Artificial Intelligence for People with Special Educational Needs

Abstract

Artificial intelligence have very high capacities and capabilities in special education and have been strengthened and upgraded compared to the past. Especially in the field of education, teaching and learning, remarkable progress has happened and is happening. There are examples of artificial intelligence based on assistive technologies that can help people with special educational needs access and interact with educational content. In this chapter of the book, the opportunities and capabilities of artificial intelligence for people with special educational needs, particularly in teaching and learning process, have been introduced and analyzed. The types of assistive technologies based on artificial intelligence for disabled people have been explained. Also, in this chapter of the book, considering the importance of approaches and educational design models in the teaching and learning process of people with special educational and learning needs, the blended learning approach and model in special education have been introduced and its basic components and sub-components have been explained. At the end of the chapter of the book, case studies that have been done by author or by author’s supervision in this field have been mentioned.

1. Introduction

Perhaps the following classification of types of education and training can be provided:

• General or regular Education

• Special Education

• Organizational Education/Training

• Adult Education

• Lifelong learning and continuous professional development

What is meant from the types of education and training mentioned in this chapter of the book is special education. Special education is the study and practice of educating students in a way that adapts their individual differences, disabilities, and special needs. In other word, Special education is the study and ethical application of best practices to improve learning and performance of people with special educational and learning needs through the special strategic of analysis, designing, developing, production, implementation, management, support, assessment and evaluation of learning and instruction processes and resources [1]. Special education aim is to provide adapted education for people with special educational needs such as mental retardation, visual impairment, hearing impairment, physical-motor impairment, learning difficulties, emotional-behavioral impairment, speech-communication impairment, special diseases, multi handicapped and talented and gifted [2]. Some scholars of education may categorize gifted education under the umbrella of “special education”. Although there are still differences of opinion in this field. People with special educational needs include a wide variety of people with different cognitive, physical, emotional and behavioral learning needs.

New learning approaches such as blended learning approach and new technologies such as artificial intelligence have changed the fundamental nature of teaching - learning process particularly in special education areas. They have made possible learning activities that were not previously practical or feasible. A blended learning environment provides opportunities for the students with special educational needs to interact with their teachers and content. Various researchers have noted that there are limited studies investigating learning through blended learning approach among students with special educational needs [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. According to Allahi [13], Jangizehi [14], Mathews [15], Stamer [16], Toofaninejad [17], Moradi [18], Zaraii Zavaraki & Schneider [11], Zaraii Zavaraki [1], and Linda [12], blended learning approach and new technologies have some potential for special education teachers and their students.

2. Artificial intelligence in education

Artificial intelligence is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns.

Some of Artificial intelligence applications are:

• Advanced web search engines

• Recommendation systems

• Understanding human speech

• Generative or creative tools

• Competing at the highest level in strategic games

The traits described below have received the most attention and cover the scope of AI research:

Reasoning, problem-solving, Knowledge representation, Planning and decision making, Learning, Natural language processing, Perception, Robotics, Social intelligence, General intelligence.

Artificial Intelligence technology is used in most of the essential applications, including:

• Search engines

• Targeting online advertisements, recommendation systems

• Driving internet traffic, targeted advertising

• Virtual assistants

• Autonomous vehicles

• Automatic language translation

• Facial recognition

• Image labeling

The rapid development of Artificial Intelligence is having a major impact on all of areas of educational systems. Advances in AI-powered solutions carry enormous potential for the achievement of the sustainable development goals. Artificial Intelligence has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards sustainable development goal 4, quality education. Sustainable Development Goal 4 aims at ensuring inclusive and equitable quality education and promote lifelong learning opportunities for all [19]. Over the past decade, the use of AI tools to support or enhance learning has grown exponentially [20]. This has only increased following the COVID-19 school closures. However, evidence remains scarce on how AI can improve learning outcomes and whether it can help learning scientists and practitioners to better understand how effective learning happens [21]. AI applications designed for education have been divided into three main categories: system-facing, student-facing and teacher-facing [19]. However, for policy-makers, UNESCO propose a set of four needs-based categories of emerging and potential applications:

• Education management and delivery

• Learning and assessment

• Empowering teachers and enhancing teaching

• Lifelong learning [19].

Educational chatbots, OU Analyze, Swift, The ALP, The UniTime, Intelligent tutoring system, Dialog-based tutoring systems, Exploratory learning environments, Automated writing evaluation, AI-supported reading and language learning, Smart robots, Teachable agents, Educational virtual and augmented reality, Learning network orchestrators, AI-enabled collaborative learning, AI-driven discussion forum monitoring, AI-human ‘dual teacher’ model, AI-powered teaching assistants, AI-driven lifelong learning companion, AI-enabled continuous assessment, AI-enabled record of lifelong learning achievements, and opportunities for AI to advance inclusion and equity in education are some of applications of AI in education [19].

2.1 Artificial intelligence in special education

Artificial intelligence and its applications in the process of education, teaching and learning of people with special educational needs have been proposed at the global level under the two keywords of inclusive education and assistive technologies. In the continuation of this chapter, the inclusive education and then the assistive technologies have been introduced and explained.

2.1.1 Opportunities for artificial intelligence to advance inclusion and equity in education

In addition to focusing on equitable access to Artificial Intelligence technologies for all, we also need to consider the potential of Artificial Intelligence to help achieve SDG 4, to help ‘ensure inclusive and equitable quality education and promote lifelong learning opportunities for all’ [22].

In this challenging context, many Artificial Intelligence technologies might be used, or further developed, to help improve education – especially for older people, refugees, marginalized or isolated communities, and people with special educational and learning needs. To begin with, the UNESCO’s ROAM framework (‘Rights, Openness, Access and Multi-stakeholder Governance’) should be applied, to ensure that the application of Artificial Intelligence in education addresses broader human rights and emerging ethical issues in a holistic manner [23].

For example, and in particular, Artificial Intelligence in education should be made accessible to all citizens, especially for vulnerable groups such as students with learning disabilities), without exacerbating existing inequalities.

Some of examples of Artificial intelligence applications for inclusion and equity in education are:

• The Global Digital Library.

• Dytective, an AI-powered screening tools.

• AI-powered artificial voices.

• AI and augmented reality applications.

• AI-enabled ‘smart’ robots,

• Telepresence robots.

• AI-powered intelligent tutoring systems.

2.1.2 Assistive technologies based on artificial intelligence

Assistive technologies are tools, devices and services that help people with special educational needs to achieve better functioning and independence at home, school and community. Assistive technologies have high capacities due to the potentials of artificial intelligence and have been strengthened and upgraded compared to the past. Especially in the field of special education, teaching and learning, remarkable progress has happened and is happening.

There are examples of AI-based assistive technology that can help people with special educational needs access and interact with educational content. Some of these examples are:

• Speech recognition software: This technology uses artificial intelligence to convert spoken words into written text. This can be especially helpful for students with physical–motor impairment who may have difficulty typing or writing by hand.

• Text-to-speech software: This technology uses artificial intelligence to convert written text into spoken words, making digital content easier to access and understand for students with visual impairments or learning disabilities.

• Augmentative and alternative communication devices: These devices use artificial intelligence to help students with communication disorders to express themselves. They may include features such as speech synthesis, predictive text, and eye tracking to help students communicate more effectively.

• Virtual assistants: Artificial intelligence-based virtual assistants such as Google Assistant can be used to help students with special educational needs access information, complete tasks, and interact with digital devices using voice commands. Chat GPT as an assistant is another example that has been in use in recent months.

• Adaptive learning software: This technology uses artificial intelligence to personalize learning experiences for students, adapting content and activities to their unique needs and abilities. This can be particularly useful for students with learning disabilities or other special needs who may need different types of support to learn effectively.

• Computer vision technology: This technology uses artificial intelligence to interpret the visual input from the camera. It can be used to help visually impaired students navigate their surroundings, recognize objects, and read printed materials. For example, a computer vision system can be used to read text aloud to a visually impaired student or to identify and describe objects in the student’s environment. The Horus system is an example of this technology. The Horus system has integrated capabilities such as routing and independent movement during game activities, being aware of the location of friends in class and school, text recognition and reading, face recognition and object recognition. This technology has the ability to navigate and identify obstacles in 3D, which helps the safe movement of a visually impaired person, and it also has the ability to read written text and Braille, which can help a person independently in their education and learning activities.

• Predictive analytics: This technology uses artificial intelligence to analyze data and predict future outcomes. In education, it can be used to identify students who are at risk of falling behind or dropping out and provide targeted interventions to help them succeed. For students with special needs, predictive analytics can help educators and caregivers identify patterns and trends in their behavior and performance and develop more effective strategies to support them.

• Brain-computer interfaces: These devices use artificial intelligence to convert brain signals into computer commands, allowing users to interact with digital devices using their thoughts. They can be especially useful for students with physical –motor impairment who may not be able to use traditional input devices such as keyboards. For example, a brain-computer interface might allow a student with cerebral palsy to control a computer using their brain signals.

• Social robots: These robots use artificial intelligence to interact with humans in social and emotional ways, providing companionship and support. They can be especially helpful for students with autism spectrum disorder, who may have difficulty with social interactions and communication. Social robots can be programmed to respond to facial expressions, body language and other social cues, helping students with autism spectrum disorder develop social skills and build relationships.

• Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR) and Extended Reality (ER): These technologies use artificial intelligence to create immersive and interactive digital environments. They can be used to provide students with special educational needs hands-on learning experiences that may not be possible in the physical world. For example, a virtual reality simulation can allow a student with physical –motor impairment to explore a historical site or laboratory, while an augmented reality application can provide visual and auditory feedback to a student with autism spectrum disorder educate during a session social skills.

Overall, AI-based assistive technology has the capacity to change the way students with special needs are supported. Using the power of machine learning and other artificial intelligence techniques, we can create more effective and personalized tools and interventions to help these students learn, communicate, and thrive.

Elon Musk’s company has obtained the license to implant a chip in the human brain. Neuralink announced that it has received approval from the US Food and Drug Administration to conduct the first human trials of its chip. The company says it wants to help restore sight and mobility to the disabled by connecting the brain to a computer.

One of the important missions of educational technologists and learning specialists is to identify emerging technologies and introduce their strengths, weaknesses, opportunities and threats in the process of education, teaching and learning. In the meantime, it is very important to discover the platforms and how to use these technologies intelligently and effectively to improve education and learning. One of the emerging technologies that has emerged in recent years is Metaverse technology, which may be called Farajehan technology in Persian. It seems that this technology has considerable capacities and platforms in improving the teaching and learning process. Whether it is normal education, special education, organizational internship, adult education, continuous learning, lifelong learning, and professional development, which requires extensive research. Metaverse is a multi-user environment that integrates physical and virtual reality. Virtual reality, augmented reality, mixed reality, extended reality, recording of daily activities of a person using a digital device1 and mirror world can be considered among the Metaverse technologies that have wide applications in the process of education, teaching and learning, especially for people with special educational needs. Computer, network, game console, headset and virtual reality glasses, mixed and augmented, virtual reality gloves are some of the necessary tools to use this technology. Some of the advantages that Metaverse technology has in the teaching and learning process are: establishing educational justice, experiencing comprehensive interaction, visualization, increasing participation, reducing costs, continuous improvement, unlimited time and space, fast sharing, personalization and strengthen communication. It seems that we need more researches at the national and international level to discover more of this emerging technology [24].

2.2 Blended learning approach and models

In the process of education, teaching and learning, we need pedagogy, andragogy and heutagogy to take advantage of the capacities of any type of technology, including artificial intelligence technology. Therefore, choosing the right approach and models in this field is very necessary and vital. Without having a suitable approach and model, we will not be able to make the most of the capacities of technology, especially new and emerging technologies in education, especially in the education of people with special educational needs. Therefore, in the rest of this chapter, we will discuss the blended learning approach and its conceptual and procedural models.

Blended learning refers to the systematic integration of online and face-to-face engagement to support and enhance meaningful interaction between students, teachers and resources [25]. Blended learning gives students with special educational needs the benefits of both online learning and in-person instruction. According to Christensen, Horn, and Staker ([26], p. 9), blended learning is defined as: “a formal education program in which a student learns at least in part through online learning with some element of student control over time, place, path, and/or pace and at least in part at a supervised brick-and-mortar location away from home. The modalities along each student’s learning path within a course or subject are connected to provide an integrated learning experience.” Students in formal blended learning educational programs learn online part of the time, yet have the benefit of face-to-face instruction and supervision to maximize their learning and to best fit their own needs ([27], p. 5).

Blended learning may also allow teachers to spend less time giving whole-class lessons, and more time meeting and interacting with students with special educational needs individually or in small groups to help them with specific concepts, skills, questions, or learning problems (Figure 1, [1]). In blended-learning situations, students with special educational needs are required to use digital and online technologies and they naturally acquire more technological literacy and greater confidence using new technologies, which is very essential in twenty-first century life and in future [11].

Figure 1.

The conceptual learning model based on blended learning approach for students with special educational needs [11, 28].

The author of the chapter, made extensive efforts to design and develop the educational model based on blended learning approach for students with special educational needs. According to Table 1, 6 main components and 49 sub-components were found. These components underneath the umbrella of two other infrastructure components were considered as face-to-face learning and computer and network-based learning. First, a conceptual model that included all of the main components was designed and then the procedural model was designed and developed by the investigator. The conceptual model and the procedural model showed in the Figures 2 and 3.

Row	Components	Sub-components
1	Analysis	Goal, Learner (mental retardation, visual impairment, hearing impairment, physical-motor impairment, learning difficulties, emotional-behavioral impairment, speech-communication impairment, special diseases, talent and gift), Teacher, The content, Media (interactive, non-interactive), Message, Environment and learning space, Educational resources and learning resources, Technology (print, visual, audio, audio-visual, computer-based, network-based, technology integration), The Context (social, cultural, economic, political).
2	Designing	The combination of learning theories (Behaviorism, Cognitivism, Constructivism, and Connectivism), Purpose (general, partial and behavioral), Environment and learning space, Methods (based on class, computer-based, multimedia-based, social media -based, web-based), Strategies (educational, learning), Media (interactive, non- interactive), Message, Technology (print, visual, audio, audio-visual, computer-based, network-based, technology integration), Principles of universal design (fair use, flexibility of learning activities in use, simple and direct, comprehensible information, Tolerance against errors, Low physical effort, Space and size for use, community of learners, educational atmosphere), Presence (cognitive, teaching, social, emotional), Learning activities (based on information and communications technology, without information and communications technology, based on artistic, individual and group activities), Measurement techniques (self-assessment, peer evaluation, teacher assessment, portfolio, intelligent evaluation).
3	Production	Technology (print, visual, audio, audio-visual, computer-based, network-based, gaming, simulation, augmented reality, virtual reality, technology integration), Media (interactive, non- interactive), Content (textbook, tuition, announcement, newsletter), Training, material, Guides (Teacher’s guide, Learner’s guide, Parent’s guide, Tutorial assistant guide).
4	Implementation, Management and Support	Enrichment, Interaction (learner - teacher, learner-learner, learner-content); Participation (learner, teacher, parent, assistant, manager, therapist, counselor, social worker), Access (limited to the classroom, limited to school, unlimited, throughout the day), Engagement (question and answer, discussion, activity, observe, report presentation), Facilitate, Coordination, Presence (cognitive, teaching, social, emotional), Cooperation, Learner support (administrative, service, advisory, medical, therapeutic, educational, technical), Teacher support (educational, administrative, service, professional, specialized), Course support (educational, technical).
5	Assessment and Evaluation	Portfolios (traditional, electronic), Self-assessment, Peer-assessment, Teacher-assessment, Intelligent assessment, Diagnostic evaluation, Formative evaluation, Summative evaluation, Fallow up evaluation, Evaluation of learners’ interaction (in the learning environment, in the learning Space. (
6	Revision and Modification	Revision and Modification of the teaching – learning process.

Table 1.

Component and sub-components of educational model based on blended learning approach for students with special educational needs [1].

Figure 2.

The conceptual learning model based on blended learning approach for students with special educational needs [1, 28].

Figure 3.

The procedural learning model based on blended learning approach for students with special educational needs [1].

According to the Figure 2, 6 main components of the educational model based on blended learning approach for students with special educational needs are: analysis, designing, production, implementation, management and support, assessment and evaluation and revision and modification were obtained. These six main components underneath the umbrella of two other infrastructure components were considered as face-to-face learning and computer and network-based learning.

According to the Figure 3, 49 sub-components of the educational model based on blended learning approach for students with special educational needs are:

goal, learner, teacher, the content, media, message, environment and learning space, educational resources and learning resources, technology, the context, the combination of learning theories, purpose, environment and learning space, methods, strategies, media, message, technology, principles of universal design, presence, learning activities, measurement techniques, technology, media, training, material, guides, enrichment, interaction, participation, access, engagement, facilitate, coordination, presence, cooperation, learner support, teacher support, course support, portfolios, self-assessment, peer-assessment, teacher-assessment, intelligent assessment, diagnostic evaluation, formative evaluation, summative evaluation, fallow up evaluation, evaluation of learners’ interaction.

As showed in Table 1 and Figure 3, each of the sub-component including several minor sub-components. For example, the sub-component of learner including minor sub-components of mental retardation, visual impairment, hearing impairment, physical-motor impairment, learning difficulties, emotional-behavioral impairment, speech-communication impairment, special diseases, talent and gift, and the sub-component of learner support including minor sub-components of administrative, service, advisory, medical, therapeutic, educational and technical support [1].

2.3 Case studies

To check the validity of the blended learning approach and its models in special education, several researches were designed and implemented by the author of the current chapter of the book or with his supervisor in the last years. In the rest of this chapter of the book, some of these researches are mentioned.

Allahi [13] conducted a research entitled “design and validation of a mobile phone learning pattern for visually impaired students”. The aim of his study was to design an educational model of using a mobile phone and its effects on academic achievement motivation, learning and participation ratio of blind students in English courses. He developed an educational conceptual model of using the mobile phone for blind people with 4 components of human, training, technology and support factors as well as a procedural model with 4 stages of preparation, design, implementation and evaluation.

Toofaninejad [17] conducted a research entitled “designing instructional pattern of the learning environment enriched by virtual social network and its impact on learning rate and the social skills of the students with hearing impairment in science course”. He discovered 7 final categories including engagement, interaction, feedback, content, sources, evaluation, and support. He also were extracted 24 sub categories. Based upon discovered categories, a conceptual model was designed that is encompassed all mentioned categories. Next, procedural model, which it is a practical model, was designed and developed. The investigator also showed that the instructional pattern of the learning environment enriched by the virtual social network by 99% of confidence interval has a positive effect on learning and social skills of DHH students.

Zareei [29] conducted a research entitled “design and validation of virtual social networks of Iran’s schools”. He discovered four main components of the model including network learning, network facilitation, network management, network technology, and the following sixteen sub-components: information valuation, content co-creation and reproduction, interaction, self-assessment and peer-assessment, excitement, learning support, process evaluation, empowerment, learning analytic, network information, network content, network interaction, network evaluation.

Moradi [30] Conducted a research entitled “design and validate an educational model based on assistive technology and its effect on the amount of motivation for academic achievement, learning and academic satisfaction of students with physical-motor impairment in English language lesson”. He discovered five important components of analysis, design, production, implementation, support and evaluation. Also, 34 sub-components were extracted for the main components. The researcher designed and developed a conceptual model and the procedural pattern. Also, the results showed that the educational model based on assistive technology for teaching English language lesson for students with physical-motor impairment is effective.

Zaraii Zavaraki and Schneider [11] conducted a research entitled “blended learning approach for students with special educational needs: a systematic review”. The analysis revealed that approach, environment, learner, tools, support and evaluation are categories of extracted in blended learning approach for students with special educational needs. The elements of each category are introduced and explained in the article. However besides presenting the evidence found in literature, our analysis highlights that researching the effect of blended learning approach on special educational needs students remains an under-explored area of study. Further well-designed research into the use of blended learning approach in special education is therefore needed.

Zaraii Zavaraki [1] conducted a research entitled “designing and validating of blended learning model with emphasis on digital technologies for students with special educational needs”. The purpose of this study was to design and validate an educational model based on blended learning approach for students with Special Educational Needs. A mixed method in a type of the exploratory was used. To obtain the components of the model, an inductive content analysis was performed and for internal validity of the model, descriptive survey method was used. First, a conceptual model that included all of the main components was designed and then the procedural model was designed and developed. Findings of internal validation from the viewpoint of experts showed that the conceptual and procedural models were considered to be comprehensive, suitable, applicable, enriching, appropriate, promote new educational approaches, can help in the development of educational strategies, suitable and can improve the quality of teaching and learning processes of students with special educational needs at the international level. Therefore, it is suggested to use these models as innovative strategies for students with special educational needs.

Delavaryan [31] conducted a research entitled “designing a technology-based instructional program for teaching English to mentally retarded students and its impact on academic achievement motivation, learning and retention”. The collected data were analyzed in multi-variable MANCOVA method. The results of statistics analysis showed that the technology-based instruction has caused a meaningful increase in academic achievement motivation of experiment group students and also the development and improvement of their learning and retention in English Language Lesson.

Khateri [32] conducted a research entitled “the impact of augmented reality technology on academic achievement motivation and learning of second-grade dyslexic students”. In this study, the sample size was 20 students divided into experimental and control groups. At first, a pre-test learning and academic achievement motivation is taken from both groups and then an Augmented Reality Program was used for the experimental group. The results show that augmented reality utilization improve learning and academic achievement motivation the students with dyslexia disorder.

Ghasemi Sameni [33] conducted a research entitled “designing and validating of instructional model in computer game environment and its effect on cooperative learning of high-functioning autism students”. Once content analysis and code extraction had been completed, the components and sub-components were provided in the form of a model. A conceptual model was constructed accordingly. Then, a procedural model was developed. It was found that the difference between the experimental group and control group in cooperative learning and its components (positive interdependence, individual accountability, group processing, social skills, and interaction) was significant after weighting the pre-test scores. Therefore, it can be said that the instructional model in computer game environment had a significant effect on the cooperative learning of high-functioning autistic students.

Masnavi [34] conducted a research entitled “Design and validation of a model of mobile multimedia learning environment and its effect on learning and learning transfer of social skills of students with high-performance autism spectrum disorder”. Quantitative and qualitative research methods were used to conduct the research. The results showed that the program designed based on the model of mobile multimedia learning environment was effective on learning and learning transfer of 3 subjects and had an effect on one subject learning but on his learning transfer did not have. Using these results, it can be said that the use of the proposed mobile multimedia learning environment model was effective on learning and transfer of learning, learners with autism spectrum disorder with high performance.

Bakhtiarvand [35] conducted a research entitled “designing and validating an instructional model of picture exchange communication system based on technology and investigating its effectiveness on social and communication skills and behavioral problems in children with high-functioning autism disorder”. Mixed research method was used to conduct the research. Using these results, it can be said that the use of the proposed educational model of technology-based PECS is effective on social and communication skills and reducing the behavioral problems of high-functioning autistic children.

Kabiri [36] conducted a research entitled “designing and validating of educational model for applying technology in philosophy program for children and its impact on the critical thinking of gifted students in the second period of elementary school”. In order to achieve the goal of the research, a mixed method of sequential exploratory type was used. The results obtained from the data analysis related to the research hypothesis showed that after removing the pre-test effect, there was a significant difference between the average critical thinking scores of the two groups in the post-test stage. The results showed that this educational intervention had a positive impact on all the components of critical thinking. According to the results of the research, it can be said that the educational model for applying technology in the philosophy program for children is effective on the development of critical thinking of gifted students in the second period of elementary school.

Ghanat [37] conducted a research entitled “the effect of computer game on the recognition of facial emotions in students with hearing impairment in the elementary school of Arak city”. In this study, the experimental group received 12 sessions of the Emotion Sorting computer game, which was designed for this group of children with the aim of improving facial emotion recognition skills and in accordance with the pattern and principles of game design. On the other hand, in the control group, it was done in the usual way. The results of this study showed that the emotion matching computer game with the aim of recognizing facial emotions in the four levels of discrimination, naming, selection and matching was effective in children with hearing impairment and improved this skill, the most impact being on the emotion matching component with 71% was. The results of this study can provide important information about the effectiveness of computer games as a tool to improve cognition and emotional understanding in children with hearing loss. This information can be used to guide the development of interventions and programs to improve the social and emotional well-being of children with hearing loss.

Abbas Zadeh Rogoshui [38] conducted a research entitled “the effect of digital educational game on the visual perception of dyslexic first grade students”. The findings showed that the digital educational game has an effect on improving the subjects’ visual perception and all five students were able to identify and write Farsi signs independently after participating in the project. Therefore, it can be concluded that the digital educational game is a new tool with the combination of images, sound and interactive activities in the game environment of the subject, which creates and maintains the focus of learning and the continuity of the signs compared to the traditional model, and it can be used to improve and learn better Farsi signs benefit students with dyslexia.

Zaraii Zavaraki and Alimardani [28] conducted a research entitled “the role of blended learning approach on interaction process of students with special educational needs”. The purpose of this study was to determine the role of blended learning approach on interaction process of students with special educational needs. The data gathering tool was a questionnaire of 52 items that 51 questions was created according to7 point Likert scale and 1 question was created according to open-ended question. Three items of this questionnaire were related to the interaction variable and the other items included other variables such as learning enrichment, engagement, participation, facilitation, coordination, sense of presence, cooperation, support, motivation, academic achievement and learning rate. The validity of the questionnaire was evaluated by teachers and its reliability was 0.98 with Cronbach’s alpha. Findings from the viewpoint of teachers showed that the blended learning approach has a great role on interaction process of students with special educational needs.

Faizi [39] study is in progress) Conducting a research titled “designing and validating of learning program using artificial intelligence and its impact on problem solving skills of students with math learning disorder”.

Ahmadi [40] study is in progress) Conducting a research titled “designing an educational program based on social robot technology and its impact on social skills and academic achievement motivation of high-functioning autism spectrum disorder students”.

3. Conclusions

In this chapter of the book, the author tried to introduce the capacities and capabilities of artificial intelligence in education, especially special education, and share its common uses in the process of education, teaching and learning of people with special educational needs with the esteemed readers. Also, to introduce various assistive technologies based on artificial intelligence in special education. Considering the importance of the approach and model in the education process, the blended learning approach and related models were introduced. The case studies that have been done and are being done in the field of artificial intelligence and its applications in special education were also discussed by the author and his research team. It seems that we need more researches at the national and international level to discover more of this emerging technology.

Acknowledgments

I sincerely thanks from all of the colleagues and students who have cooperated in the research team of the author of this chapter of the book especially the students, teachers, parents and experts who in any way that have provided basic knowledge in the field of artificial intelligence technology.