Index

Abstract

This study investigated the role of e-learning and artificial intelligence (AI) in improving the faculty members’ practices when switching to online education in the wake of the COVID-19 pandemic. The researcher employed a descriptive and analytical approach. A questionnaire was used to measure the impact of AI and e-learning on four different themes related to education in Saudi Arabia. The data from 406 respondents suggested a statistically significant (α≤ 0.05) effect of AI on e-learning. The faculty members strongly agreed that AI and e-learning improved relevant aims of teaching and related practices during the pandemic. Moreover, the influence on practices related to teaching methods during the pandemic was also highly positive. Further, the instructors strongly acknowledged that e-learning and AI ameliorated their teaching practices in general. Finally, the study depicted that e-learning and AI assisted instructors to automate grading and evaluation, ensures the confidentiality of data, and provides easier access to information. The average of the sample responses for all four fields analyzed in the study corresponded to the degree of approval. This study has a paramount importance due to the continuity of education in the context of the COVID-19 pandemic and the role of e-learning and artificial intelligence in education in general.

Keywords: AI in education, Artificial intelligence, Conventional education, COVID-19, e-learning, Pandemic

Received: 30 June 2021 / Revised: 23 September 2021 / Accepted: 25 October 2021 / Published: 11 November 2021

Contribution/ Originality

This study is one of very few studies which have investigated the efficiency of applying AI techniques in e-learning during COVID-19 pandemic. It also investigated the faculty members’ practices to improve teaching methods and quality during the pandemic. The findings could provide insights for universities’ leaders to improve e-learning process.

1. INTRODUCTION

In 2020, the COVID-19 virus spread as a pandemic worldwide and led to numerous changes in the education sector, which was severely affected. To reduce the spread of the pandemic, physical distancing measures were adopted (Crawford et al., 2020). The education in schools was substituted with e-learning at home. Several online learning websites offered free access to their services as part of demand. Online classrooms have been widely in-use since mid-February, 2020. Gradually, students have evolved to accept e-learning and are seeking to adapt to new circumstances (Chick et al., 2020).

A significant proportion of teachers is eager to switch to the online mode of learning globally. Online education is, however, not a new mode of delivery for universities. Most faculty are already trained to use online learning tools as an alternative way of teaching, though the sudden transition to online education raised concerns about the faculty's ability to adapt to new technologies. Working from home could be a difficult job for teachers, and their perspectives could change towards teaching methods. Many universities also did not have enough infrastructure or resources to promote online teaching immediately.

The transition from face-to-face to online teaching has a significant impact on students’ evaluation. Although technology has been commonly used to support teaching and learning, the assessment procedures are still underdeveloped (Timmis, Broadfoot, Sutherland, & Oldfield, 2016). Moreover, due to the current pandemic, the students and teachers do not know what steps should be taken regarding the management of unfinished tasks, projects, and other assignments (Raaheim et al., 2019). If the exams are taken online, it is not easy to monitor the students to ensure transparency. Through online means, training, practical exams, and performance tests also cannot be conducted. Furthermore, the students who do not have readily available internet facilities face a significant disadvantage when engaging in the online assessment processes that affect their average grade point (Alruwais, Wills, & Wald, 2018).

The advances in emerging e-learning technologies and techniques, such as artificial intelligence techniques (AI) have introduced new educational possibilities; nevertheless, it is essential to mention that education is a human-centric task and not a technological issue. Despite rapid developments in AI, it is a risky path to rely on technology. It is also essential to maintain focus on the notion that humans should identify glitches, troubleshoot problems, manage risks, and ask questions to enhance their understanding and creative thinking. The role of humans in education has also left the doors open to novelty in teaching.

Previous research has proposed that teachers can better understand online teaching through training programs (Lee, March, & Peters, 2015). Some of the studies identified a series of motivating and discouraging factors that can influence educators' enthusiasm to teach online (Shreaves, 2019). The views of the faculty on online education must be understood in order to address their concerns adequately. The acceptance of new technologies that will eventually help in developing learning management systems at universities also requires a faculty perspective (Farhan, Razmak, Demers, & Laflamme, 2019). In this backdrop, the current research sought to determine the reaction of faculty members in Saudi universities towards employing e-learning and taking advantage of artificial intelligence systems to improve the quality of education and raise the level of educational gains for students in light of the COVID-19 pandemic.

2. RESEARCH QUESTIONS

This study consisted of one central question and three sub-questions. The central question was, "what is the role of e-learning and AI techniques in improving the behavior and practices of faculty members when switching over from traditional education to e-learning in the light of the COVID-19 crisis?"
Based on the main question mentioned above, three sub-questions were explored:

3. RESEARCH HYPOTHESES

  The current study consists of one primary hypothesis and three sub-hypotheses. The main hypothesis was “There is no statistically significant effect at the α≥0.05 level of AI techniques on improving the behavior and practices of faculty members when switching from traditional education to e-learning in the light of COVID-19 crisis”.

The following sub-hypotheses were developed from the main hypothesis:

4. OBJECTIVES OF STUDY

The current study consisted of a main objective and three sub-objectives. The main objective was "to identify the role of AI techniques in improving the behavior and practices of faculty members when switching from traditional education to e-learning in light of the COVID-19 crisis."

From the main objective, three sub- objectives were stated:

5. LITERATURE REVIEW

5.1. Artificial Intelligence and Education

Artificial intelligence in education focuses on research, development, and evaluation of teaching and learning applications. There have been several long-term goals, such as comprehension and learning of nuanced student responses, discerning when and why a student's understanding has fallen apart, providing clues to helping students understand the study materials, and eventually simulating the conduct and guidance of a human tutor in this context. The researcher envisaged personalized tutors who adapt to each student's needs or teach student groups based on gender classification, level of achievement, lesson time, etc. Artificial intelligence techniques include interpreting complex student answers, getting to know learner’s performance, and understanding when/why the understanding of a student has fallen, and providing hints about the same. Approaching, learning, planning, language processing, and responding are the main objectives of artificial intelligence research (Woolf, 2015). The education sector can effectively employ AI. In this context, Xu (2019) evaluated the effect of AI on education.

Artificial intelligence research has culminated in advances and inventions in the recent past resulting in computers, software, and other devices demonstrating human-like intelligence distinguished by cognitive abilities, comprehension, adaptability, and decision-making capability. AI techniques has extensively been embraced and used in learning, particularly by teaching institutions, in various forms. Initially, AI took the shape of computer technology and then transitioned to internet advertisement platforms, advanced education systems, and finally embedded computer systems. Among other advancements, AI allowed humanoid robots and web-based robots to execute the roles and duties of teachers individually or with instructors. These tools have helped educators conduct various administrative tasks, such as efficiently reviewing and marking assignments and ensuring greater teaching material quality. Therefore, courses have been tailored to students’ new needs to improve their learning experience.

Contact North Contact Nord—a distance education and training network—addressed some features, facts, and challenges regarding artificial intelligence techniques in teaching and learning (Contact North Contact Nord, 2018) as follows:

Nguyen et al. (2020) claimed that AI technology has made great strides in our everyday lives and helped people fight against COVID-19. While numerous studies have been published on COVID-19, the researches on AI applications and contributions in this war in the context of education are limited. This is partly because of the restricted availability of COVID-19 data in this specific background. The computational models usually need a lot of data for learning and analysis. Nonetheless, it is anticipated that the focus on AI studies related to COVID-19 will augment substantially in the coming years when more data would become available. For this purpose, repositories should be developed and made available to researchers worldwide to use the available data in their research.

5.2. Online Learning or E-Learning

Technology has been playing a prominent role during the present crisis. E-learning techniques have been helping the learners to study at home. Educators' roles in the education changes, especially when they switch from conventional classroom to virtual teaching. Improvements to such transition are continually being made by validating new teaching and learning ideas and offering encouragement to learning emerging technologies. Distance education is often commonly referred to as online education since the students are physically far away from their place of education. With the implementation of technology in higher education, the primary responsibilities of people interested in distance and online education have changed. Educational theorists have examined how and where education theories, including social constructivism, communication, and learning and practice groups, can be used in the online world to accelerate learning. Frameworks and models have also emerged to teach and design the educational materials, as suggested by Nagro and Campion (2017). These include the technological, teaching, knowledge information, and research community models. Such frameworks help in recognizing new or updated educators' roles, abilities, and skills. Traditionally, remote education providers used to hire part-time educators to undertake the needs of distance learners. Indeed, the instructors need new skills and knowledge as they are heading into the online world. It is anticipated that the universities, jointly with online educational services providers, will analyze and support the evolving roles and skills through trainings  (Ní Shé et al., 2019).

Amid the deadly pandemic of COVID-19, online platforms are crucial for learning. Additionally, the wide availability of information technologies offers opportunities for exploiting these resources to continue education. In the current era, internet access is satisfactory, and through readily available tools, video conferencing with 40 to 50 students is comfortably possible. Moreover, discussions may take place with the students to keep the classes interactive, while lectures are also available on mobile phones and laptops (Basilaia, Dgebuadze, Kantaria, & Chokhonelidze, 2020).

He (2020) proposed to continue the college teaching of English throughout the COVID-19 virus shut down period via online learning where both teachers and learners can build an interactive learning environment. In Saudi Arabia, because students’ expectations were in favor of e-learning for English as a Foreign Language (ESL) and English as a Second Language (EFL) classes, the researchers called for its adoption as a viable means of learning during the pandemic owing to its significant benefits and marginal drawbacks (Ahmad, 2020). The benefits of e-learning and its potential in education is also evident from the reports which have mentioned improved writing quality by using cloud-based collaboration through Google Docs (Ahmad, 2020); Telegram preferred EFL vocabulary learning (Abu-Ayfah, 2020); the use of mobile technology for improving student-teacher connectivity (Alshehri, Rutter, & Smith, 2019) and Nearpod platform for enhancing collaboration among students (Hakami, 2020). Other researchers also investigated and illustrated the paramount role of e-learning in the COVID-19 crisis (Kaur, Sahdev, Chaturvedi, & Rajawat, 2020).

5.3. The Transition of Teaching Quality from Traditional Education to E-learning

In comparison with the evaluation of the quality of science comprehended by quantitative methods, the quality of education remained a contentious field for years. Nevertheless, approaches such as class observations, analysis of student statistics, peer reviews, and teacher interviews have made significant efforts to assess teaching efficiency objectively. The students’ assessment has also become a priority method because, as many sources have pointed out, the teaching performance can be measured at the student level as the direct recipient of teaching (Steinberg & Garrett, 2016). Ustünlüoğlu (2016) suggested that a measure of the academic success or degree expectancy of a university should be taken into account in order to provide an in-depth analysis of the findings in other variables, including ethnicity, gender, and average grade point. The quality of the education would improve through an emphasis on the student needs and desires, a comparison of the teaching staff's performance and competencies, and the development of good higher education assessment systems.

The quality of teaching can be evaluated by the traces of educational activities that are increasingly created, processed, analyzed, and organized in a way that not only assesses but also educates people in the first place. However, the readers should carefully consider the following points to understand the aspects of quality of education:

5.4. Teachers’ Perception toward the E-Learning Classes

Perception is a thought about something, which can be expressed through attitude. The experiences of teachers form an essential part of the learning process. Therefore, the interpretation of the teacher about e-learning would affect the actions of the students. One major reason for studying teachers' perceptions is that their beliefs also impact the students’ behaviors. Hence, the evaluation of teachers should be part of an appraisal for effective learning. Researchers observed that, since the 1980s, teachers' assumptions became increasingly recognized as a technique for estimating the quality of the learning process (Azjen & Fishbein, 1980; Windiarti, Fadilah, Dhermawati, & Pratolo, 2019).

The attitudes of the educators concerning technology affect their ability to use and incorporate it into the curriculum and teaching. That is why the tech teachers, having programming knowledge, depict a constructive approach to technology adoption. Hence, computer knowledge has a direct connection to teaching behavior. The more informed the teachers about technology are, the more likely they are to demonstrate positive attitudes towards using it in education (Yanti & Setiawan, 2018). Afzal, Safdar, and Ambreen (2015) explored the teachers' perceptions of employing information and communication technology (ICT) in physics to develop a systematic theoretical base and apply the developed framework for e-learning environments, demonstrating that teachers had a positive attitude towards technology. The students also considered technological ways of teaching to be superior to the conventional ones, and they were more welcoming to online learning material.

Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) emerged at the end of 2019 and spread globally. In March 2020, the World Health Organization declared SARS-Cov-2 a global pandemic (Di Gennaro et al., 2020). In order to avoid any probable potential health care crises, the educational campuses were closed. In this scenario, a big challenge to the education sector was how institutions could continue to provide schooling if they decide to cancel the in-person classes? Since the pandemic, an increasing number of students are taking classes online as a short-term solution. Of course, switching to online learning is not an ideal solution: not many professors have taught online before, and technical support is often lacking; however, many institutions are looking to online learning as a way to save student education in the wake of the current situation. It can be anticipated that this group of learners and teachers will provide insights into the use of technology for educational purposes in uncertain times (Dill, Fischer, McMurtrie, & Supiano, 2020).

This study investigated the concept and various aspects of e-learning and its role in online-teaching when switching from traditional learning, keeping in view the current pandemic. The main objective was to investigate the role of AI techniques to facilitate this sudden transformation to faculty members, It also investigate the faculty members’ perspectives regarding AI techniques and e-learning in education amid the COVID-19 crisis. The sampling population belonged to Saudi Arabian institutions. A descriptive-analytical approach was undertaken to analyze data.

6. METHODOLOGY

6.1. Study Design

A descriptive analytical approach was used to illustrate the faculty members’ perspectives in Saudi Arabian universities regarding practices related to teaching methods, faculty members’ practices generally, and the role of applying artificial intelligence techniques in e-learning management systems in the wake of COVID-19virus pandemic. The researcher conducted the study by identifying the problem, developing the questionnaire, selecting appropriate samples, collecting data, classifying and coding the data, and then describing, analyzing, and interpreting results.

6.2. Sample Size and Type

The study population consisted of teaching staff members in Saudi Arabia’s public sector universities. The data were collected using survey monkey by distributing the survey to all faculty members through emails. The samples were picked randomly from the universities, along with demographic variables and educational qualification data. It was ensured that the participants understood AI techniques and its implementation by providing concise definitions of the terms in the survey. The faculty members’ responses were collected on a five-point Likert scale ranging from strongly disagrees to strongly agree. Four themes of e-learning and the role of artificial intelligence were covered, including 1) The lecture aims related practices during the pandemic, 2) Practices related to teaching methods during the pandemic, 3) The faculty members’ practices in general during the pandemic, and 4) The use of artificial intelligence techniques in e-learning management systems used by the college during the pandemic. A total of 406 samples were recorded from various institutions of the country.

6.3. Statistical Analysis of the Data

Pearson’s correlation analysis was done to assess construct validity. For reliability testing, Cronbach’s alpha coefficient was determined for the four dimensions of the study. Moreover, the normal distribution of the samples was confirmed using the Kolmogorov-Smirnov test.

Table-1. Demographic data of the sampling population.
Variables Categories
Frequency
Percent
Sex male
146
36%
female
260
64%
Nationality Saudi
271
66.7%
Non- Saudi
135
33.3%
Age 20-30
24
5.9%
31-40
194
47.8%
41-50
132
32.5%
51+
56
13.8%
Level of education Bachelor's degree
21
5.2%
Master's degree
89
21.9%
Ph.D. degree (Assistant professor)
203
50%
Associate professor
77
19%
Professor
16
3.9%
Major Business administration and accounting
28
6.9%
E-commerce
13
3.2%
Medicine and health sciences
47
11.6%
Computer science and information technology
97
23.9%
Law
23
5.7%
Digital media
3
0.7%
English language and translation
20
4.9%
Arabic studies
12
3%
Natural Science (Biology, Chemistry, Mathematics, Physics, and Earth & ocean sciences)
65
16%
Engineering
8
2%
Social sciences including History, Linguistics, Psychology, and Women's studies
13
3.2%
Economics
7
1.7%
Art
3
0.7%
Islamic studies
28
6.9%
others
39
9.6%
Experience at traditional teaching Less than one year
38
9.4%
From 1 to 3 years
81
20%
More than 3 years
287
70.7%
Experience at e- learning Less than one year
164
40.4%
From 1 to 3 years
111
27.3%
More than 3 years
131
32.3%
Experience at using artificial intelligence technique Less than one year
233
57.4%
From 1 to 3 years
103
25.4%
More than 3 years
70
17.2%
In the early days of the pandemic, my institution trained the faculty members in the learning management system that we are going to use Strongly disagree
4
1%
Disagree
15
3.7%
Neither agree nor disagree
59
14.5%
Agree
147
36.2%
Strongly Agree
181
44.6%
During the pandemic, what type of learning management system does your institution provide Blackboard
385
94.9%
Non
5
1.2%
Others (Canvas, Google classroom, Webex, Zoom, M team)
16
3.9%
Have you ever used E-learning and artificial intelligence techniques in teaching before the COVID-19 pandemic? Never
131
32.3%
Sometimes
146
36%
Always
129
31.7%

For four themes of influence of e-learning and AI techniques in the current scenario of the pandemic, the percentages of different responses to the questionnaire were calculated. Further, means were determined, ranks were established, and classification of the responses was done. Ultimately, dimensions means were calculated. The impact of demographic factors (age, nationality, level of education, and type of institution) was analyzed using statistical tests such as ANOVA, LSD test, t-test, as appropriate. Moreover, the impact of experience in traditional learning, e-learning, using artificial intelligence, and using e-learning and artificial intelligence techniques before the COVID-19 pandemic was also determined. Finally, hypothesis testing was done through the one-sample t-test.

7. RESULTS

7.1. Collected Data

It was noticed that the use of Blackboard as a learning management system represented the highest percentage among the faculty members. The sampling population comprised a higher number of female participants than the males. Moreover, the percentage of the native respondents was greater than the non-Saudi faculty members. Regarding the participants' ages, the largest percentage was constituted by the faculty members ranging in age from 30 to 40 years.

7.2. Demographic Variables

The sampling population was constituted of 406 university faculty members. The figures about the demographic variables of the samples are presented in Table 1.

7.3. Construct Validity

The construct validity is one of the measures that depicts the validity of the approaches employed in research. It determines the extent to which the objective, the tool is intended to accomplish, is achieved. Further, it shows the level of association between each field of study and the overall degree of the questionnaire paragraphs.

In this study, the highest correlation coefficient (.121) was observed for the field “Practices related to teaching methods during the pandemic.” The positive correlation was weak but statistically significant. The correlation coefficient for the field practices during the pandemic was equal to .047. It was also a weak, direct, and statistically significant correlation. The other two fields analyzed in this study depicted inverse correlation. The correlation coefficient value for the domain, the faculty members' practices in general during the pandemic, was -.097; while the field “Application of artificial intelligence techniques” demonstrated the maximum and highly significant negative correlation coefficient of -0.215 Table 2.

Table-2. Results of the construct validity.
Field
Pearson’s Correlation Coefficient
Sig.
Practices during the pandemic
0.047*
0.002
Practices related to teaching methods during the pandemic
0.017
The faculty members practices in general during the pandemic
-0.097-*
0.019
Application of artificial intelligence techniques
0.000

Table-3. Cronbach's alpha coefficient of reliability.

Field
Number of paragraphs
Cronbach Alpha Laboratories
The lecture aims related practices during the pandemic
8
0.348
Practices related to teaching methods during the pandemic
8
0.741
The faculty members practices in general during the pandemic
11
0.878
Applying artificial intelligence techniques
17
0.640
All fields
44
0.871

7.4. Reliability

The reliability testing was done through the Cronbach’s alpha coefficient. The results evinced a total (for all fields of the questionnaire) Cronbach’s alpha coefficient value of 0.871 Table 3. Hence, the validity and reliability of the study questionnaire for testing hypotheses was verified.

7.5. Normal Distribution Test

The normal distribution test is an important pre-parameter test. Table 4 shows the normal distribution of the data was corroborated using the Kolmogorov-Smirnov test. All the four fields (denoting the themes of e-learning analyzed in the study) illustrated the values of .  The results of the test showed that the data followed a normal distribution.

Table-4. Results of Kolmogorov-Smirnov test for normal distribution of data.
 
Kolmogorov-Smirnov
Z
Degrees of freedom
Sig.
The field1
0.831
406
0.120
The field2
0.771
406
0.320
The field3
0.812
406
0.251
The field4
0.481
406
0.321
Total marks
0.771
406
0.072

7.6. The Lecture Aims Related Practices during the Pandemic

Our results indicated that faculty members strongly agreed about the influence of AI techniques and e-learning on the lecture aims related practices during the pandemic. Overall, a mean value of 4.22 was recorded for this field Table 5. The teachers strongly consented that e-learning and AI techniques improved their lecture aims related practices during the pandemic, such as live interaction with the students. Moreover, the educators strongly approved that e-learning and AI techniques facilitated in formulating educational goals, setting lectures' aims logically, taking into account the setting of goals according to the needs and inclinations of students, providing the ability to implement lesson objectives on time, and focusing on the goals that address student's difficulties. Further, the participants agreed that the technologies mentioned above helped them focus on the students’ role in decision-making and participate with their students in formulating the goals of the course whenever possible.

7.7. Practices Related to Teaching Methods during the Pandemic

The faculty members strongly agreed (m= 4.12) on the role of AI techniques and e-learning in ameliorating their teaching methods during the pandemic Table 6.

They strongly approved that e-learning and AI techniques facilitated in preparing lectures from multiple sources, using open discussions, exploiting interesting teaching methods to excite students' motivation, employing a variety of teaching methods that contribute to the understanding of the scientific subject, and looking for new teaching methods that suit virtual classes.

Moreover, the sampling population also agreed that AI techniques and e-learning helped to use teaching methods that take into account individual differences among students. These technologies also provided the option to stop giving the lecture whenever teachers or the students felt uncomfortable. Furthermore, these techniques made it possible to employ e-lab software for teaching purposes.

7.8. The Faculty Members Practices in General during the Pandemic

The faculty members strongly agreed that AI techniques and e-learning bettered their teaching practices in general during the pandemic (m= 4.49). Various aspects of coaching were evaluated in this context Table 7. The teachers strongly agreed that the said technologies helped them enhance their availability to answer students' queries. The data also depicted that the faculty members enjoyed the subject they taught through virtual classes. Moreover, the educators strongly approved that these techniques helped them direct students to benefit from internet services, treat their students with appreciation and respect, deal with them fairly, cultivate confidence in them, and encourage them to extract results and ideas through discussion and questioning. Furthermore, the instructors strongly believed that these techniques did not spoil the delivery of lectures, and they could speak clearly during the virtual classes.

Also, e-learning assisted teachers in presenting study plan for the course at the beginning of the semester and recording students’ absences.

7.9. University Enables You to Apply Artificial Intelligence Techniques

Overall, the faculty members agreed that their university enabled them to apply artificial intelligence techniques (m= 3.74) in teaching Table 8. They acceded that AI techniques helped them automate grades and evaluation within the educational management system, increase the confidentiality of information, and communicate efficiently with the students.

Moreover, the faculty members strongly suggested that e-learning and AI techniques benefit students in accessing information quickly. Further, the instructors agreed that AI techniques helps students solve their academic problems, guides them where they need improvement in coursework, and evaluates their knowledge to provide feedback depending on their grades. Additionally, they consented that AI techniques supports self-learning by providing students with applications and artificial intelligence-based educational programs and aids adaptive learning by developing a detailed report about the subjects that students find difficult to understand.

The data of this study evinced that teachers in Saudi Arabia had adopted online education to some extent, and they were making effective use of e-learning and AI techniques.

The academics agreed that they use virtual mentoring to track learners' progress, employ rubrics scoring tools to evaluate graded work, determine their students' knowledge using electronic exams, use smart content (digitized textbooks), and tailor educational programs to the needs of students through AI techniques. Nevertheless, the educators did not agree nor disagree regarding the advantages of using virtual facilitators or ChatBots to help students get answers to their queries. They also neither agreed nor disagreed that e-learning and IA techniques improved their teaching experience by sending a notification when they would speak too fast or too slow.

Table-5. Influence of AI and e-learning on the first dimension: The lecture aims related practices during the pandemic.

Items
Degree of agreement
Mean
Rank
 Classification
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
%
f
%
f
%
f
%
f
%
f
Emphasize the live interaction between students
47.54%
193
44.83%
182
5.17%
21
1.48%
6
0.99%
4
4.36
1
 SA
Focus on the student's role in decision-making
29.56%
120
57.64%
234
10.84%
44
1.23%
5
0.74%
3
4.14
7
 A
Formulate educational goals well
40.89%
166
52.71%
214
5.67%
23
0.49%
2
0.25%
1
4.33
3
 SA
Take into account the setting of goals according to the needs and inclinations of students
35.47%
144
52.71%
214
10.59%
43
0.99%
4
0.25%
1
4.22
6
 SA
Arrange lectures' goals logically from easy to difficult, from simple to complex
45.81%
186
45.81%
186
6.90%
28
0.99%
4
0.49%
2
4.35
2
 SA
Have the ability to implement lesson objectives on time
39.90%
162
52.46%
213
5.17%
21
1.97%
8
0.49%
2
4.29
4
 SA
Focus on the goals that address student's difficulties
39.16%
159
51.23%
208
8.13%
33
1.23%
5
0.25%
1
4.27
5
 SA
Participate with your students in formulating the goals of the course whenever possible
22.66%
92
45.57%
185
24.88%
101
5.17%
21
1.72%
7
3.82
8
 A
Dimension mean
4.22
 SA

Note: SA: strongly agree; A: agree.

Table-6. Influence of AI and e-learning on the second dimension: Practices related to teaching methods during the pandemic.
Items
Degree of agreement
Mean
Rank
Classification
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
%
f
%
f
%
f
%
f
%
f
Use teaching methods that take into account individual differences between students
32.02%
130
51.23%
208
13.79%
56
2.96%
12
0.00%
0
4.12
5
A
Use a variety of teaching methods that contribute to the understanding of the scientific subject and suit its nature
34.24%
139
56.40%
229
7.88%
32
1.23%
5
0.25%
1
4.23
4
SA
Look for new teaching methods that suit virtual classes
35.71%
145
53.94%
219
8.62%
35
1.48%
6
0.25%
1
4.23
4
SA
Use interesting teaching methods to excite students' motivation to learn
36.79%
149
54.57%
221
7.90%
32
0.74%
3
0.00%
0
4.27
3
SA
Use open discussion that provides an opportunity to participate in the scientific subject
40.15%
163
50.99%
207
7.64%
31
0.99%
4
0.25%
1
4.29
2
SA
Prepare your lectures from multiple sources
42.36%
172
47.29%
192
8.37%
34
1.97%
8
0.00%
0
4.30
1
SA
Stop giving the lecture whenever you feel the students are uncomfortable
23.15%
94
45.07%
183
19.70%
80
11.08%
45
0.99%
4
3.78
6
A
Employ teaching methods in its practical aspects using E-lab software
20.44%
83
45.32%
184
24.88%
101
7.14%
29
2.22%
9
3.74
7
A
Dimension mean
A

Note: SA: strongly agree; A: agree

Table-7. Influence of AI and e-learning on the third dimension: The faculty members practices in general during the pandemic.
Items
Degree of agreement
Mean
Rank
Classification
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
%
f
%
f
%
f
%
f
%
f
Enjoy the subject you offer during virtual classes
38.42%
156
50.00%
203
8.13%
33
2.71%
11
0.74%
3
4.22
10
SA
Encourage your students to ask and discuss questions
57.88%
235
38.92%
158
2.71%
11
0.25%
1
0.25%
1
4.53
5
SA
Available to answer students' inquiries by conducting extra virtual office hours
49.01%
199
41.13%
167
6.16%
25
3.45%
14
0.25%
1
4.35
9
SA
Present the study plan for the course at the beginning of the semester
59.11%
240
36.70%
149
3.45%
14
0.74%
3
0.00%
0
4.54
4
SA
Speak clearly during the virtual class
65.76%
267
31.77%
129
1.72%
7
0.74%
3
0.00%
0
4.62
3
SA
Treat your students with appreciation and respect
69.21%
281
29.31%
119
1.23%
5
0.00%
0
0.25%
1
4.67
1
SA
Record the absences of your students continuously
53.20%
216
38.18%
155
6.16%
25
1.72%
7
0.74%
3
4.41
8
SA
Direct your students to benefit from internet services and encourage them to do so
56.40%
229
39.41%
160
3.45%
14
0.49%
2
0.25%
1
4.51
6
SA
Provide an opportunity for your students to extract results and ideas through your discussion with them
50.99%
207
44.33%
180
3.69%
15
0.74%
3
0.25%
1
4.45
7
SA
Cultivate confidence in your students
58.13%
236
38.42%
156
3.20%
13
0.25%
1
0.00%
0
4.54
4
SA
Deal with your students fairly and equitably
66.50%
270
31.77%
129
1.48%
6
0.25%
1
0.00%
0
4.64
2
SA
Dimension mean
4.49
SA

Note: SA: strongly agree.

Table-8 . Influence of AI and e-learning on the fourth dimension: University enables the teachers to apply artificial intelligence techniques
Items
Degree of agreement
Mean
Rank
Classification
Strongly agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
%
f
%
f
%
f
%
f
%
f
 
Automate grades and evaluation within the educational management system
36.95%
150
47.54%
193
12.56%
51
2.22%
9
0.74%
3
4.17
2
A
Allow the machine to evaluate the students’ knowledge to provide feedback depending on their grades
20.69%
84
32.27%
131
37.68%
153
6.65%
27
2.71%
11
3.61
10
A
Allow the machine to draw personal training plans for students depending on their grades
16.75%
68
26.11%
106
41.13%
167
12.07%
49
3.94%
16
3.39
13
N
Use virtual facilitators to help students to get answers to their queries without the intervention of the teacher (such as artificial intelligence technology in education IBM Watson)
14.29%
58
22.41%
91
42.86%
174
14.53%
59
5.91%
24
3.24
14
N
Support self-learning by providing students with applications and a series of educational programs based on artificial intelligence to meet the students' cognitive needs
20.20%
82
35.22%
143
31.53%
128
9.11%
37
3.94%
16
3.58
11
A
Support adaptive  learning by obtaining a detailed report about the subjects that students find difficult to understand and assimilate by updating and modifying educational paths and curricula whenever the need arises
16.75%
68
39.16%
159
30.79%
125
9.61%
39
3.69%
15
3.55
12
A
Increase confidentiality of information, individual interaction, and communication efficiency between you and your students
40.15%
163
40.15%
163
16.75%
68
1.72%
7
1.23%
5
4.16
3
A
Help students solve their academic problems
38.67%
157
43.10%
175
15.02%
61
2.22%
9
0.99%
4
4.16
3
A
Tailor educational programs to the needs of students
28.57%
116
38.18%
155
26.35%
107
4.43%
18
2.46%
10
3.85
6
A
Help your students to indicate where they need improvement in coursework
36.45%
148
40.89%
166
17.98%
73
2.96%
12
1.72%
7
4.07
5
A
Help students to access information easily
41.63%
169
42.12%
171
13.79%
56
1.48%
6
0.99%
4
4.21
1
SA
Use virtual mentoring to track learners progress
23.65%
96
38.18%
155
28.82%
117
5.67%
23
3.69%
15
3.72
8
A
Examine your students' knowledge using electronic exams
37.68%
153
43.84%
178
14.29%
58
2.46%
10
1.72%
7
4.13
4
A
Use rubrics scoring tool to evaluate graded work
28.57%
116
37.93%
154
23.65%
96
4.93%
20
4.93%
20
3.80
7
A
Use smart content that allows you to provide your students with a digitized textbook and customized interfaces.
24.88%
101
35.71%
145
29.06%
118
5.67%
23
4.68%
19
3.70
9
A
Improve your teaching experience during the virtual class by sending a notification if you are speaking too fast or too slow.
19.21%
78
24.88%
101
34.24%
139
10.34%
42
11.33%
46
3.30
15
N
Allow ChatBot to answer students’ questions regarding the lessons or any question generally
15.76%
64
20.20%
82
35.22%
143
14.78%
60
14.04%
57
3.08
16
N
Dimension mean
3.74
A

Note: SA: strongly agree; A: agree; N: Neither agree nor disagree

7.10. Impact of Age

Since the age of the participants in a study can be an essential factor influencing the overall results, this research used the analysis of variance (ANOVA) test to find the differences among the responses of the faculty members with respect to their age Table 9.

Table-9. Results of ANOVA test regarding ages of the participants.
Dimension Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
83.482
3
27.827
1.630
0.182
Within Groups
6860.915
402
17.067
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
55.371
3
18.457
0.963
0.410
Within Groups
7705.567
402
19.168
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
242.985
3
80.995
3.295
0.021
Within Groups
9880.269
402
24.578
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
904.927
3
301.642
2.021
0.110
Within Groups
60014.846
402
149.291
Total
60919.773
405
total Between Groups
3483.750
3
1161.250
2.652
0.058
Within Groups
176041.799
402
437.915
Total
179525.549
405

It was found that the factor of age exhibited significant differences only in one dimension viz. the faculty members’ practices in general during the pandemic (f= 3.29, α ≤0.05) while other dimensions did not depict significant differences ( α > 0.05). Further, post hoc analysis was conducted by LSD test, as shown in Table 10.

Table-10. Results of LSD test for differences among the age groups.
Dependent Variable
Category1
Category2
Mean Difference (I-J)
Sig.
The faculty members practices in general during the pandemic
20-30
31-40
-3.12801*
0.004
41-50
-3.34848*
0.002
51+
-2.59524*
0.032
31-40
20-30
3.12801*
0.004
41-50
-0.22048
0.694
51+
0.53277
0.479
41-50
20-30
3.34848*
0.002
31-40
0.22048
0.694
51+
0.75325
0.341
51+
20-30
2.59524*
0.032
31-40
-0.53277
0.479
41-50
-0.75325
0.341

Our results indicated that the faculty members whose ages range from 20-30 years have less practice in general during the pandemic than others whose ages ranged from (31-51+).

7.11. Differences According to the Nationality

The independence t-test was used to find the differences among faculty members according to nationality Table 11.

Table-11. Results of t-test regarding the nationality of the participants.
Dimension Nationality
N
Mean
Std. Deviation
t
sig
The lecture aims related practices during the pandemic Saudi
271
33.4280
4.16971
-2.65
0.008
non-Saudi
135
34.5778
3.98779
Practices related to teaching methods during the pandemic Saudi
271
32.5720
4.47145
-2.73
0.007
non-Saudi
135
33.8222
4.07187
The faculty members practices in general during the pandemic Saudi
271
49.2177
5.06263
-1.75
0.085
non-Saudi
135
50.1407
4.83020
University enables you to apply artificial intelligence techniques Saudi
271
61.8376
12.93013
-5.18
0.000
non-Saudi
135
67.7778
9.69664
total Saudi
271
177.0554
21.40891
-4.26
0.000
non-Saudi
135
186.3185
18.93455

The results depicted significant differences in all dimensions (α ≤0.05) except the dimension of the faculty members’ practices in general during the pandemic. All of the differences related to the non-Saudi faculty members.

7.12. Differences According to the Level of Education

The influence of education on shaping responses of the participants was also investigated. ANOVA test was used to find the significant differences among the faculty members' responses (Table 12). The results indicated no significant differences in all dimensions concerning this factor (α> 0.05).

Table-12. Influence of the level of education.
Dimension Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
32.852
4
8.213
0.477
0.753
Within Groups
6911.545
401
17.236
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
49.067
4
12.267
0.638
0.636
Within Groups
7711.872
401
19.232
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
204.577
4
51.144
2.068
0.084
Within Groups
9918.676
401
24.735
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
918.999
4
229.750
1.535
0.191
Within Groups
60000.774
401
149.628
Total
60919.773
405
total Between Groups
2430.696
4
607.674
1.376
0.241
Within Groups
177094.853
401
441.633
Total
179525.549
405

7.13. Differences According to Teaching Experience in Traditional Learning

Further, the impact of teaching experience in traditional learning was also investigated on the use of e-learning and AI (Table 13).

The significant differences were found only in dimension relating to the faculty members' practices in general during the pandemic for which the f= 4.68 and α ≤0.01, whereas the f values of all other dimensions had no significant degree (α > 0.05). Next, a post hoc analysis was carried out by LSD test Table 14.
The results indicated that the faculty members having less than one year of experience in traditional learning had less practice in general during the pandemic than the other groups who had 1- 3 years and more than three years of the traditional learning experience.

7.14. Differences According to Teaching Experience in E-Learning

The influence of teaching experience in e-learning was also examined. For this purpose, the current study used the ANOVA test to find the differences among faculty members according to the teaching experience in e-learning Table 15.

Table-13. Results of ANOVA test regarding teaching experience in traditional learning.
Dimension Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
33.297
2
16.648
0.971
0.380
Within Groups
6911.100
403
17.149
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
7.044
2
3.522
0.183
0.833
Within Groups
7753.895
403
19.240
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
229.903
2
114.952
4.682
0.010
Within Groups
9893.350
403
24.549
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
253.146
2
126.573
0.841
0.432
Within Groups
60666.627
403
150.538
Total
60919.773
405
total Between Groups
958.321
2
479.161
1.081
0.340
Within Groups
178567.228
403
443.095
Total
179525.549
405

Table-14. Results of LSD test for faculty members’ practices in general during the pandemic.
Dimension Category1 Category 2
Mean Difference (I-J)
Sig.
The faculty members practices in general during the pandemic Less than one year From 1 to 3 years
-2.53184*
0.010
More than 3 years
-2.59628*
0.003
From 1 to 3 years Less than one year
2.53184*
0.010
More than 3 years
-0.06444
0.918
More than 3 years Less than one year
2.59628*
0.003
From 1 to 3 years
0.06444
0.918

Table-15. Results of ANOVA test regarding the influence of teaching experience in e-learning.
Dimension Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
132.676
2
66.338
3.925
0.021
Within Groups
6811.721
403
16.903
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
291.774
2
145.887
7.871
0.000
Within Groups
7469.165
403
18.534
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
428.578
2
214.289
8.908
0.000
Within Groups
9694.676
403
24.056
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
4122.349
2
2061.175
14.625
0.000
Within Groups
56797.424
403
140.937
Total
60919.773
405
total Between Groups
12814.360
2
6407.180
15.488
0.000
Within Groups
166711.189
403
413.675
Total
179525.549
405

Significant differences were observed in all dimensions. Therefore, a post hoc analysis was conducted by LSD test Table 16.

Table-16. Results of LSD test for differences according to the experience in e-learning.
Dependent Variable Category 1 Category 2
Mean Difference (I-J)
Sig.
The lecture aims related practices during the pandemic Less than one year From 1 to 3 years
-1.04922*
0.038
More than 3 years
-1.24446*
0.010
From 1 to 3 years Less than one year
1.04922*
0.038
More than 3 years
-0.19524
0.713
More than 3 years Less than one year
1.24446*
0.010
From 1 to 3 years
0.19524
0.713
Practices related to teaching methods during the pandemic Less than one year From 1 to 3 years
-1.43271*
0.007
More than 3 years
-1.90379*
0.000
From 1 to 3 years Less than one year
1.43271*
0.007
More than 3 years
-0.47108
0.397
More than 3 years Less than one year
1.90379*
0.000
From 1 to 3 years
0.47108
0.397
The faculty members practices in general during the pandemic Less than one year From 1 to 3 years
-1.74017*
0.004
More than 3 years
-2.30581*
0.000
From 1 to 3 years Less than one year
1.74017*
0.004
More than 3 years
-0.56564
0.372
More than 3 years Less than one year
2.30581*
0.000
From 1 to 3 years
0.56564
0.372
University enables you to apply artificial intelligence techniques Less than one year From 1 to 3 years
-4.65227*
0.002
More than 3 years
-7.38345*
0.000
From 1 to 3 years Less than one year
4.65227*
0.002
More than 3 years
-2.73117
0.075
More than 3 years Less than one year
7.38345*
0.000
From 1 to 3 years
2.73117
0.075
total Less than one year From 1 to 3 years
-8.87437*
0.000
More than 3 years
-12.83751*
0.000
From 1 to 3 years Less than one year
8.87437*
0.000
More than 3 years
-3.96314
0.132
More than 3 years Less than one year
12.83751*
0.000
From 1 to 3 years
3.96314
0.132

In all dimensions, the members who had less than one year of experience in e-learning showed lesser degrees and smaller total degrees compared with the other categories.

7.15. Differences According to Experience in Using Artificial Intelligence Techniques

The ANOVA test was employed to find the significant differences among faculty members according to their use of artificial intelligence techniques Table 17.

Significant differences were observed in all the dimensions, as evident from f values at the level of significance α ≤0.05. Subsequently, a post hoc analysis was performed using the LSD test Table 18.

In the first dimension (the lecture aims related practices during the pandemic), there were significant differences among the participants who had less than one year of experience compared with those who had more than three years of experience in artificial intelligence techniques. In the second dimension (practices related to teaching methods during the pandemic), there was a significant difference between the respondents who had less than one year of experience compared with those having 1-3 years as well as more than three years of experience in using artificial intelligence techniques.

Table-17. Results of ANOVA test regarding the influence of the experience of respondents in using artificial intelligence techniques.
Dimensions Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
163.765
2
81.883
4.867
0.008
Within Groups
6780.631
403
16.825
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
351.347
2
175.674
9.555
0.000
Within Groups
7409.591
403
18.386
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
368.627
2
184.313
7.615
0.001
Within Groups
9754.627
403
24.205
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
5459.972
2
2729.986
19.838
0.000
Within Groups
55459.802
403
137.617
Total
60919.773
405
total Between Groups
15460.076
2
7730.038
18.988
0.000
Within Groups
164065.474
403
407.110
Total
179525.549
405

Table-18. Results of LSD test for differences with respect to the experience of the respondents in using artificial intelligence techniques.

Dependent Variable

Category 1

Category 2

Mean Difference (I-J)

Sig.

The lecture aims related practices during the pandemic

Less than one year

From 1 to 3 years

-0.87974

0.071

More than 3 years

-1.63814*

0.004

From 1 to 3 years

Less than one year

0.87974

0.071

More than 3 years

-.75839

0.233

More than 3 years

Less than one year

1.63814*

0.004

From 1 to 3 years

0.75839

0.233

Practices related to teaching methods during the pandemic

Less than one year

From 1 to 3 years

-1.50385*

0.003

More than 3 years

-2.27112*

0.000

From 1 to 3 years

Less than one year

1.50385*

0.003

More than 3 years

-0.76727

0.249

More than 3 years

Less than one year

2.27112*

0.000

From 1 to 3 years

0.76727

0.249

The faculty members practices in general during the pandemic

Less than one year

From 1 to 3 years

-1.68132*

0.004

More than 3 years

-2.21183*

0.001

From 1 to 3 years

Less than one year

1.68132*

0.004

More than 3 years

-0.53051

0.487

More than 3 years

Less than one year

2.21183*

0.001

From 1 to 3 years

0.53051

0.487

University enables you to apply artificial intelligence techniques

Less than one year

From 1 to 3 years

-5.34439*

0.000

More than 3 years

-9.32164*

0.000

From 1 to 3 years

Less than one year

5.34439*

0.000

More than 3 years

-3.97725*

0.029

More than 3 years

Less than one year

9.32164*

0.000

From 1 to 3 years

3.97725*

0.029

total

Less than one year

From 1 to 3 years

-9.40931*

0.000

More than 3 years

-15.44273*

0.000

From 1 to 3 years

Less than one year

9.40931*

0.000

More than 3 years

-6.03343

0.054

More than 3 years

Less than one year

15.44273*

0.000

From 1 to 3 years

6.03343

0.054

Likewise, the third dimension (faculty members’ practices in general during the pandemic) also produced similar results, and significant differences were observed among less than one year, 1-3 years, and more than three years’ groups. In the fourth dimension, it was evident that the entrants who had greater experience in the use of artificial intelligence techniques exhibited more degrees than those with lesser experience (less than one year and from 1-3 years).

Table-19. Results of ANOVA test regarding the influence of using e-learning and artificial intelligence techniques in teaching before the

COVID-19 pandemic.
Dimension Variance source
Sum of Squares
df
Mean Square
F
Sig.
The lecture aims related practices during the pandemic Between Groups
320.254
2
160.127
9.742
0.000
Within Groups
6624.142
403
16.437
Total
6944.397
405
Practices related to teaching methods during the pandemic Between Groups
707.771
2
353.886
20.220
0.000
Within Groups
7053.167
403
17.502
Total
7760.938
405
The faculty members practices in general during the pandemic Between Groups
742.252
2
371.126
15.943
0.000
Within Groups
9381.002
403
23.278
Total
10123.254
405
University enables you to apply artificial intelligence techniques Between Groups
6756.494
2
3378.247
25.136
0.000
Within Groups
54163.279
403
134.400
Total
60919.773
405
total Between Groups
23011.052
2
11505.526
29.625
0.000
Within Groups
156514.479
403
388.373
Total
179525.549
405

Table-20. Results of LSD test for differences with respect to using e-learning and artificial intelligence techniques in teaching before COVID-19 pandemic.
Dependent Variable Category 1 Category 2
Mean Difference (I-J)
Sig.
The lecture aims related practices during the pandemic
Practices related to teaching methods during the pandemic		 never sometimes
-1.45937*
0.003
Always
-2.17202*
0.000
sometimes never
1.45937*
0.003
Always
-0.71265
0.147
Always never
2.17202*
0.000
sometimes
0.71265
0.147
The faculty members’ practices in general during the pandemic
University enables you to apply artificial intelligence techniques		 never sometimes
-2.75928*
0.000
Always
-2.89266*
0.000
sometimes never
2.75928*
0.000
Always
-0.13338
0.792
Always never
2.89266*
0.000
sometimes
0.13338
0.792
The lecture aims related practices during the pandemic
Practices related to teaching methods during the pandemic		 never sometimes
-1.47360*
0.012
Always
-3.37091*
0.000
sometimes never
1.47360*
0.012
Always
-1.89731*
0.001
Always never
3.37091*
0.000
sometimes
1.89731*
0.001
The faculty members practices in general during the pandemic never sometimes
-7.10996*
0.000
Always
-9.83922*
0.000
sometimes never
7.10996*
0.000
Always
-2.72927
0.052
Always never
9.83922*
0.000
sometimes
2.72927
0.052
total never sometimes
-12.80221*
0.000
Always
-18.27481*
0.000
sometimes never
12.80221*
0.000
Always
-5.47260*
0.022
Always never
18.27481*
0.000
sometimes
5.47260*
0.022

Hence, the respondents having 1-3 years of experience demonstrated more degrees than those with less than one year of experience in using artificial intelligence techniques. Overall, the greater experience in the use of AI techniques produced comparatively higher values.

7.16. Differences According to Using E-learning and Artificial Intelligence Techniques in Teaching before the COVID-19 Pandemic

The ANOVA test was used to find the differences among faculty members according to their experience of using e-learning and artificial intelligence techniques in teaching before the COVID-19 pandemic. The results of this analysis are presented in Table 19.

Significant differences were noticed in all dimensions (α ≤0.05). Next, a post hoc analysis was conducted using the LSD test Table 20.
The results of LSD analysis indicated that the respondents who had prior e-learning and artificial intelligence techniques use experience had more degrees in all dimensions.

7.17. Differences Due to Type of Institution

For assessing the effect of affiliation of the responders, the researcher considered the Saudi Electronic University as the first category and all other universities as the second category. Then, t-test was employed to find the significant differences (Table 21).

Table-21. Results of t-test regarding the influence of the type of the university.
Dimension Type of university
N
Mean
Std. Deviation
t
Sig.
The lecture aims related practices during the pandemic Saudi Electronic University
165
34.3333
4.09690
2.11
0.035
other universities
241
33.4523
4.14111
Practices related to teaching methods during the pandemic Saudi Electronic University
165
33.6727
4.22560
2.62
0.009
other universities
241
32.5187
4.42633
The faculty members practices in general during the pandemic Saudi Electronic University
165
50.6909
4.58739
3.95
0.000
other universities
241
48.7261
5.12100
University enables you to apply artificial intelligence techniques Saudi Electronic University
165
67.0061
10.09135
4.44
0.000
other universities
241
61.6266
13.13145
total Saudi Electronic University
165
185.7030
18.14146
4.51
0.000
other universities
241
176.3237
22.07325

There was a significant difference between Saudi Electronic University and other universities in all dimensions (t values had α <0.05). This analysis showed that e-learning and AI techniques had a greater role in improving faculty members' behavior and practices in Saudi Electronic University compared with the other universities.

Table-22. Means of sample responses for the four fields of the study.

Statistics
 
Practices during a pandemic
Teaching methods
Faculty member
Artificial intelligence
N Valid
406
406
406
406
Missing
1
1
1
1
Mean
4.2263
4.1239
4.5103
3.7537
Arrangement
Assent
Assent
Strong approval
Assent

Further dissection of our data indicated that the average of the sample responses for the field of practices during the pandemic was equal to 4.2263, which corresponded to the degree of approval. While the average of the samples’ responses for the field of teaching methods was 4.1239, which also corresponded to the degree of approval (Table 22). The highest average of the sample responses was noticed for the field “faculty members.” The mean for this parameter was 4.5103, and it corresponded to the degree of strong approval. Similarly, the average of the sample answers for the field of artificial intelligence also corresponded to the degree of approval (3.7537).

7.18. Hypothesis Testing

Through the one-sample t-test, the hypothesis testing illustrated a significant effect (at the α≤ 0.05 level) of AI techniques on improving the faculty members’ practices during the pandemic (Table 23 ). Secondly, the AI techniques also depicted a statistically significant effect (at the α≤ 0.05) on improving the practices related to teaching methods during the pandemic. Furthermore, the effect of AI techniques on improving faculty practices in general during the pandemic was also significant (α≤ 0.05).

Table-23. Results of hypotheses testing using one-sample t-test.

One-Sample Test
Variables
Test Value = 3
T*
Df*
Sig.
(2-tailed) (alpha)
Mean Difference
95% Confidence Interval of the Difference
Lower
Upper
F1
47.737
405
0.000
1.22629
1.1758
1.2768
F2
41.364
405
0.000
1.12394
1.0705
1.1774
F3
68.086
405
0.000
1.51031
1.4667
1.5539
F4
21.050
405
0.000
0.75369
0.6833
0.8241

Note:
*DF: Degrees of freedom.
*T: T-test of the sample.
Sig= (2-tailed) =(alpha).
F1: Practices during the pandemic.
F2: Practices related to teaching methods during the pandemic.
F3: The faculty members’ practices in general during the pandemic.
F4: Application of artificial intelligence techniques.

8. DISCUSSION

The COVID-19 pandemic presented a unique challenge to the world in 2020. It was a year of lockdowns, social distancing, and masks. The deadly virus brought changes to every aspect of life. Likewise, the education sector was one of the badly hit areas by this pandemic worldwide (Toquero, 2020). Educational institutions were closed all over the world, leading to interruptions in conventional teaching. In this scenario, online education was adopted globally (Bozkurt & Sharma, 2020). In many of the countries, Zoom, Microsoft Teams, and Google Meet dominated as tools for video conferencing and lecturing, while others opted for learning management systems such as Blackboard, Google Classroom, Edmodo, and Moodle (Layali & Al-Shlowiy, 2020; Mukhtar., Javed, Arooj, & Sethi, 2020b; Shahzad, Hassan, Aremu, Hussain, & Lodhi, 2021). Similarly, in Saudi Arabia, the ministry of education instructed the education sector to suspend conventional schooling on 8th March 2020, and the universities shifted to online means of education. Nevertheless, the adoption of online education presented new challenges both to the educators as well as students (Adedoyin & Soykan, 2020). Keeping this background in view, the current study investigated the pedagogical perspective of e-learning in the time of the COVID-19 pandemic. Moreover, since artificial intelligence has become an essential component of e-learning (Zawacki-Richter, Marín, Bond, & Gouverneur, 2019) its significance in improving online education was also examined. A total of 406 samples were collected from various universities in Saudi Arabia, and then an extensive exploration was done to investigate the lecture aims related practices, teaching methods related practices, and faculty members’ practices in general and during the pandemic. Furthermore, the researcher also analyzed the influence of artificial intelligence techniques in improving e-learning. Finally, the impact of various factors such as age, nationality, level of education, and experience in traditional learning, e-learning and AI techniques was researched. Hence, this study carries paramount importance with respect to the current pandemic, online education, and use of AI techniques in e-learning. The data of this study revealed that the age and teaching experience of the participants in conventional education significantly impacted the faculty members’ practices in general during the pandemic. For non-Saudi faculty, the same dimension was significantly influenced by nationality as well, while the level of education did not show a significant effect. This observation could be attributed to the fact that the sampling population encompassed faculty members of academic institutions, and all of the participants were educated. Similar results were reported earlier by Tayyib et al. (2020) while studying e-learning in Saudi Arabia. The differences with respect to the prior teaching experience in e-learning and using AI techniques impacted all the dimensions significantly, indicating that the entrants who had used these tools earlier had appreciated and embraced them. This kind of former experience would have also increased the educators’ readiness to use these techniques (Tayyib et al., 2020). In accordance with our results, Alqahtani and Rajkhan (2020) explored the educators at Saudi universities to determine the factors associated with the e-learning adoption and found that the knowledge of the implementation strategies was a critical component of e-learning success in the COVID-19 crisis. Our results revealed that teachers at Saudi Arabian universities were making extensive use of the e-learning management system Blackboard. The faculty members strongly agreed that e-learning and AI techniques improved relevant lecture aims related practices during the pandemic by facilitating in formulating educational goals, tailoring lecture plans, implementing lessons’ objectives, and assisting in students’ difficulties. Hence, the potential role of e-learning and AI techniques in Saudi Arabia in the wake of the current pandemic was evident. Various AI tools have been developed for teachers as well as students (Pedro, Subosa, Rivas, & Valverde, 2019). These tools interact with the students and learn from their responses (Chassignol, Khoroshavin, Klimova, & Bilyatdinova, 2018). Subsequently, data from these interactions help teachers assess students’ difficulties and set educational goals accordingly (Crown, Fuentes, Jones, Nambiar, & Crown, 2011). Duffy and Azevedo (2015) reported a tool called MetaTutor that aids students in accomplishing their academic targets. Artificial intelligence has also been applied to teach English as a second language, and it successfully remitted linguistic errors and improved the learning efficiency of students (Dodigovic, 2007).

The influence of e-learning and AI techniques on practices related to teaching methods during the pandemic was also highly positive, and the study population either strongly agreed or agreed to the questions in this dimension. The role of e-learning in providing novel teaching methods has been recognized for a while (Andrade, 2015; Gibson, 1983; Li, Qi, Wang, & Wang, 2014). Parallel to our results, Mukhtar et al. (2020b) also mentioned that e-learning facilitates group discussions. Similarly, the role of intelligent tutoring in augmenting students’ motivation has been established in previous studies (Duffy & Azevedo, 2015). The students in a class exhibit individual differences and may have dissimilar needs (Durlach & Lesgold, 2012). In conventional classrooms, it may be challenging to recognize and cater to each students’ learning requirements. AI techniques builds students’ performance profiles and develops models based on their knowledge and personal skills (Hussain, Zhu, Zhang, Abidi, & Ali, 2019). This data can then be used to tweak the teaching methods and content and provide adaptive learning to students (Almohammadi, Hagras, Alghazzawi, & Aldabbagh, 2017). In line with the former reports, the researcher observed the educators' agreement that e-learning and AI techniques helped them take into account the individual differences of learners. The flexibility of online education was another benefit that the study population consented to. The flexibility and better control of e-learning are not limited to teachers alone, but students also experience it Alqahtani and Rajkhan (2020); Layali and Al-Shlowiy (2020); Dhawan (2020). Many times, schedules can be adjusted, or recorded lectures are available for revising the content in online education (Mukhtar et al., 2020b). Thence, learners with busy schedules prefer online courses instead of traditional ones (Butcher & Rose-Adams, 2015). Another theme explored in this study related to the faculty members' practices in general during the pandemic. Here too, the educators strongly agreed that e-learning and AI techniques ameliorated their teaching practices.Our data depicted that online-learning made the teachers' experience exciting. Online education, in fact, goes beyond than just a learning tool (Paudel, 2020). Similar reports have emerged from the educators’ responses from other countries also (Arslan, 2015; Finlayson, Maxwell, Caillau, & Tomalin, 2006). The instructors handle students with more respect, raise their confidence, and promote conversations when they are more interested in e-teaching. All of these parameters are related to “effective online teaching” (Frazer, Sullivan, Weatherspoon, & Hussey, 2017; Sun & Chen, 2016). Hence, it could be speculated that most of our responders were using effective teaching strategies. Regarding learners’ perspectives, Arslan (2015) suggested that students also appreciated the online education and AI experience. Moreover, the entrants of this study asserted that they could speak clearly in online classes. This observation was contrary to the report of Khalil et al. (2020) who analyzed the same aspect with reference to students’ views. In their study, the Saudi students mentioned voice distortion as one of the difficulties they encountered in e-learning. This dissimilarity could be attributed to the differences in teachers versus students’ perspectives. The online teaching and AI techniques also helped mentors in improving their availability to learners, planning courses at the beginning of the semester, and advising students to consult from online resources. Similar results were documented by Mukhtar et al. (2020b) and Zawacki-Richter et al. (2019) as well. The fourth dimension of the study was related to the role of universities in enabling their faculty to use various features of e-learning and AI techniques in the wake of the current pandemic. Our data demonstrated that e-learning and AI techniques assisted instructors in automatic grading and evaluation, better confidentiality of data, improved communication efficiency, easier access to information, and tailoring educational content when needed. Moreover, the faculty members believed that AI techniques were helpful to students to solve their academic issues.Chan and Zary (2019)agreed to such characteristics of online education.The e-learning software are generally equipped with AI techniques that can handle tasks like automatic grading, assessment of learners’ performance, and provision of feedback on their progress (Zawacki-Richter et al., 2019).  The collected data can then be employed to amend educational content and course plans (Wakelam, Jefferies, Davey, & Sun, 2014). The current study revealed insights into the adoption and the use of e-learning and AI techniques by Saudi faculty members in higher education. Hence, this research demonstrated a clear picture of the efficacy of e-learning in the current scenario. The faculty members, in response to many of the queries of this research, consented and admired e-learning and AI techniques. E-learning has been adopted throughout the world as a response to the COVID-19 (Bozkurt & Sharma, 2020). The artificial intelligence tools provide ease, quick response, and essential data for analyzing the success of e-teaching strategies (Chan & Zary, 2019). Thus, these tools have a significant role to play in online education. Elhajji (2020) suggested that AI techniques could be markedly helpful in learning-teaching in Saudi Arabia; nevertheless, backing from all the stakeholders (government, teachers, and students) was necessary. Therefore, vital support for e-learning and AI platforms is suggested, and a further improvement in relevant educational systems is recommended. In the wake of the current COVID-19 pandemic, this study carries significant importance regarding educational research.

9. LIMITATIONS

This study focused on the faculty members’ perspectives regarding online teaching and the use of artificial intelligence in the current scenario of the COVID-19 pandemic. In prospective studies, it would be great to explore the learners’ viewpoint that would highlight the efficiency of these technologies for the actual beneficiary. Although the sample responses were collected from many of the universities in the country, increasing the number and type of institutions under coverage can further augment the predictability of the data. Moreover, for generalizing the results to other countries, future studies should focus on collecting responses from faculty members from various countries as this study only focused on Saudi academic institutions. Also, in the context of the world's current scenario, it would be interesting to see how the education sector would change in the post-pandemic era.

10.CONCLUSION, IMPLICATIONS AND FUTURE PERSPECTIVES

The current study dealt with the role of artificial intelligence techniques in LMS to improve the behavior and practices of faculty members when switching from traditional education to e-learning in light of the COVID-19 crisis. This study fills the knowledge gap concerning e-learning and AI techniques' relevancy in unpredictable circumstances, i.e., a pandemic in this case. It was discovered that the faculty members in Saudi Arabia had a significant inclination towards e-learning and AI techniques in LMS, and they strongly approved many of the advantages of online teaching. A high percentage of the study samples' responses supported the role of online teaching and AI techniques in improving faculty members’ practices in general during the pandemic. Therefore, it can be anticipated that online education and artificial intelligence techniques would play a substantial role in the education sector in the coming years. The current COVID-19 crisis was sudden, and many educators were not equipped with the knowledge of the technicalities involved. Despite these snags, the results of the current study proposed that new doors for online education and using AI techniques in LMS will open as many of the faculty members are trained now, and universities are also investing more resources in this context. This research envisages that e-learning and using AI techniques in LMS have an influential role to play in education even in the post-COVID-19 era. Additionally, the current investigation divulged that the faculty members in Saudi Arabia were embracing e-learning as a beneficial strategy for teaching activities during the pandemic. This research also highlighted that artificial intelligence helped educators automate certain regular tasks, manage their schedules, and tailor their e-teaching practices. This study has paramount importance regarding continuity of education in the context of the COVID-19 pandemic and the role of e-learning and artificial intelligence in education in general.

Funding: The author extends their appreciation to the Deanship of Scientific Research at Saudi Electronic University for funding this work.

Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper.

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