Computer Competencies Needed for Implementing Artificial Intelligence in Special Education Schools from the Perspective of Pre-Service Teachers

Abstract

The purpose of this study is to investigate, through the use of both quantitative and qualitative methods, the computer capabilities that pre-service teachers believe are necessary for integrating Artificial Intelligence (AI) in Special Education Schools (SES). They reviewed earlier research on schooling and created a three-dimensional survey to determine what computing skills are required for implementing AI. Additionally, a range of Al-Qasemi Academy students from the first to the fourth year of study participated in semi-structured interviews with the researchers. These interviews are intended to gather information related to the research questions. After testing, the reliability of the questionnaire produced a reliability score of (0.963). Within the Green Line, it consists of 580 male and female students from Al-Qasemi Academy. A sample of 150 kids was selected by researchers, with 10 female students participating in the interviews. The results of this study show a moderate level of computer competency availability required for utilizing artificial intelligence. It is suggested that training pre-service teachers in the essential AI application skills is necessary for practical implementation.

1. Introduction

The information revolution is transforming education, necessitating a focus on teacher development and learning processes. Teachers must adapt to the digital age by upgrading teaching methods and strategies, particularly embracing technology applications like AI, to ensure education aligns with the evolving needs of the era. AI serves as a powerful tool for interactive digital learning, aiming to grasp the essence of human intelligence by creating computer applications that mimic intelligent human behavior. Whether assisting in specific school tasks or software development, it includes various domains such as personalized learning, language interaction, understanding child learning stages, and educating individuals with special needs in tailored educational settings.

The integration of AI in schools has proven highly advantageous, offering substantial support to students with special needs in turning their fantasies into reality. Drawing from educational studies, it is now deemed essential for pre-service teachers to leverage AI technologies in teaching individuals with special needs, aiding their learning process, and enhancing their social and collaborative skills.

AI technologies are a crucial field that captivates the attention of scientists and researchers, witnessing ongoing advancements that hold key implications for the future of humanity. This science revolves around creating machines that engage in intelligent behaviors, with applications spanning across diverse sectors like education, healthcare, modern technologies, and the social sciences. Consequently, major corporations such as Google, Facebook, and others are now investing in AI research to develop efficient, precise, and rapid solutions across various human-interfacing domains [1].

1.1. Problem of the Study

The problem of the study lies in tackling the computer competencies needed for employing AI in SES Schools from the perspective of pre-service teachers.

1.2. Aims of the Study

This study aims at:

• exploring the tendencies of pre-service teachers towards employing AI while serving at SES.
• exploring their tendencies towards AI in their schools according to the variables of gender, major, academic achievement.

1.3. Questions of the Study

The main question of this study is:

What are the computer competencies needed to employ AI in SES from the perspective of pre-service teachers?

From this question, the following questions should be answered:

• How well-equipped are pre-service teachers in SES with the computer competencies required to implement AI?
• What are the challenges facing the application of AI in SES?
• What are the attitudes of pre-service teachers towards employing AI in SES?
• Are there statistically significant differences in the degree of availability of computer competencies necessary to employ AI in SES among pre-service teachers due to variables of gender, major, and level of achievement?

1.4. Interview Questions

• Demonstrate your tendencies as a pre-service teacher towards employing the applications of AI in education?
• What is your previous experience as pre-service teachers in implementing AI application in education?
• Describe the required technological competencies for students in order to employ AI application in teaching?
• What are the challenges pre-service teachers face in the implementation of AI applications in communication, students’ participation motivation, teaching AI ethics, assessment, moral motivation.

1.5. Hypotheses of the Study

• There are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-service teachers towards employing AI in the special education environment due to the variable of gender.
• There are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-service teachers towards employing AI in the special education environment due to the variable of major.
• There are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-service teachers towards employing AI in the special education environment due to the variable of educational achievement.

1.6. Significance of the Study

This study empowers special needs students and enables them to be independent and productive, along with highlighting their individual differences by employing AI applications in order to help them read text to them loudly.

Hence, it will enable college students to bridge the gaps in students’ needs in crowded classrooms, making them aware of their progress and enabling them to modify classes according to the needs of every learner.

It will also attract educators’ attention to develop the skills of interactions between pre-service teachers, making the AI technology the most effective method, which will increase students’ motivation towards learning because this technology is more than a set of tools.

2. Previous Studies

2.1. The Emergence of Artificial Intelligence

AI, which is a term coined in the 1950s by the scientist Allen Torengh, marked a significant milestone in 1950 when he devised a computer intelligence test, evaluating its smartness based on its ability to mimic human cognition. The formal proclamation of the concept of AI came in 1956 from scientist Jhon Macarthy, who convened two-month workshop uniting researchers keen on artificial networks. The dawn of the twenty-first century witnessed remarkable advancements in AI, paving the way for interactive robots now readily available in stores [1].

2.2. The Significance of Artificial Intelligence

AI plays a crucial role in enhancing the performance and productivity of organizations by automating tasks that previously required human effort. AI also has the ability to comprehend data on a vast scale beyond human capacity. AI is important since it:

• helps in preserving human expertise by transferring it to smart machines.
• allows humans to interact with machines using natural language, making machines more accessible to a wider range of people, including those with special needs, who were previously excluded due to the complexity of computer programming languages.
• has the potential to revolutionize scientific research, facilitating new discoveries and accelerating progress across various scientific fields.
• brings numerous benefits to humans across various areas by enabling computers to replicate the intelligence processes that take place in the human mind. This grants computers the ability to solve intricate problems and make swift, logical decisions akin to human thinking [2].

First: Computing Competencies Needed to Employ Artificial Intelligence in Special Education Schools:

Clearly, digital competency encompasses more than just proficiency in operating devices and programs; it is also closely intertwined with the ability to communicate using technologies and digital skills. It should include a balanced view of technology for responsible and healthy use of digital technology. Knowledge and attitudes about privacy and security, legal and ethical considerations, and the role of digital technologies in society should be included [3].

The researchers define computer competencies as the ability of pre-service teachers to deal with modern technology by using it in their field of work, employing it in creating files, creating electronic lessons, taking tests, and correcting them. This includes a set of skills and concepts with which pre-service teachers can complete their work without facing any problems or difficulties. These skills and concepts that pre-service teachers acquire as a result of their training in educational programs before and during their work improve their performance levels.

AI in education involves creating computerized learning environments similar to real ones, requiring student interaction with educational material. Adaptive learning, a type of learning, aims to adapt content to all students, reducing academic burden and increasing efficiency, particularly in science and mathematics, enhancing student learning [4]. It is also necessary to use AI systems that rely on the involvement of AI experts and curriculum specialists in the preparation of educational materials. These systems rely on four main modules, namely: problem solving module, experience module, student model module, teaching module, and user interface.

The content of this study in these systems consists of objectives, content, activities, and evaluation procedures. Students and teachers can also use mini-worlds, which is a miniature teaching system with the use of AI applications. AI applications such as machine learning or computer modeling can also be used to design a special educational evaluation system to evaluate all aspects of the educational process instead of traditional evaluation methods, as it includes the learner’s personality, perseverance, and attitudes towards learning [5].

Industrial robots also appeared as one of the examples of artificial intelligence applications that have the ability to interact with the real world, including those that work independently and those that work under human control [6].

Artificial intelligence applications may be used as tools for diagnosing, integrating, and intervening in some of the learning difficulties that appear in special education students [7]. Some of these applications may contribute to improving the ability of the teacher or parents to deal with special education children, evaluate and improve their learning, and monitor their levels of academic development, which helps to make future decisions to deal with any academic delay or to deal with each case separately according to the individual differences of the students [8].

The field of ‘Special Educational Needs’ covers a large number of difficulties that can cause problems during the learning process. Recent developments in the area of artificial intelligence and special education may enable the development of collaborative interactive environments and facilitate the lives of individuals with special educational needs and the people around them [9].Special education services include all categories of students who face difficulties that negatively affect their ability to learn. These categories include students with special talents, outstanding students, and students with mental, physical, sensory, visual, hearing, autism, learning difficulties, and other disabilities [10] This important group is most in need of educational services that help them become independent in learning and adapting to circumstances, and applications of artificial intelligence may create a revolution in this field, as many specialists see [11].

Secondly, The Challenges Facing the Application of Artificial Intelligence in Special Education Schools:

Education faces numerous challenges, including the coronavirus, population growth, and environmental issues. External challenges include technology, globalization, and democracy, while internal ones include cultural diversity, work pressures, and digital infrastructure. Skills like creativity and critical thinking are crucial for AI-assisted learning, as pre-service teachers face challenges in meeting the needs of exceptional students.

The AI interventions and applications required can indeed vary significantly across different areas of disability in special education. The applications of AI suitable for some major disability categories such as visual impairments which include text-to-speech, object recognition, 3D modeling, and navigation aids [11]. It also includes learning disabilities including intelligent tutoring, reading comprehension support, and gamification. Hence, autism spectrum disorder includes VR social environments, emotion recognition, robot-assisted instruction [12]. In addition, other categories include intellectual and physical/mobility disorder. The former includes task prompting, simplified UIs, augmented reality training [13] while the latter includes voice controls, alternative input methods, mobility assistance [14].

Students with visual impairments are unable to see printed material and cannot use digital learning during their studies [15]. Many teachers are unaware of what AI is and its applications, and the Arabic technologies used are insufficient to cover the teaching of AI applications, in addition to the need to train teachers to use social intelligence applications with students in special education environments, as well as the difficulty of dealing with AI applications in the educational environment and the weak technological structure [16].

Thirdly: Student Teachers’ Attitudes Towards the Use of Artificial Intelligence in Special Education Schools:

AI applications are widely used in learning environments and in the educational field, as they help enable students with special needs to gain independence and productivity. And because the teachers are not specialists in the field of artificial intelligence, therefore, artificial intelligence in education is not limited only to educational content but is much more than educational content that includes the integration of some applications in the field of artificial intelligence, especially for educational students [17] The matter goes much beyond that, as these emerging technologies are developing very quickly, such as ChatGPT and Sora. With generative AI tools, there was a need to refer to specialists in these applications to learn about ways to apply them and benefit from them in education, especially in the field of special education [18].

AI techniques have been gradually used to improve the lives of individuals with special needs, covering a large number of difficulties and causing problems in learning, cognition, communication, behavior, emotion, sensory, and physical development [19]. Using artificial intelligence, study information can be perceived in new ways through visualization, simulation, and a web-based study environment. Learning content updates: Moreover, AI helps in preparing the content of lessons, keeping the information up-to-date, and making it adaptable according to different learning curves [20].Some of the most typical AI applications in the educational field involve know-edge representation, intelligent tutoring, natural language processing, autonomous agents, etc. AI offers effective support for online learning and teaching, including personalized learning for students, automated instructions, routine tasks, and powering adaptive assessments. Robotics with AI can be used to support, assist, and augment teaching professionals. Appropriate assistive technology enables students with disabilities to compensate for certain impairments [21]. This specialized technology promotes independence and decreases the need for other support. AI can dramatically improve the efficiencies of our workplaces and augment the work humans can do. AI helps teachers with detaching and teaching, including personalized learning for students, automated instructions, routine tasks, and powering adaptive assessments. Robotics with AI can be used to support, assist, and augment teaching professionals. Appropriate assistive technology enables students with disabilities to compensate for certain impairments. This specialized technology promotes independence and decreases the need for other support [22].

AI applications are widely used in learning environments and in the educational field, as they help in enabling students with special needs to gain independence and productivity. They are also applied to help the blind by reading text to them aloud and recognizing people and their emotions, as blind people can explore the world around them through three-dimensional images.

Also, the application of AI technologies will help in monitoring students’ performance and achievement and will be a communication tool between pre-service teachers. Also, AI technologies will make education more effective and interactive and will contribute to increasing students’ motivation towards learning because AI applications are more than just learning tools [23].

Pre-service teachers’ attitudes on artificial intelligence have been investigated. In the current qualitative study, done using the phenomenology design, data were collected from 94 pre-service teachers attending various departments at Manisa Celal Bayar University, Faculty of Education in Turkey during the 2018–2019 academic year autumn semester. Data were acquired using the author’s semi-structured and written interview forms. Collected data were evaluated using the content analysis method and categorized into topics. Analyses revealed that pre-service teachers attached diverse meanings to artificial intelligence, had primarily negative sentiments about it, and did not wish to live in a future dominated by artificial intelligence. Furthermore, pre-service teachers believed that artificial intelligence might have both beneficial and negative consequences on education. Based on the study’s findings, several recommendations for future research and implementations on the subject were made [24].

AlKanaan conducted a study to determine whether pre-service science teachers were aware of the use of artificial intelligence in science education. It also intended to identify the factors that contributed to this level of pre-service science teachers’ awareness of using AI in science education. The mixed method was employed in conjunction with an interpretative sequential design. The researcher created a measurement and gave it to all forty-three pre-service science teachers. In terms of qualitative methodology, interviews were performed with a sample of 15 female pre-service teachers. The quantitative analysis found a poor degree of awareness among pre-service science instructors on the use of AI in science teaching [25].

The qualitative interview data were utilized to explain the quantitative study’s findings. There are statistically significant variations, at a level of confidence of 0.01, between the hypothetical and real means of the overall score of pre-service science teachers’ awareness of using AI in science education, as well as in terms of dimensions. The discrepancies favor the hypothetical mean.

Another study was conducted by Lee and his colleagues aimed to better understand in-service teachers’ opinions of AI education for teaching in schools and their AI teacher training programs. Data was gathered using an online survey (n = 20) and a focus group interview (n = 4). The poll looked into teachers’ impressions of AI education for teaching in the classroom, whereas the focus-group interview looked at teachers’ perceptions of AI teacher training programs. The survey results revealed that teachers have positive sentiments regarding AI education for both teaching and future application. The interview findings highlight the benefits and drawbacks of AI teacher training programs. Although the majority of teachers indicated pleasure, there was a strong need for a more practice-oriented curriculum [26].

Specifically, respondents discovered that many theory-centric courses failed to provide relevant skills or knowledge for real-world classroom settings. The findings of this study are original and distinctive in that understanding teachers’ opinions on AI convergence education for K-12 is critical for teacher education programs. Thus, this study can provide useful insights and recommendations for better structuring teacher professional development in AI convergence education, particularly in different higher education environments.

In addition, the study of Agrusti & Bonavolontà elaborated that their study is part of a particularly new and demanding research trend that aims to bridge a knowledge gap in the viewpoints and concerns of learning support teachers on educational robotics. Specifically, by interrogating teachers who have not been trained in the use of educational robotics. In this exploratory study, 125 Italian pre-service learning support teachers were assessed at the end or during their course specialization to assess their knowledge and understanding of educational robotics use with kids with special needs. The poll finds that support teachers are inadequately educated to use educational robotics, are typically unfamiliar with it, and rarely use it [27].

Hence, Molefi and his colleagues studied how in-service teachers in Lesotho accept and use artificial intelligence (AI), with a focus on the mediating function of school support and resources (SSR). In Lesotho’s educational landscape, which is marked by a growing interest in technology integration, this study fills an important gap in the existing literature by investigating in-service teachers’ opinions on AI adoption and the mediating role of SSR. The study uses the Unified Theory of Acceptance and Use of Technology (UTAUT) as its theoretical framework and employs a cross-sectional approach to collect data from a sample of 315 in-service instructors using online surveys. The data were examined using maximum likelihood estimation [28].

Still, Ayanwale and his colleagues investigated AI literacy among 529 pre-service instructors at a Nigerian institution, using structural equation modeling (SEM) for thorough analysis. It investigates many aspects of AI literacy and finds that a thorough grasp of AI greatly predicts favorable outcomes in AI use, detection, ethics, creation, and problem-solving. However, there is no association between AI knowledge and emotion control, nor is there any evidence that active AI use improves AI detection capacities. It reveals a trade-off between AI application and creation, focusing on ethical concerns as well as emotional and persuasive aspects of AI use. It also promotes the link between AI creation and issue solving, emphasizing the importance of AI knowledge in shaping various areas of AI literacy in pre-service teachers. The findings provide useful information for educators, administrators, legislators, and academics looking to improve AI literacy in pre-service teacher education programs [29].

Which components new generations of teachers’ digital competencies should have and how this field can be effectively fostered within academic education have been discussed. The essay is based on interviews with 35 specialists (known scholars in the field of media pedagogy) from North and South America, Africa, Australia and Oceania, Asia, and Europe. From June to September 2022, qualitative interviews were performed using the Delphi approach. Using grounded theory principles, it was noted that there is a need to modify pre-service teachers’ curricula in the following areas: creating, retrieving, and managing digital content; integrating ICT into subject content; developing soft skills; enhancing interactivity in the classroom; social aspects of ICT; distance learning; new socio-technical processes; advanced digital competencies; predictive learning; analyzing the impact of ICT on human life; data protection [30].

2.3. Research Methods

The researchers employed the mixed approach including the qualitative and quantitative approaches for its appropriateness for this study.

2.4. The Community of the Study

The study’s community at Al-Qasemi Academy’s Special Education Department comprised all student teachers, amounting to 580 students. These students are enrolled at Al-Qasemi Academy—West Bay Branch within the Green Line for the academic year 2023–2024, spread across different academic years (1st–4th) as per their academic records.

2.5. Sample of the Study

The sample of the study randomly included pre-service teachers from the specialization of Special Education (SE). The overall size of the sample was (150) male and female students as shown in

Table 1. The distribution of the sample of the study according to independent variables:

Variables	Categories	No.	Percentage
Gender	Male	11	8%
	Female	127	92%
Major	Pure Major of (SE)	70	51%
	Other majors with SE certificate.	68	49%
Educational Achievement	Good (75–84)	22	16%
	Very Good (85–94)	92	67%
	Excellent (95–100)	24	17%

.

3. Tools of the Study

3.1. First: A Questionnaire

The researcher used the questionnaire as a tool to collect data related to her topic, with the aim of identifying the degree of computer competencies needed to employ AI in special education schools among student teachers. After reviewing the theoretical literature and previous studies related to the topic of the study, including the study of Ojha [18], the questionnaire consisted of three sections:

• The first section included the title of the study and the introduction of the questionnaire, which contains a set of elements that define the purpose of the study and adherence to the ethics of the study by the researchers.
• The second section: The personal data of pre-service teachers involved in filling out the data, introduced as independent variables in the study, namely: gender, year of study, major.
• Third section: (30) paragraphs of the questionnaire related to exploring the degree of computer competencies needed to employ AI in SES among student teachers, and these items were distributed in four domains (knowledge of AI applications, AI applications, challenges of using AI applications, and the trend towards employing AI applications).

  Table 2. The stability of the domains of the questionnaire and the overall degree:

  No.	Dimension	Stability Coefficient
  1	Students’ tendencies towards employing AI in SES	0.750
  2	Computer Competencies in employing AI applications.	0.863
  3	AI implementation.	0.743
  4	The challenges of employing AI	0.914
  Overall, Degree		0.963

  shows the areas of the questionnaire and the number of paragraphs for each dimension:

It is obvious from Table 2 that the stability coefficient of the sum of the domains is (0.963) which is a high degree indicating the stability of the domains and their appropriateness for the purposes of the study.

3.1.1. The Validity of the Questionnaire

The questionnaire was presented to a group of referees with experience in the field of AI, including experts in Palestinian universities and technology teachers in some schools in the Directorates of Education, and the questionnaire was modified based on their comments. A phenomenological study approach was adopted, as it aims to understand the computing competencies needed to employ AI in special education schools among student teachers. This approach may lead to the categorization of the computing competencies needed to employ AI among students and teachers.

3.1.2. Participants of the Study

The study, conducted in 2024–2023, involved 10 female special education pre-service teachers at Al Qasimi Academy, using social constructivism to build knowledge and develop attitudes towards using AI applications in the classroom. Purposeful sampling was used to select relevant participants.

A phenomenological study should include between 3 and 10 subjects. Therefore, the current study will include 10 student interns selected for this phenomenological study. The participants were semi-randomly selected to complete this study as demonstrated in

Table 3. The participants in the study according to their demographic variables:

Name	Gender	Major	Age	Academic Achievement	Residence	Uni. Year
Amal	Female	Special Edu.—Arabic	23	Excellent	Zemar	3rd
Adeba	Female	Special Edu.—Arabic	20	Good	Al-Tira	3rd
Rawan	Female	Special Edu.—Arabic	20	Excellent	Qalanswa	3rd
Adan	Female	Special Edu.—Arabic	21	Excellent	Al-Tira	3rd
Maram	Female	Special Edu.—Arabic	24	Good	Qalanswa	3rd
Mona	Female	Special Edu.—Arabic	25	Excellent	Zemar	3rd
Saja	Female	Special Edu.—Arabic	21	Good	Al-Tera	3rd
Thara’	Female	Special Edu.—Arabic	22	Good	Baqa Al-Gharbiya	3rd
Heba	Female	Special Edu.—Arabic	20	Excellent	Ara	3rd
Asma’	Female	Special Edu.—Arabic	23	Good	Al-Tera	3rd

.

3.2. Secondly: Interviews

The study used semi-structured interviews to gather data on the computing competencies needed for pre-service teachers to use AI in SES. The interviews were conducted using Zoom software and aimed to understand the challenges faced by teachers, such as communication, creativity, ethics, evaluation, and moral encouragement. Participants were recorded and asked questions repeatedly to ensure they fully communicated their thoughts.

The Tools of the Analysis of the Interviews involved experienced analysts from Al-Qasemi Academy’s Special Education Department, who interpreted data and transcribed interviews. Thematic analysis was used to identify patterns and assess computer skills for integrating AI among student teachers, ensuring accurate interpretation and coding.

Also, to maintains data analysis credibility, the researchers employed the agreement between reviewers, pinpointing repetitions and connections between categories and subcategories. The reviewers’ agreement stood at 0.87, a reliable measure ensuring the study’s validity.

4. The Results of the Quantitative Analysis

4.1. Statistical Analysis

The researchers utilized the Statistical Package for the Social Sciences (SPSS) to analyze the data, addressing study questions by calculating averages, standard deviations, and relative importance (ranking), conducting independent sample t-tests, and performing one-way ANOVA analysis. They relied on the following statistical standard to interpret the sample individuals’ estimates as demonstrated in

Table 4. The statistical measurement of explaining the degrees of the individuals of the sample.

Degree	Range	Availability Degree
5	4.20–5.00	Very High
4	3.40–4.20	High
3	2.60–3.40	Moderate
2	1.80–2.60	Low
1	1–1.80	Very Low

.

4.1.1. First: Results Related to the Answer of the First Research Question

How well-equipped are students and teachers in SES with the computer competencies required to implement AI?

To answer this question, the moderate scores and standard deviations for the study domains were calculated, with

Table 5. Average scores, standard deviations, rank, and the level of computer skills availability required for implementing AI in SES among students and teachers in each domain of the study tool.

No.	Domain	Average	SD	Rank	Availability Degree
1	The competencies of the implementation of AI applications	3.67	0.37	2	High
2	Employing AI	3.82	0.26	1	High
Total Degree			0.31		High

presenting these findings.

It is apparent from Table 5 that the degree of availability of computer competencies necessary to employ AI in SES among pre-service teachers in general was high, as the arithmetic average reached 3.56 and the standard deviation was 0.31. The field of competencies for employing AI applications came in first place with a mean of (3.82) and a standard deviation of (0.26), and in second place was the field of employing AI with a mean of (3.67) and a standard deviation of (0.37).

As for the items in each domain, the results were as follows:

The First Domain: Competencies For Employing AI Applications:

Arithmetic means and standard deviations were calculated, and the rank and degree were determined for each item in this domain as shown in

Table 6. Arithmetic means, standard deviations, and relative importance of the domain of competencies for employing AI applications.

No.	Item	Arithmetic Mean	SD	Rank	Degree of Availability
1	I know the concepts and terminology related to AI applications	3.76	0.52	2	High
2	I am confident in my abilities to recognize AI applications and employ them for students with special needs	3.78	0.49	1	High
3	I am confident in my ability to identify a set of AI applications that can be used with students with special needs	3.73	0.57	3	High
4	I know how to use many artificial intelligence applications to help students with special needs.	3.62	0.60	7	High
5	Follow a systematic plan to ensure the effectiveness of the use of AI applications in education.	3.63	0.61	6	High
6	I have the ability to find development resources related to AI applications.	3.70	0.56	4	High
7	I can solve the technical problems I face when using AI applications in education.	3.66	0.61	5	High
8	I have received adequate training in the use of AI technologies in education.	3.15	0.81	8	Moderate
Total Degrees		3.67	0.37		High

.

Table 6 shows pre-service teachers’ availability of computer competencies for AI in SES. The first item, “I trust my abilities to recognize AI applications and employ them for students with special needs”, ranks highest, while the lowest is “I received sufficient training on AI techniques in education”.

The Second Domain: Employing AI

Arithmetic means and standard deviations were calculated, and the rank and degree were determined for each item in this domain as shown in

Table 7. Arithmetic means, standard deviations, and relative importance to the field of AI employment.

No.	Item	Arithmetic Mean	SD	Rank	Degree of Availability
1	AI applications facilitate the completion of work with the least possible time, effort, and cost.	3.80	0.48	6	High
2	AI applications contribute to changing the role of the student from a recipient of knowledge to a seeker of knowledge.	3.81	0.42	5	High
3	Employing AI applications in learning increases communication between students with special needs and student teachers.	3.86	0.37	2	High
4	Educational AI applications reduce the psychological barrier among students with disabilities & special needs towards the learning process (Shame, for example)	3.68	0.57	8	High
5	AI applications contribute to the development of research skills of students with special needs.	3.82	0.37	4	High
6	Educational applications contribute to AI applications in developing thinking skills of students with special needs.	3.84	0.36	3	High
7	AI applications help students with special learning needs.	3.77	0.46	7	High
8	AI applications provide a kind of liveliness and attractiveness on the presentation of the educational process.	3.87	0.37	1	High
9	AI applications motivate student with special needs to increase the level of motivation towards the teaching and learning process.
Total Degrees		3.81	0.26		High

.

Table 7 reveals that pre-service teachers have varying levels of computer competencies for using AI in special education schools. The first item, “AI applications stimulate students with special needs to increase motivation”, has the highest availability, while the second item, “educational AI applications reduce the psychological barrier of students with special needs”, has the lowest availability.

4.1.2. Secondly: Results Related to the Answer to the Second Question

What are the challenges facing the application of AI in SES?

To answer the second question, the arithmetic averages and standard deviations for the domains of study were extracted as demonstrated in

Table 8. Arithmetic means, standard deviations, and grades for the challenges facing the application of AI in special education schools among pre-service teachers.

No.	Item	Arithmetic Mean	SD	Degree
1	Resistance to the use of AI applications by teachers in school.	3.51	0.64	High
2	Lack of awareness among some decision-makers in schools of the importance of AI applications.	3.64	0.59	High
3	Arab applications that serve education are insufficient in AI applications.	3.65	0.65	High
4	The educational technologies available for AI applications are not compatible with the characteristics of the target group of people with special needs.	3.48	0.69	High
5	Lack of sufficient experience to deal with AI applications by student teachers	3.56	0.69	High
6	The need to train students and teachers to use AI applications.	3.80	0.46	High
7	Difficulty dealing with AI applications in the educational process.	3.27	0.78	Moderate
8	AI applications are not suitable for current teaching methods used in schools.	3.23	0.79	Moderate
9	Weak technological infrastructure in some schools	3.60	0.65	High
10	The technical support provided is disproportionate to the importance of using parameters for AI applications.	3.52	0.66	High
11	There are no material or moral incentives for those who use AI applications in education.	3.40	0.78	High
12	I do not have enough time to use AI applications in education.	2.93	0.80	Moderate
13	The high prices of AI applications prevent schools from purchasing them.	3.55	0.60	High
14	I am concerned about using AI-based applications in teaching.	3.03	0.80	Moderate
15	The use of AI technologies within the classroom will be a distraction for students with special needs.	3.08	0.81	Moderate
Total Degrees		3.80	0.45

.

It is noted from Table 8 that the arithmetic averages for ten of the items that express the challenges facing the application of AI in SES among pre-service teachers were high, as these items are (1, 2, 3, 4, 5, 6, 9, 10, 11, 13). The results showed that the item (the need to train pre-service teachers on using AI applications) had the highest arithmetic mean (3.80), while the results showed that the rest of the paragraphs that express the challenges facing the application of artificial intelligence in special education schools for pre-service teachers came in at a moderate degree, and these paragraphs are (7, 8, 12, 14, 15), where the item (I do not have enough time to use AI applications in education) came with the lowest arithmetic mean (2.93).

4.1.3. Third: Results Related to the Answer to the Third Question

What are the attitudes of students and teachers towards employing AI in SES?

To answer this question, the researchers calculated the arithmetic means and standard deviations for the items in the field (attitude towards employing AI applications). The following criterion was adopted in determining the degree of pre-service teachers’ attitudes towards employing AI in SES at three levels (low, moderate, high), which is based on subtracting the upper value of the response scale on a five-point Likert scale from the lower value (5 − 1 = 4), then dividing the result by the number of levels (4/3 = 1.33), and then adopting the following criterion in judging the attitudes of pre-service teachers towards employing AI in SES.

It is noted from

Table 9. Arithmetic means, standard deviations, and rank for the level of pre-service attitudes toward applying AI in SES.

No.	Item	No.	Arithmetic Average.	SD	Rank
1	AI applications help me monitor the performance and achievements of students with special needs	90	3.81	0.45	High
2	AI applications are an auxiliary communication tool between me and students with special needs	89	3.78	0.46	High
3	The use of AI applications makes the teaching process more convenient.	78	3.82	0.45	High
4	I prefer teaching using techniques based on AI applications.	93	3.66	0.53	Moderate
5	The information provided by AI applications lacks quality.	109	3.12	0.80	Moderate
6	The use of AI applications in education helps increase the motivation of students with special needs.	88	3.76	0.47	High
7	I advise my fellow students and teachers to use AI applications in education to be more effective.	65	3.80	0.40	High
Total Degree		118	3.60	0.44	Moderate

that the level of pre-service teachers’ attitudes towards applying AI in SES was moderate, as the arithmetic average for the overall domain reached (3.60), and all items came in the moderate and high levels, where the arithmetic averages ranged between 3.82 and 3.12), and the item (The use of AI applications makes the teaching process more interactive) came in first place with a arithmetical average of (3.82), while the item (The information provided by AI applications lacks quality) came in last place with an arithmetical average of 3.12. came in last place, with an arithmetical average of (3.12).

4.1.4. Fourth: Results Related to the Answer to the Fourth Question

Are there statistically significant differences in the degree of availability of computer competencies necessary to employ AI in SES among pre-service teachers due to variables of gender, major, and level of achievement?

To answer the second question, the researchers tested the following hypotheses:

The first hypothesis is that there are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-service teachers towards employing AI in the special education environment due to the variable of gender.

To test this hypothesis, the arithmetic means and standard deviations were calculated for all axes of the questionnaire for each level of the variable and the results as showed in

Table 10. Arithmetic means, standard deviations, and a t-test for two independent samples to examine the significance of differences in the degree of availability of computer competencies necessary to employ AI in SES among pre-service teachers according to the variable of gender.

Domain	Gender	No.	Arithmetic Mean	SD
Competencies for employing AI applications	Female	9	3.66	0.34
	Male	119	3.67	0.38
Employing AI	Female	8	3.64	0.30
	Male	108	3.83	0.25
Total Degree	Female	11	3.46	0.31
	Male	124	3.57	0.32

.

In general,

Table 11. Results of the t-test for differences between the average of the responses of males and females on the domains of the questionnaire.

Domain	(T) Value	Level of Statistical Significance
Competencies for employing AI applications	1.02	0.31
Employing AI	0.07	0.93
Total Average	1.85	0.66

shows that the averages of females were higher than the averages of males in all dimensions of the study, referring to Table 5 No statistically significant differences appeared in all dimensions of the study and the overall dimension, as the value of statistical significance for the overall domain reached (0.66), which is a value that is not statistically significant.

Second Hypothesis: To answer the second question, the following hypothesis was formulated: There are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-service teachers towards employing AI in the special education environment due to the variable of major.

To test this hypothesis, arithmetic means and standard deviations were calculated for all areas of the questionnaire for each level of the variable and the results as shown in

Table 12. Arithmetic means, standard deviations, and rank for the level of pre-service attitudes toward applying AI in (SE).

Domain	Major	No.	Arithmetic Mean	SD
Competencies for employing AI applications	Pure Major of (SE)	66	3.66	0.36
	Other majors with SE certificate.	62	3.68	0.39
Employing AI	Pure Major of (SE)	61	3.81	0.27
	Other majors with SE certificate.	55	3.83	0.25
Total Degree	Pure Major of (SE)	70	3.54	0.34
	Other majors with SE certificate.	64	3.58	0.28

.

In general,

Table 13. Results of the t-test for the differences between the average responses of the study sample on the domains of the questionnaire.

Domain	(T) Value	Level of Statistical Significance
Competencies for employing AI applications	0.37	0.71
Employing AI	0.39	0.03
Total Average	0.82	0.41

shows that the averages of pre-service teachers with a pure major (SE) were higher than the averages of students and teachers with other majors with a SE certificate in all dimensions of study, including the overall domain. As for Table 5, there were no statistically significant differences in the domain of competencies for employing AI applications or the overall domain, while it showed statistically significant differences in the domain of employing AI in favor of students with a pure major of (SE).

The Third Hypothesis: There are no statistical differences at the statistical level (α = 0.05) among the averages of the tendencies of pre-serviced teachers towards employing AI in the special education environment due to the variable of educational achievement.

To test this hypothesis, arithmetic means and standard deviations were calculated for all domains of the questionnaire, and the results were as demonstrated in

Table 14. Means and standard deviations of the questionnaire dimensions according to the achievement level variable.

Domain	Level of Achievement	No.	Arithmetic Means	SD
Competencies for employing AI applications	Good (75–84)	18	3.60	0.33
	Very Good (85–94)	78	3.63	0.45
	Excellent (95–100)	22	3.51	0.48
Employing AI	Good (75–84)	18	3.75	0.29
	Very Good (85–94)	77	3.85	0.24
	Excellent (95–100)	21	3.75	0.28
Total Degree	Good (75–84)	22	3.47	0.41
	Very Good (85–94)	90	3.59	0.29
	Excellent (95–100)	23	3.50	0.27

.

The researchers used one-way analysis of variance (One-way ANOVA) to examine this hypothesis and the results as shown in Table 14.

The averages of the questionnaire domains according to the achievement level variable ranged between (3.85 and 3.47, as shown in Table 14. The results showed that the highest averages were in the domain (the importance of employing artificial intelligence) for the level (very good).

There are no statistically significant differences between the averages of the sample’s responses to all domains and the overall domain, with the exception of the domain (competencies for employing AI applications), which showed a statistical significance of (0.00) as showed in

Table 15. One-way analysis of variance to examine the differences between the averages of the sample members’ responses according to the achievement level variable.

Domain	Source of Variance	Sum of Squares	Degree of Freedom	Mean Squares	F Value	Significance Level
Competencies for employing AI applications	Between Groups	1.71	2	0.85	6.51	0.00
	Within Groups	16.44	125	0.13
	Total	18.15	127
Employing AI	Between Groups	0.23	2	0.11	1.76	0.17
	Within Groups	7.61	113	0.06
	Total	7.85	115
Total Degree	Between Groups	0.37	2	0.19	1.91	0.15
	Within Groups	13.09	132	0.09
	Total	13.47	134

.

To identify the source of the differences, the Scheffé test was used for post-hoc comparisons, which showed the presence of significant differences. Statistics at the level of significance (α = 0.05) between the responses of the study sample with the achievement level (very good) on the one hand and the responses of the study sample with the achievement level (excellent) and in favor of (very good) as shown in

Table 16. One-way analysis of variance to examine the differences between the averages of the sample members’ responses according to the achievement level variable.

Domain	Source of Variation	Sum of Squares	Degree of Freedom	Mean Squares	F Value	Significance Level
Competencies for employing AI applications	Between Groups	1.71	2	0.85	6.51	0.00
	Within Groups	16.44	125	0.13
	The Total	18.15	127
Employing AI	Between Groups	0.23	2	0.11	1.76	0.17
	Within Groups	7.61	113	0.06
	The Total	7.85	115
Total	Between Groups	0.37	2	0.19	1.91	0.15
	Within Groups	13.09	132	0.09
	The Total	13.47	134

.

5. Interview Results

The researchers conducted a number of semi-structured interviews with the study sample with the aim of obtaining real and basic information about the subject of the study. Then the researchers analyzed the study sample’s answer qualitatively, with the aim of comparing the results of the interviews with the results of the questionnaire that was analyzed quantitatively and seeing points of agreement and points of difference.

5.1. Results of the First Question

Demonstrate your tendencies as a student/a trainee towards employing the applications of AI in education?

The results showed that employing AI applications in education in general is a very important and necessary matter, especially in the era of technology and its applications. It is an interesting field, as the present time is witnessing a great and rapid development of AI systems in various domains, especially education. It allows the teacher to use various tools that contribute positively to the education of students with special needs and in a way that is appropriate to their abilities and educational level, as the 3rd-year pre-serviced teacher A.S. mentioned:

“In fact, it is a very effective way of teaching students because they are students with special needs and special abilities, so they need applications like this in order to teach them correctly, appropriate to them and their levels.”

The sample members believe that using AI applications can customize the learning process, providing tailored educational content and increasing effectiveness. However, they express reservations about the process’s suitability for individual students.

This application must be simplified in order to achieve its goals, and it should not be applied at all stages of learning but rather according to the students’ needs. As the 3rd-year pre-service teacher, M.K., said:

“Applying AI in classrooms can be a good and different way, but not for a long time, and it must be used from time to time.". As for the 3rd year, pre-service teacher A said: “According to my opinion and after my experience, it is very important to employ AI in the learning process, but that the application be simple and appropriate to the students’ level and abilities, and we do not use programs and applications that are inappropriate and not simple for them.”

As for the 3rd year, pre-service teacher S. M. pointed out the necessity of using AI applications in a balanced way alongside traditional methods in education and that AI applications in education are not a complete substitute for direct interaction between students and teachers, as live discussions are still necessary to build and develop social skills.

“Overall, I see AI applications in education as an exciting opportunity to diversify and improve learning experiences. However, these applications should be used in balance with traditional methods.”

A number of pre-service teachers expressed that employing AI in the education process has many positives and many negatives. Among these positives,

As for some of the negatives mentioned by the study sample,

AI applications offer interactive learning experiences using augmented or virtual reality, personalized educational content for each student, and extensive study materials with examples, exercises, and reinforcement methods. Also, Teachers lack skills for AI applications, leading to boredom and uninterest in learning. Lack of internet and weak network in schools impact the effectiveness of these applications.

5.2. Results of the Second Question

What is your previous experience as a pre-service teacher in implementing AI application in education?

The study participants have limited experience with AI applications in education, introducing simple applications in their first year but not heavily relying on them. The 3rd year, pre-service teacher H.M., needs more training on AI integration. Where she added:

“But more work must be done on workshops that help us as female pre-service teachers to use AI application tools better, more broadly, and in many ways.”

As for ways to integrate AI applications into education, study participants pointed to a number of ways that AI-based videos, educational stories, lesson planning, examples, exercises, and translations are utilized to enhance students’ understanding of educational content and provide effective teaching methods.

Traditional educational strategies are viewed as the foundation, while AI applications can enhance learning through social interaction. AI applications have seen significant development, but pre-service teachers are necessary to create an interactive educational atmosphere, despite the need for tangible methods in the classroom. As the 3rd year pre-service teacher, M., mentioned:

“I use this wonderful technology in classes, which in turn contributed to introducing me to effective strategies that create a very different educational atmosphere that is close to the world of the students.”

The sample members also indicated the necessity of subjecting these applications to study and analysis, especially for students with special needs. The pre-service teacher, S.M., stated that she studied and analyzed student interaction during the class with educational applications based on AI, but she indicated a greater need for these applications to be subjected to analysis and study:

“I studied and experimented with personalized educational applications that use artificial intelligence to meet the needs of students with special needs. These experiences were very exciting, and I had the opportunity to explore how artificial intelligence can improve and adapt the educational process to effectively meet the needs of students.”

5.3. Results of the Third Question

Describe the required technological competencies for students in order to employ AI applications in teaching.

Teachers need high computer, design, and programming skills to effectively integrate AI applications into education, ensuring continuous practice and skill development for trainee students. The 3rd-year pre-service teacher, A.H., pointed out the need to determine when and how to use these applications:

“Otherwise, the trainee must have sufficient knowledge of how to use it, when, and how to achieve the learning objectives.”

As for the pre-service teacher A, she said:

The interview sample stressed the necessity of subjecting the pre-service teacher to focused and comprehensive training and keeping up with everything new in this field. In order to ensure that he has the necessary experience and competencies to employ artificial intelligence applications in education, this can only be done if a special budget is allocated by the Ministry of Education to qualify teachers in this field, as they suggested allocating special courses related to introducing students in universities and colleges during their studies to these applications and ways to employ them in education.

The participants in the study mentioned a number of skills and competencies that must be possessed by the teacher in the field of employing artificial intelligence applications, including, as they stated:

• The teacher’s possession of communication and communication skills, especially since these applications require high skills for communication and interaction between the teacher and his students, the teacher and his colleagues, and all educational parties.
• Teachers possess creative thinking skills and the ability to innovate, especially since they deal with a group of students with special needs.
• Teachers have the flexibility and ability to adapt these applications to ongoing developments.
• Pre-service teachers possess computer and technological competencies and sufficient technical knowledge in general.
• Teachers have the ability to automate administrative tasks, which contributes to developing and improving their computer competencies.
• Pre-service teachers possess the competencies to evaluate education and recognize the strengths and weaknesses of students in order to work to provide customized education for each student with special needs.
• Pre-service teachers possess educational design skills and the ability to develop educational applications and work to design innovative educational experiences that suit the needs of special education students.
• The ability to motivate students and attract their attention using AI applications.
• Identifying modern sources, websites, modern technological applications, and ways to benefit from them in education.

5.4. Results of the Fourth Question

What are the challenges teachers face in the implementation of AI applications in communication, students’ participation motivation, teaching AI ethics, assessment, and moral motivation?

The pre-service teachers pointed out that employing AI applications requires a balance between achieving the maximum benefit from these applications in education and confronting challenges and addressing them effectively. Teaching with the use of AI applications faces many challenges.

In terms of communication and participation:

Interviews reveal AI applications may hinder effective communication between pre-service teachers, as they may reduce the teacher’s role, causing students to focus on technology rather than the teacher’s explanation. This matter was pointed out by the pre-service teacher, M.K., who said:

“The problem with AI applications is that they often do not support the Arabic language, so the language may be unclear and thus affect students’ understanding of what they hear. Therefore, communication may be less and participation may be almost non-existent.”

Some have pointed out that AI applications effectively support the communication process between the student, the trainee, and the students themselves in the classroom, where friendly and comfortable relationships prevail among them. Participants in the study also stated that student interaction and participation in the classroom increase when artificial intelligence applications are employed.

In terms of stimulating creative thinking:

AI applications lack creative stimulation, posing challenges for teachers. pre-service Teachers should choose applications that encourage unconventional stimulation and human interaction, as mentioned by pre-service teacher A. G, said that:

“AI can make the student or teacher somewhat dependent because it makes it easier for the student and teacher to search for answers to the questions asked or to search for new electronic learning methods that dispense with the role of the teacher at times.”

In terms of the ethics of employing artificial intelligence applications:

The results showed that employing AI applications must be done ethically, respect students’ privacy, and take fairness into account when dealing with students. The interview sample indicated that students’ or teachers’ lack of understanding of how to apply technology may lead to ineffective or unethical use, or it may lead to neglect of human aspects, which affects the achievement of justice or equality, as mentioned by the pre-service teacher S. M., who said that:

“AI applications raise ethical issues related to privacy, discrimination, and justice. It is important to ensure that smart technologies are used in an ethical and responsible manner and to provide a safe and equal learning environment for all.”

In terms of evaluation:

The results showed that difficulties may arise in effectively integrating technology with delicate and sensitive human interactions in the classroom environment, and it may also be difficult for intelligent systems to determine performance standards effectively and fairly, which affects the accuracy of evaluation when employing artificial intelligence applications. Some members of the interview sample indicated that evaluation is not a blind process devoid of human relations, and therefore the lack of these applications in human factors may affect the performance of students, which is reflected in their evaluation. The pre-service teacher, M. G., said:

“Assessing student performance can be challenging, especially if applications rely heavily on numerical data without taking into account other human factors that may affect student performance.”

Also, teaching using these applications as an effective evaluation of students, as indicated by pre-service teacher R.A., said:

“Intelligent tutoring systems provide individual guidance and support to students, enabling them to assess their understanding, identify areas of difficulty, provide feedback, and provide them with additional exercises.”

In terms of motivation and moral encouragement:

The results showed that the lack of AI applications leads to social interaction between the teacher and students, which may greatly affect the understanding of students’ needs, which is reflected in their motivation to learn. Also, failure to take into account the teacher’s choice of appropriate applications for his students may lead to obstructing the student’s learning process, and this also reflects negatively on his motivation towards learning. However, some see that AI applications can develop a highly effective evaluation system that contributes to improving the learning process. As the pre-service teacher, R.A., pointed out:

“AI is used to develop smart assessment systems that provide immediate, timely personal feedback to learners, in addition to evaluating assignments and tests, which helps students identify points of improvement and receive effective suggestions that improve the results of the learning process.”

6. Discussion of the Results

The study’s findings revealed that the presence of essential computer skills required for implementing AI varied significantly across different sections—high in most, moderate in some, and low in others. However, it leaned towards the moderate range, reflecting the students’ average inclination towards AI applications. This is due to the numerous challenges educators face in employing AI, particularly in finding appropriate assessment methods.

When it comes to the challenges of implementing AI, they are substantial. Interviews have revealed that many teachers lack the necessary skills for applying AI. Research findings indicate a gender bias in AI applications, with females using these tools more frequently than males, recognizing their significance. In terms of the major of students, students with a primary major benefit more than those with a supplementary one, as do high-achieving students, aligning with interview outcomes. Female students participating in the study emphasized the importance of AI in education, expressing a preference for AI applications due to their positive impact on student learning, especially for those with special needs, as these tools cater to their unique learning abilities.

According to some students, integrating AI should complement traditional methods rather than replace them. They point out drawbacks to AI use, such as teachers lacking training in AI applications, the interconnection of AI with the internet, infrastructure, ethical concerns in AI deployment, and more. AI implementation could hinder communication and engagement by reducing teachers’ communication skills with students. It’s essential to train both students and educators to utilize AI effectively and address the potential drawbacks students might encounter. Consequently, the study offers various recommendations, including:

• introducing AI and its educational applications as a part of teacher training programs in higher education institutions.
• conducting training sessions for pre-service teachers to equip them with the skills to utilize artificial intelligence applications in education.
• enhancing and regularly updating computer labs in schools.
• providing guidelines on the integration of AI applications into the teaching process.
• formulating ethical guidelines for pre-service teachers regarding the use of AI in learning.
• establishing effective assessment tools to evaluate students’ progress using AI during the learning process.

7. Study Limitations

One of the most important limitations of this study is that it did not address the levels of pre-service student teacher training and their experiences according to the academic year of the study sample. This is because the target university in the study does not train pre-service teachers on artificial intelligence applications in a systematic and studied manner, as these future teachers do not study topics. Artificial intelligence and methods of applying it in their study programs independently, and they do not undergo special training in these applications; rather, these applications are specifically integrated into their curricula.

The other limitation indicates that this study is limited to what was measured only in the questionnaire through which the data was collected.