. and Mezhuyev, V., 2018. Technology enhancement

learning reflection on improving students’ satisfaction

CESIT 2020 - International Conference on Culture Heritage, Education, Sustainable Tourism, and Innovation Technologies

126

in Omani universities. Advanced Science

Letters, 24(10), pp.7751-7757.

Thiraviyam, T., 2018. Artificial intelligence marketing.

Tawafak, R.M., Romli, A.B., bin Abdullah Arshah, R. and

Almaroof, R.A.S., 2018. Assessing the impact of

technology learning and assessment method on

academic performance. EURASIA Journal of

Mathematics, Science and Technology

Education, 14(6), pp.2241-2254.

Tawafak, R.M., Mohammed, M.N., Arshah, R.B.A. and

Romli, A., 2018, February. Review on the effect of

student learning outcome and teaching Technology in

Omani's higher education Institution's academic

accreditation process. In Proceedings of the 2018 7th

International Conference on Software and Computer

Applications (pp. 243-247).

Tawafak, R.M., Buraimi, O., Jabbar, J., Alfarsi, G., Malik,

S.I., Romli, A. and Alsideiri, A., A Review Paper on

Student-Graduate Advisory Expert system.

Tatnall, A., 2019. Editorial for EAIT issue 2,

2019. Education and Information Technologies, 24(2),

pp.953-962.

Tawafak, R., Romli, A., Malik, S. and Shakir, M., 2020. IT

Governance Impact on Academic Performance

Development. International Journal of Emerging

Technologies in Learning (iJET), 15(18), pp.73-85.

Tawafak, R.M., Alfarsi, G., AlNuaimi, M.N., Eldow, A.,

Malik, S.I. and Shakir, M., 2020, April. Model of

Faculty Experience in E-Learning Student Satisfaction.

In 2020 International Conference on Computer Science

and Software Engineering (CSASE) (pp. 83-87). IEEE.

Tatnall, A., Correction to: Editorial for EAIT issue 2, 2020.

Tawafak, R.M., Romli, A.B., Abdullah, R., Alfarsi, G.,

Jabbar, J., Esbai, R., Sharaf, S., Khalifa, M., Ahmed, H.,

Qamber, I.S. and Abdelhameed, W., Competitiveness &

Quality.

Tawafak, R.M., Romli, A.B. and Arshah, R.B.A., 2019,

February. E-learning Model for Students’ Satisfaction in

Higher Education Universities. In 2019 International

Conference on Fourth Industrial Revolution

(ICFIR) (pp. 1-6). IEEE.

Tawafak, R.M., Malik, S.I. and Alfarsi, G., 2020.

Development of Framework from Adapted TAM with

MOOC Platform for Continuity

Intention. Development, 29(1), pp.1681-1691.

Tawafak, F.R.M., The Comprehension Theories Of

Continuous Intention To Use Tel With E-Learning

Models In Educations. TABLE OF CONTENT ID

TITLE AUTHOR PAGE NO., p.41. ICOEL 2019.

International Conference on E-Learning, 2019

Viner, R.M., Russell, S.J., Croker, H., Packer, J., Ward, J.,

Stansfield, C., Mytton, O., Bonell, C. and Booy, R.,

2020. School closure and management practices during

coronavirus outbreaks including COVID-19: a rapid

systematic review. The Lancet Child & Adolescent

Health.

Winer, L.R. and Cooperstock, J., 2002. The "intelligent

classroom": Changing teaching and learning with an

evolving technological environment. Computers &

education, 38(1-3), pp.253-266.

Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron

spectroscopy studies on magneto-optical media and

plastic substrate interface,” IEEE Transl. J. Magn.

Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th

Annual Conf. Magnetics Japan, p. 301, 1982].

General View about an Artiﬁcial Intelligence Technology in Education Domain

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