Investigation into the Applications of Artificial Intelligence (AI) in Special Education: A Literature Review

Abstract

The integration of artificial intelligence (AI) in special education has the potential to transform learning experiences and improve outcomes for students with disabilities. This systematic literature review examines the application of AI technologies in special education, focusing on personalized learning, cognitive and behavioral interventions, communication, emotional support, and physical independence. Through an analysis of 15 studies conducted between 2019 and 2024, the review synthesizes evidence on the effectiveness of AI tools, including intelligent tutoring systems, adaptive learning platforms, assistive communication devices, and robotic aids. The findings suggest that AI-driven technologies significantly enhance students’ academic performance, communication skills, emotional regulation, and physical mobility by providing tailored interventions that address individual needs. This review also highlights several challenges, including limited access to AI technologies in low-resource settings, the need for more comprehensive teacher training, and ethical concerns related to data privacy and algorithmic bias. Additionally, the geographic focus of the current research is primarily on developed countries, overlooking the specific challenges of implementing AI in resource-constrained environments. This review emphasizes the need for more diverse and ethical research to fully realize the potential of AI in supporting students with disabilities and promoting inclusive education.

1. Introduction

The landscape of education has undergone a profound transformation over the past decade, driven by rapid technological advancements that have permeated various aspects of our daily lives (Al-Hendawi 2023; Al-Hendawi et al. 2022, 2023). At the forefront of this revolution is artificial intelligence (AI), which has evolved from a theoretical concept to a practical tool with far-reaching implications across multiple sectors, including education (Brown et al. 2020). The field of special education, in particular, stands to benefit significantly from the integration of AI technologies, offering new avenues to support students with disabilities and create more inclusive learning environments (Chassignol et al. 2018). Artificial intelligence, broadly defined, refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks include visual perception, speech recognition, decision-making, language translation, and adapting to new situations (Ng et al. 2022). In the context of education, AI encompasses a range of technologies and approaches designed to enhance learning experience, personalize instruction, and provide support for both educators and students. Recent research has explored various applications of AI and related technologies in special education, such as augmented reality for students with autism spectrum disorder and other AI-based systems aimed at improving learning outcomes for students with diverse needs (Jose and Jose 2024).

The application of AI in education, often referred to as AIEd, has gained substantial traction in recent years. This growth is particularly evident in special education, where the unique needs of students with disabilities present both challenges and opportunities for technological innovation. As Catlin and Blamires (2019) note, there has been an increased emphasis on inclusive education, with particular attention paid to accommodating the diverse needs of students receiving special education services. AI offers promising solutions to overcome traditional barriers, providing assistive technologies that can adapt to various types of disabilities, including visual, auditory, physical, and cognitive impairments.

The potential of AI in special education is multifaceted. Intelligent tutoring systems can adapt to individual learning styles and paces, offering personalized instruction that caters to each student’s specific needs (Chemnad and Othman 2024). Natural language processing technologies can assist students with communication difficulties, while computer vision applications can aid those with visual impairments. Moreover, AI-powered tools can help educators in assessment and progress monitoring, allowing for more targeted interventions and support strategies.

Machine learning, a subset of AI, plays a crucial role in these applications. By analyzing vast amounts of data, machine learning algorithms can identify patterns and make predictions, enabling educational systems to continually refine and improve their effectiveness. Deep learning, a more sophisticated form of machine learning, utilizes artificial neural networks to mimic human cognitive processes, opening up new possibilities for complex problem-solving and adaptive learning environments (Li 2022).

However, ethical considerations, including data privacy and the potential for AI to exacerbate existing inequalities, must be carefully addressed. Additionally, there are concerns about the readiness of educators to effectively integrate AI tools into their teaching practices, with many feeling unprepared for this technological shift (Sharma et al. 2023). The integration of AI into special education represents a transformative shift in how educators address the diverse needs of students with disabilities. This systematic literature review aims to comprehensively analyze the existing body of research on AI applications in special education. The primary objective is to evaluate the effectiveness of AI technologies in improving learning outcomes, promoting engagement, and enhancing the overall educational experience for students with a wide range of disabilities. Specifically, the study focuses on how AI supports personalized learning pathways, facilitates cognitive and behavioral interventions, enhances communication and emotional support, and fosters greater physical independence. Additionally, it seeks to uncover the challenges and ethical concerns associated with the implementation of AI in special education settings. This review aims the following:

(1)

Identify the most impactful AI-driven tools and strategies used in special education

(2)

Clarify the extent to which AI contributes to educational effectiveness alongside underlying instructional strategies.

(3)

Provide evidence-based insights for educators, researchers, and policymakers to inform the development of inclusive, adaptive, and ethically sound educational technologies.

2. Materials and Methods

This study employed a systematic literature review approach, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, to explore research trends in the application of artificial intelligence (AI) for supporting students with disabilities in special education. The use of PRISMA ensures a transparent, structured, and comprehensive review process, allowing for a systematic assessment of the literature while minimizing bias and improving the reliability of findings. Given the rapid expansion of studies in this area, PRISMA’s rigorous methodology is particularly valuable for tracking and synthesizing the growing body of research (Higgins et al. 2019).

The review process began with a comprehensive search of academic databases, including ERIC, PubMed, Scopus, and Google Scholar, to gather peer-reviewed articles, conference papers, and reports relevant to AI applications in special education. The search involved combining three primary keywords—“artificial intelligence”, “assistive technology”, and “machine learning”—with two educational context keywords: “students with disabilities” and “special education”. Each primary keyword was searched separately in combination with each context keyword. For example: “artificial intelligence” AND “students with disabilities”; “artificial intelligence” AND “special education”; “assistive technology” AND “students with disabilities”; “assistive technology” AND “special education”; “machine learning” AND “students with disabilities”; and “machine learning” AND “special education”. This approach ensured thorough coverage of literature specifically relevant to artificial intelligence technologies within educational settings focused on special education and students with disabilities. These keywords were deliberately chosen to broadly capture relevant studies and ensure comprehensive coverage of the research topic, rather than using narrower or more specialized terms.

Following the initial search, the PRISMA flow diagram was applied to track the inclusion and exclusion of studies. Articles were first screened based on titles and abstracts, followed by full-text reviews to ensure alignment with the study’s objectives. Studies that met inclusion criteria—those focusing on AI interventions designed to support students with disabilities in special education settings—were included in the final review.

The keywords for this study were selected based on a combination of domain expertise, a review of relevant literature, and preliminary searches in academic databases. We conducted exploratory searches to identify commonly used terms in the field and refined our selection based on their relevance and frequency in prior studies. This approach ensured that our keywords effectively captured the scope of our research topic.

2.1. Eligibility Criteria

Inclusion in this review was determined based on five specific eligibility criteria: (a) articles must be peer-reviewed and published in English, (b) publication dates must fall between January 2019 and August 2024, (c) studies should focus on disabilities within the context of special education, (d) articles must involve the use of AI tools, and (e) research should be conducted within educational settings, where structured instruction is provided. This definition does not include informal educational support, such as family assistance, private tutoring, or homework help outside of school. Studies published as books or review articles were excluded from consideration.

2.2. Information Sources

A structured search was performed to assess the use of AI for students with disabilities in special education, adhering to a systematic literature review approach. This search spanned five major databases—PubMed, ERIC, IEEE, Education Source Ultimate, and APA PsycARTICLES—to ensure comprehensive coverage. Only peer-reviewed journal articles published in English from January 2019 to August 2024 were included. The search strategy employed keywords such as “Artificial Intelligence” in combination with “Special Education” and “Disability or Disabilities”, selected through careful ex-amination of titles, abstracts, and keywords from prior studies, as well as through pre-liminary exploratory searches.

2.3. Search Strategy

The ERIC database was selected as a primary source for this study. A targeted search strategy was implemented using specific keywords and filters. The main search terms included “Artificial Intelligence” combined with “Disability” or “Disabilities” and “Special Needs”. To capture recent advancements, the search was restricted to English-language articles published between 2019 and 2024. Additional filters prioritized peer-reviewed journal articles and research conducted in educational settings, such as schools, universities, and special education centers. The search initially yielded one hundred and seventeen articles, all of which were reviewed through titles, abstracts, and full-text screening, resulting in six articles meeting the inclusion criteria. Relevant data were systematically organized in an Excel sheet. This approach aimed to identify studies focused on the application of artificial intelligence to support individuals with disabilities in special education environments.

2.4. Selection Process

The reviewers screened the abstract of each identified article, assigning a Yes (Y) or No (N) in the spreadsheet to indicate whether it met the five predetermined inclusion criteria. Only articles marked Yes for all criteria by the reviewers were selected for full-text review and analysis. If an abstract lacked sufficient information to determine eligibility, the reviewers conducted a brief full-text review to reach a consensus on whether the inclusion criteria were satisfied. Any coding discrepancies were resolved through discussion until consensus was achieved. This rigorous screening process ensured that the included studies accurately reflected the target population, interventions, comparators, and outcomes.

2.5. Study Risk of Bias Assessment

A comprehensive risk of bias assessment was performed to ensure the reliability and validity of the findings (Higgins et al. 2024). The assessment systematically evaluated key aspects of the included studies, including study design, data collection methods, and analytical processes. Particular attention was paid to identifying potential sources of bias that could impact the outcomes of using AI technologies to support students with disabilities. This included assessment of selection bias related to the data collection process, as well as analysis bias concerning the interpretation and reporting of results. This risk of bias evaluation aimed to uncover any systematic deviations that could compromise the credibility of the evidence base, thereby contributing to a more accurate and trustworthy synthesis of how AI can be effectively and safely implemented within special education environments (Higgins et al. 2024).

2.6. Certainty Assessment

Discrepancies or variations in findings across studies were analyzed by systematically examining potential sources of heterogeneity. The authors reviewed differences in study design (e.g., qualitative vs. quantitative approaches), data collection methods, sample characteristics (such as participant demographics and disability types), and the implementation of AI interventions. These factors were assessed to determine their contribution to inconsistencies in outcomes. This analytical process enabled a clearer evaluation of the coherence and reliability of the evidence, supporting a comprehensive certainty assessment. The goal was to transparently articulate the strengths and limitations of the existing research, thereby informing stakeholders’ decisions regarding the effective implementation of AI technologies to support students with disabilities in educational settings.

2.7. Subgroup Analysis and Sensitivity Analysis

Subgroup analysis and sensitivity analysis were performed to strengthen the robustness of the findings. The subgroup analysis aimed to investigate potential variations in outcomes based on specific characteristics of the included studies. Subgroups were categorized by factors such as the type of disability (e.g., learning disabilities, physical disabilities, autism spectrum disorder), the type of AI technology employed (e.g., machine learning, assistive robotics), and the educational context (e.g., primary versus secondary education). This analysis was intended to identify any differential effects of AI interventions across diverse populations and settings, thereby providing insights into how these technologies can be effectively tailored to address the unique needs of different student groups.

Sensitivity analyses were conducted to evaluate the stability of the results by examining the impact of methodological variations on the outcomes (Higgins et al. 2024). This process involved adjusting the inclusion criteria, such as the type of study design or sample size and reassessing the effect sizes. By analyzing how these factors influenced the overall conclusions, we aimed to establish whether the results were consistent and reliable across various scenarios (Higgins et al. 2024).

3. Results

3.1. Study Selection and Study Characteristics

Out of the initial pool of 228 studies identified for potential inclusion, 15 unique studies were ultimately reviewed after applying the selection criteria (Figure 1). These 15 studies (

Table 1. Key characteristics of studies using AI.

Authors	Sample Age; Gender; Disability	Research Design	Using of AI	Type of AI	Results
Dutt et al. (2022), India	7 y; N = 24 (15 B, 9 G); Learning disability	Quantitative	Identification assessment	Framework of intelligent tutoring system (ITS) involves fuzzy neural network (FNN)	The system uses an FMM neural network to identify dyslexia, classify learners into disability categories, and assess severity based on score and time inputs. It offers an enhanced profiling system and shows strong potential as a real-time solution.
Standen et al. (2020), UK, Rome and Spain	6–18 y; N = 67 (46 B, 21 G); intellectual disabilities (ID), ASD	Quantitative; experimental study	Engagement, emotional, and educational skills	MaTHiSiS adaptive learning system	Intervention sessions showed increased engagement and reduced boredom compared to control sessions, though achievement remained similar. These results indicate that personalizing activities to the learner’s needs and emotional state boosts engagement and fosters supportive affective states for learning.
Ouherrou et al. (2019), Morocco	7–11 y; N = 42 (24 B, 18 G); Learning disability	Quantitative; experimental study	Emotional and behavioral skills	Information and communication technology (ICT) in virtual learning environments (VLE)	The results show that emotions are present in virtual learning environments, VLEs, with children with learning disabilities (LDs) experiencing similar emotions to their peers without LDs. The findings indicate that children with LDs report fewer negative emotions in VLEs compared to traditional classroom settings.
Toyokawa et al. (2023), Japan	12 y; N = 2 B; ASD	Quantitative	Behavior and educational skills	Learning and evidence analysis framework (LEAF)	The results indicated that Boy 1 had fewer additional actions unrelated to AR activities, while Boy 2 exhibited more extra actions, showing a fixation on ICT features. The study highlighted that learners can become distracted by non-learning elements, such as interacting with e-learning tools like timers.
Man Kit Lee et al. (2023), China	7–12 y; N = 1015; dyslexia	Quantitative	Identification and assessment	Multiple machine models with different learning algorithms	The results showed that the Support Vector Machine (SVM) model was the most accurate among five machine learning algorithms, with a 78.0% success rate in classifying children with dyslexia and their typically developing peers. The findings highlight the effectiveness of machine learning in identifying dyslexia through Chinese dictation tasks.
McDonald et al. (2023), USA	Under 30; N = 3 G; *	Qualitative	Elicitation and empathy	AI-enhanced adaptive assistive technologies (AATs); Grammarly and PINATA	The results showed that students were confident that Grammarly could not detect disabilities or medical conditions but speculated it could identify factors like language ability, foreign language use, or age. Both Grammarly and PINATA collected similar data, and the support of individuals with significant disabilities was seen as crucial. Students believed this would enhance AI effectiveness, benefiting healthcare professionals and government agencies in monitoring and assisting users with disabilities.
Schindler et al. (2022), German	9–14 y; N = 227 *; deaf or hard-of-hearing (DHH)	Qualitative	Identification and assessment	Eye-tracking device and stimuli	The study found notable differences in the enumeration processes between deaf or hard of hearing (DHH) students and hearing students. In both subitizing and counting tasks, DHH students used more effective enumeration strategies than their hearing peers across random arrangement conditions.
Rakap and Balikci (2024), Turkey	Teacher [35–39 y; N = 15 (19 G, 11 B)]; ASD	Quantitative	IEP goals development	ChatGPT4.0	The results indicate that special education teachers who used ChatGPT created significantly higher-quality IEP goals compared to those who did not use the technology.
Lawal et al. (2024), Nigeria	5–7 y; N = 10 (6 B, 4 G); hearing impairment	Quantitative; single-subject design	Educational skills	Mobile app named “Hausar Kurma”	The findings emphasize the effectiveness of the mobile app “Hausar Kurma” as an evidence-based research tool for evaluating educational tools and their impact in special education.
Kotevski et al. (2024), Croatia	6–8 y; N = 10 (5 B, 5 G); cognitive impairment, ASD, and cerebral palsy	Quantitative	Writing and motoric skills	MediaPipe for web, PixiJS, and custom AI and drawing utilities. MediaPipe Toolkit comprises the Framework and the Solutions.	The findings indicate a high level of engagement among children learning the alphabet through new technology, with nine out of ten expressing enthusiasm for this method and a desire to explore more letters. Additionally, children demonstrated significant interest in observing their peers play the game.
Carruba et al. (2024), Italy	*; N = 222; *	Mixed methods	Engagement and emotional skills	AI technologies	AI improves student engagement and personalizes learning, creating inclusive and effective educational environments. Teachers encounter challenges, including insufficient training, fears of technical incompetence, and concerns about the inappropriate use of AI tools.
Maydi and Alharthi (2023), Saudi Arabia	Teacher [*; N = 78]; Learning disability	Qualitative; descriptive	Attitudes of teachers	AI technologies	The findings indicated a positive shift in teachers’ attitudes toward artificial intelligence-based training programs for students with learning disabilities.
Hu and Wang (2021), China	17–19 y; N = 30 (16 B, 14 G); physical disability	Quantitative	Physical fitness	AI technologies	Regular inclusive dance classes were found to enhance students’ physical fitness and improve their overall movement coordination.
Molokwu and Molokwu (2024), Nigeria	*; N = 98 (42 B, 56 G); physical disability	Quantitative; quasi-experimental design	Stress management	AI technologies	The study found a significant main effect of the level of study on stress management, as well as an interaction effect between the level of study and the AI counseling technique. No significant interaction effect was observed regarding gender in relation to the level of study and the AI counseling technique.
El Naggar et al. (2024), UAE	11–17 y; N = 16 (9 B, 7 G); Learning disability (gifted children)	Qualitative	Cognitive skills	AI technologies	The findings suggest that incorporating AI technologies enhances the zone of proximal development by providing tailored support and challenges that cater to the unique needs of students with diverse abilities, including those with learning disabilities and exceptional learners.

* missing information.

) met all the inclusion criteria necessary for an in-depth analysis. During the review process, efforts were made to address potential overlaps and duplicates, which often arise when multiple databases are searched simultaneously. Specifically, three instances of overlap were identified: studies were duplicated across both the ERIC and Education Source Ultimate databases (Molokwu and Molokwu 2024; Salma 2023; Standen et al. 2020), while another overlap occurred between the PubMed and Education Source Ultimate databases (Chiu et al. 2023).

There were instances of duplicate entries within individual databases due to varying keyword search strategies. In the ERIC database, duplicates were noted among the studies by Dutt et al. (2022), Ouherrou et al. (2019), Toyokawa et al. (2023), and Standen et al. (2020). Similarly, in the Education Source Ultimate database, studies by Maydi and Alharthi (2023) and Molokwu and Molokwu (2024) were identified as duplicates. These duplicates were carefully managed to ensure that only unique studies were included in the final review, thereby maintaining the integrity of the analysis.

The selected studies reflect broad geographical diversity. Three were conducted in Arab countries: the United Arab Emirates (El Naggar et al. 2024), Morocco (Ouherrou et al. 2019), and Saudi Arabia (Maydi and Alharthi 2023). Additional studies came from Asia, including India (Dutt et al. 2022), Japan (Toyokawa et al. 2023), and China (Man Kit Lee et al. 2023; Hu and Wang 2021). European research included the UK, Italy, and Spain (Standen et al. 2020), Germany (Schindler et al. 2022), Croatia (Kotevski et al. 2024), Italy (Carruba et al. 2024), and Turkey (Rakap and Balikci 2024). Two studies were based in Nigeria (Lawal et al. 2024; Molokwu and Molokwu 2024), and one in the United States (McDonald et al. 2023).

The majority of the reviewed studies were published between 2023 and 2024, reflecting the recent surge of interest in the use of AI technologies in special education. This temporal focus highlights the timeliness of the review and ensures that the findings are relevant to current trends in AI applications within the field of special education.

3.2. Data Item

Our research highlights several variables where AI plays a crucial role in supporting students with disabilities. (1) Personalized Learning: benefits from AI-driven tools that provide customized educational content tailored to each student’s needs, abilities, interests, and knowledge level, resulting in more effective learning experiences and enhanced academic performance. (2) Communication Support: facilitated through AI-powered assistive technologies that help students overcome communication barriers, promoting greater inclusion in the classroom and increased engagement in educational activities. (3) Cognitive and Behavioral Interventions: benefit from AI systems that assist in managing classroom behavior and encouraging positive social interactions, particularly for students facing cognitive or behavioral challenges. (4) Emotional Support: provided through tools like virtual reality and emotion-recognition technology, which deliver real-time feedback to aid students in emotional regulation. (5) Physical Support: enhanced by AI innovations such as robotic devices and smart prosthetics, which improve mobility and independence, enabling students with physical disabilities to more fully participate in educational settings (Al-Hendawi et al. 2022; Al-Hendawi 2023).

3.3. Results of Individual Studies

3.3.1. Purpose of Using AI

AI significantly enhances learning experiences by providing tailored support and adaptive technologies for each student. This is especially beneficial in special education, improving engagement and comprehension for students with diverse learning challenges. AI applications in special education are extensive, focusing on personalized learning, communication, cognitive and behavioral interventions, emotional support, and physical assistance (Al-Hendawi et al. 2023).

Personalized Learning

AI technologies enable the personalization of educational content to align with students’ individual needs, thereby enhancing learning efficiency and academic performance. Beyond personalization, AI also plays a critical role in identifying learning challenges. Dutt et al. (2022) developed an intelligent tutoring system (ITS) that detects learning disabilities through a two-stage process—pretest analysis followed by a neural network-based assessment—and demonstrated its effectiveness in identifying specific educational needs in a study involving 24 children. This highlights AI’s potential for early intervention. Although primarily diagnostic, such systems can also support personalized instruction by adapting content to learners’ cognitive profiles (Dutt et al. 2022).

Similarly, Man Kit Lee et al. (2023) employed AI to support Chinese children with dyslexia, using machine learning to analyze orthographic, phonological, and semantic challenges in written tasks. By examining data from 1015 children, their approach not only enhanced early diagnosis but also tailored educational strategies to individual literacy issues.

Schindler et al. (2022) investigated the application of AI to support students who are deaf or hard of hearing (DHH), particularly in mathematics education. Eye-tracking data were collected via webcam from 63 DHH and 164 hearing students in Grades 3–5 as they completed math tasks. These data were then analyzed using AI-based pattern recognition techniques to identify visual attention patterns and cognitive processing differences between the two groups. Through this integration of eye-tracking and AI, the study provided insights into the development of numerical concepts in DHH students, informing the design of more effective educational interventions (Schindler et al. 2022).

Molokwu and Molokwu (2024) investigated AI’s impact on students with physical disabilities in engineering programs, focusing on stress management and academic performance. Their study employed AI-driven counseling techniques in a pretest–post-test control group design, demonstrating AI’s effectiveness in addressing both academic and mental health challenges.

Lawal et al. (2024) evaluated “Hausar Kurma”, a mobile app designed to teach English to Hausa-speaking students with hearing impairments. Using a single-subject design, this study affirmed the app’s effectiveness in special education settings, showcasing AI’s role in enhancing learning precision and adherence to evidence-based practices.

In summary, these studies underline AI’s diverse role in special education, providing customized educational solutions that enhance diagnostic accuracy and address both cognitive and emotional needs. This technology transforms learning environments by delivering interventions tailored to individual student profiles.

Communication Support

AI-driven assistive technologies significantly enhance communication for students with disabilities, promoting greater classroom inclusion and more effective engagement with peers and educators. These technologies adaptively support personalized interactions, crucial for students’ active participation in educational activities.

Standen et al. (2020) evaluated the MaTHiSiS adaptive learning system across the UK, Italy, and Spain, focusing on children with intellectual disabilities. The study tested whether sensor data could detect students’ affective states to dynamically tailor learning experiences. Results confirmed that MaTHiSiS significantly improved engagement and learning outcomes by adjusting educational strategies based on real-time emotional and cognitive data.

Rakap and Balikci (2024) explored how ChatGPT could improve the formulation of individualized education program (IEP) goals for preschool children with autism. Comparing goals set by special education teachers with and without AI assistance, the study found that ChatGPT-enhanced goals were higher quality, more personalized, and more comprehensive across developmental domains. This suggests that AI tools like ChatGPT can substantially boost the effectiveness of IEP goal setting, aiding teachers in crafting more nuanced and expansive communication plans.

These findings underscore AI’s transformative potential in special education, offering tools that not only enhance learning and communication outcomes but also empower educators to provide more refined and effective instruction.

Cognitive and Behavioral Interventions

AI-driven systems are pivotal in supporting cognitive and behavioral interventions for students with disabilities. These tools enhance classroom management, promote positive social interactions, and facilitate emotional regulation, all essential for creating a supportive learning environment.

Ouherrou et al. (2019) studied the emotional and behavioral challenges of children with learning disabilities. The study emphasized how virtual learning environments (VLEs), powered by information and communication technology (ICT) including AI, can mitigate emotional and behavioral barriers. Findings suggest that VLEs not only manage negative emotions but also foster a more engaging and supportive learning atmosphere, thereby reducing behavioral issues linked to emotional frustrations.

Toyokawa et al. (2023) focused on the learning and evidence analysis framework (LEAF) system, an augmented reality (AR)-based AI tool that visualizes students’ reading processes to enhance learning reflection and decision-making. The system allowed for collaborative behavioral reflections involving teachers and parents, which helped students understand their learning behaviors and improve cognitive decision-making.

El Naggar et al. (2024) conducted qualitative research on the cognitive engagement of exceptional learners in AI-mediated discussions versus traditional settings. Their findings indicated that AI discussions promoted more active participation and critical thinking among these students by providing structured, dynamic, and interactive learning experiences. This facilitated better cognitive engagement and knowledge construction than traditional classroom dialogues.

These studies underscore AI’s vast potential in facilitating cognitive and behavioral interventions. By enabling more tailored and interactive learning experiences, AI tools help educators effectively manage classroom dynamics and deepen students’ emotional and cognitive engagement, enhancing overall educational outcomes in special education.

Emotional Support

AI-driven tools, like virtual reality and emotion recognition technologies, are vital in providing real-time emotional support, helping students with disabilities recognize, regulate, and manage their emotions. These capabilities are essential for promoting emotional well-being, enhancing learners’ participation in educational and social activities, and supporting inclusive educational environments.

McDonald et al. (2023) investigated how AI-enhanced tools can foster empathy and self-reflection among Information Systems graduate students. Using a participatory elicitation toolkit, students examined their experiences with AI learning tools and those of older adults using adaptive assistive technologies. The study not only promoted empathy but also addressed broader societal impacts of AI technologies, encouraging an inclusive approach to design thinking.

Carruba et al. (2024) explored the emotional training needs of teachers integrating AI in classrooms. Their mixed-methods study highlighted the urgency of equipping educators with skills to address emotional competencies alongside technological know-how. This approach aims to foster inclusive and supportive learning environments that cater to both the academic and emotional needs of students.

These studies illustrate the importance of AI in enhancing emotional intelligence and support within educational settings. By integrating emotional competencies in AI applications and teacher training, these technologies can significantly improve students’ ability to manage emotions, enhance self-awareness, and engage more effectively in learning activities. AI-enhanced emotional support thus forms a crucial part of a holistic approach to special education, emphasizing emotional regulation alongside academic achievement.

Physical Skills

AI technologies, including robotic aids and smart prosthetics, are critical in improving physical support for students with disabilities, enhancing their mobility, independence, and participation in educational activities. These innovations foster greater inclusion and engagement in the classroom by helping students overcome physical barriers.

Kotevski et al. (2024) investigated gesture-based technology that uses predefined gestures to interact with computers, facilitating motor skill development and learning to write in the Macedonian Cyrillic alphabet. This technology not only supports fine motor skill enhancement but also promotes physical coordination, offering an engaging platform for students with physical disabilities to develop essential skills.

Additionally, Hu and Wang (2021) studied the impact of inclusive dance on students with mobility impairments, focusing on those with musculoskeletal developmental disorders. Their findings showed that AI-assisted tools in inclusive dance classes significantly improved participants’ physical coordination and psychomotor skills. This research demonstrates how AI can be incorporated into physical activities like dance, providing creative and supportive avenues for motor skill development.

These AI-driven interventions enhance not just mobility but also active participation in both educational and extracurricular activities. By granting students with physical disabilities greater autonomy and engagement opportunities, technologies like gesture recognition and AI-enhanced inclusive physical activities play a vital role in boosting self-confidence, social interactions, and academic involvement, thus contributing to more inclusive educational settings.

3.3.2. Types of AI in Special Education

AI technologies in special education span intelligent tutoring systems, machine learning models, adaptive learning environments, and emotion recognition tools. These innovations enhance personalized learning, cognitive and behavioral interventions, emotional support, and physical skill development.

Dutt et al. (2022) developed an ITS using neural network classification to identify learning disabilities, offering tailored content based on student profiles. Similarly, Standen et al. (2020) evaluated the MaTHiSiS adaptive learning system, which adjusted educational content based on students’ emotional states, improving engagement among those with intellectual disabilities.

Emotion recognition AI was explored by Ouherrou et al. (2019), who integrated AI-based facial expression analysis in virtual learning environments to enhance engagement for students with learning disabilities. Toyokawa et al. (2023) introduced LEAF, an AR-based AI tool that tracks learning behaviors, offering adaptive support.

Machine learning models, such as those used by Man Kit Lee et al. (2023), analyzed handwriting patterns in Chinese children with dyslexia, enabling early diagnosis and intervention. Meanwhile, Schindler et al. (2022) combined webcam-based eye-tracking with AI-driven pattern recognition to study how DHH students process mathematical information (Schindler et al. 2022).

AI also aids educators; Rakap and Balikci (2024) found that ChatGPT-assisted IEP goal setting led to more precise and individualized plans for children with autism. Similarly, Lawal et al. (2024) tested the Hausar Kurma mobile app, which adapted English instruction for Hausa-speaking students with hearing impairments.

AI-driven physical skill development was examined by Kotevski et al. (2024), who used gesture recognition technology to aid motor skill development and handwriting in Macedonian Cyrillic. Hu and Wang (2021) employed Bayesian networks to assess progress in inclusive dance programs for students with musculoskeletal disorders.

AI’s role in mental health support was highlighted by Molokwu and Molokwu (2024), who implemented AI-enhanced counseling to reduce stress among physically challenged engineering students. Similarly, McDonald et al. (2023) found that AI-assisted reflection tools fostered empathy and self-awareness among university students.

Lastly, El Naggar et al. (2024) demonstrated how AI-mediated discussions promoted deeper cognitive engagement in exceptional learners.

These studies collectively highlight AI’s transformative potential in special education, offering personalized, data-driven interventions that address academic, behavioral, emotional, and physical challenges.

Effectiveness of AI in Special Education

AI technologies have demonstrated substantial promise in enhancing special education, particularly when integrated thoughtfully with strong instructional strategies. While AI is a powerful tool, its effectiveness often stems from its ability to scale, personalize, and optimize well-established educational methods, rather than from the AI technology alone. ITS, machine learning algorithms, and adaptive learning platforms have facilitated more responsive educational interventions. For example, Dutt et al. (2022) employed fuzzy logic and neural networks in an ITS to diagnose learning disabilities (LDs) and tailor instructional approaches. However, the core instructional models remain grounded in established special education practices; AI’s value lies in accelerating and individualizing these interventions. Similarly, Man Kit Lee et al. (2023) demonstrated the predictive power of machine learning for identifying dyslexia—yet the underlying educational response still relies on evidence-based teaching strategies. This suggests that AI primarily acts as a facilitator of effective practices rather than a creator of them.

In terms of engagement, AI-driven environments that detect affective states (Standen et al. 2020) or incorporate multimedia elements (Ouherrou et al. 2019) can increase attention and emotional connection to learning. These features amplify rather than replace traditional engagement techniques.

Applications such as AI-enhanced augmented reality (Toyokawa et al. 2023) help students reflect on their learning processes, but again, this is an extension of metacognitive instruction already validated in the literature. ChatGPT-assisted teachers (Rakap and Balikci 2024) reportedly developed more comprehensive IEPs, yet AI served as a drafting tool—empowering human decision-making, not supplanting it.

In physical skill development, AI-enabled gesture recognition (Kotevski et al. 2024) and dance programs (Hu and Wang 2021) support fine motor skills and coordination. Still, these tools draw from movement-based learning frameworks already used in physical and occupational therapy. While AI significantly enhances the delivery and adaptability of educational tools, it is most effective when built upon robust, human-centered educational foundations.

4. Discussion

This study provided valuable insights through a synthesis of secondary and primary sources, enabling the researcher to meet the research objectives. The literature review examined 15 studies on the application of AI for supporting students with disabilities in special education, revealing key findings about the use of AI in personalized learning, cognitive skill development, emotional and behavioral communication, and physical support. Additionally, the review offered a deep understanding of the theoretical foundations of AI. This section will discuss the main themes that emerged from these findings and their implications for the field of special education.

A key finding of the review is the uneven geographical distribution of AI research in special education. While Europe contributed the majority of studies, other regions were significantly underrepresented—only three from Arab countries, two from Africa, and one from the United States. This disparity points to a global imbalance in how AI is being explored and applied to support students with disabilities, underscoring the need for broader and more inclusive research efforts.

Expanding research in this field would not only help to bridge the gap between global advances and regional practices but also ensure that AI tools are adapted to better serve the specific needs of students with disabilities in the region. Furthermore, enhancing teacher training and stakeholder awareness about the potential of AI in special education can foster more inclusive educational environments.

The review’s focus on AI in special education from 2019 to 2024 is timely and justified for several reasons. First, recent technological advancements, particularly in areas like natural language processing and machine learning, have significantly enhanced AI tools for supporting students with special needs (Chemnad and Othman 2024). These advancements have enabled more accurate diagnosis of learning difficulties, such as dyslexia, as shown in studies by Man Kit Lee et al. (2023), where machine learning algorithms could differentiate dyslexic students from their typically developing peers based on language processing deficits. Additionally, AI-powered tools have been integrated into learning environments, providing real-time feedback and adaptive learning pathways that cater to the unique learning profiles of students with disabilities.

The COVID-19 pandemic accelerated the adoption of online and hybrid learning, presenting both challenges and opportunities for AI in supporting students with disabilities. AI-driven platforms played a crucial role in providing personalized support in remote settings (Li 2022). Technologies like ITS proved particularly valuable, delivering customized content and interventions that sustained student engagement and academic progress in the absence of traditional classroom support (Chemnad and Othman 2024). These developments highlight how AI can bridge gaps in access to education, particularly for students who require specialized support.

The review also emphasizes the importance of ethical considerations in AI applications for special education. As AI technologies become more widespread, ensuring that these systems comply with ethical standards and regulations is critical. Studies by Leask and Younie (2021) and Larsen and Lee (2022) discuss the ethical challenges of AI, including data privacy concerns, algorithmic bias, and the risk of over-reliance on AI at the expense of human oversight. These concerns are especially pertinent in special education, where the stakes are high, and decisions about interventions can significantly affect students’ developmental outcomes. Thus, as AI technologies become more integrated into educational systems, it is imperative that policies and guidelines are developed to ensure the responsible use of AI, particularly for vulnerable populations such as students with disabilities.

Another critical theme emerging from the review is the maturation of AI technologies, which have now reached a level of sophistication where they are being adopted in educational settings on a wide scale. This widespread adoption has led to measurable impacts on learning outcomes, as seen in the studies reviewed. For example, Rakap and Balikci (2024) demonstrated that the use of AI, specifically ChatGPT, significantly improved the quality of IEP goals, offering more comprehensive and personalized educational plans for children with autism. Similarly, AI-driven learning environments, like the ones examined by Standen et al. (2020), have shown to be effective in increasing student engagement by adapting content based on emotional and cognitive feedback, further solidifying the potential of AI to personalize learning experiences.

The review of AI applications in special education also highlights the increasing research interest in AI-driven assistive technologies. AI is proving to be a game-changer in providing real-time feedback to help students with disabilities develop critical skills, such as fine motor skills and emotional regulation. Studies like those by Kotevski et al. (2024) and Hu and Wang (2021) illustrate how AI tools can aid in developing physical and cognitive skills through interactive and personalized interventions. These technologies are helping to bridge gaps in traditional special education, offering new ways for students to engage with learning material and develop essential life skills.

In conclusion, this review has provided a comprehensive overview of recent advancements in the use of AI for special education, synthesizing evidence from multiple studies to highlight the effectiveness of AI in personalized learning, cognitive skills development, emotional engagement, and physical support. However, more research is needed, particularly in underrepresented regions like the Middle East, to ensure that AI’s full potential in special education is realized globally. Addressing this research gap and continuing to advance AI technologies in education will be crucial for improving learning outcomes and promoting inclusivity for students with disabilities. The findings underscore the need for further investment in AI research and development, as well as the importance of ethical considerations in the deployment of these powerful tools.

4.1. Impact of AI on Outcomes

The integration of AI into special education is not simply a technological advancement—it fulfills a critical need for individualized, scalable, and responsive educational interventions that would be otherwise difficult to implement at scale. Students with disabilities often require continuous monitoring, personalized instruction, and real-time adjustments—needs that AI is uniquely equipped to meet efficiently and consistently. While traditional educational methods are foundational, their implementation can be limited by time, staffing, and subjectivity. AI helps address these limitations by:

• Automating diagnostic processes, allowing for earlier and more accurate identification of disabilities (e.g., dyslexia, ADHD);
• Adapting learning content dynamically based on real-time student performance data;
• Enhancing accessibility through speech recognition, gesture tracking, and predictive text tools that support students with communication or motor impairments;
• Scaling emotional and behavioral support, using affective computing to detect distress and prompt timely interventions.

4.1.1. Personalized Learning and Academic Achievement

AI’s capacity to personalize learning has positively impacted academic achievement for students with disabilities by aligning content with individual learning needs, pace, and preferences. ITS and adaptive learning platforms facilitate deeper engagement and retention through continuous, real-time assessment and feedback (Chemnad and Othman 2024; Leask and Younie 2021; Rakap and Balikci 2024). These technologies can support differentiated instruction and empower teachers to intervene more precisely and promptly. However, it is essential to recognize that this effectiveness depends not on AI alone, but on how educators use these tools within effective instructional strategies. AI does not inherently understand educational nuance; rather, it processes data based on patterns learned from large datasets. In special education contexts—where data may be scarce or not representative—this reliance on large-scale training data presents a major limitation. Moreover, without human oversight, AI-driven recommendations risk reinforcing existing inequities or misinterpreting atypical learning patterns.

Thus, while AI tools augment personalization, their success hinges on thoughtful human application and ongoing validation by special education professionals.

4.1.2. Enhanced Communication and Social Skills

AI-powered communication devices and voice recognition software have been instrumental in supporting students with speech and language disorders, enabling better expression and interaction (Popenici and Kerr 2017; Salas-Pilco 2020). These tools foster inclusion by giving non-verbal or minimally verbal students greater agency in the classroom. As demonstrated by Rakap and Balikci (2024), AI can even assist teachers in crafting more targeted communication-related IEP goals. Yet again, these gains are not automatic. The tools require extensive customization and contextual understanding—skills that only educators and therapists bring to the table. Furthermore, AI-driven language tools may not account for cultural, emotional, or developmental nuances in student communication unless explicitly trained to do so, which remains a challenge given the diversity of special education populations.

4.1.3. Cognitive and Behavioral Interventions

AI systems have proven effective in identifying emotional states, tracking behavioral patterns, and supporting timely interventions (Chiu et al. 2023). Such systems assist teachers in managing classroom dynamics and help students build emotional regulation skills through feedback mechanisms. Nevertheless, the data-driven nature of these tools means they may oversimplify complex emotional or behavioral conditions. An AI tool might detect a student’s silence as disengagement, when it could reflect concentration or sensory overload. Without careful calibration and interpretation by educators, such tools risk mislabeling behaviors, leading to inappropriate interventions. This reinforces the need for AI to serve as a decision-support tool—not as a decision-maker.

4.1.4. Emotional Support and Well-Being

AI technologies, including emotion recognition systems and virtual environments, have shown promise in supporting students’ emotional well-being (Almufareh et al. 2023; Popenici and Kerr 2017; Tahiru 2021). These systems promote emotional awareness and regulation, which are especially crucial for students dealing with anxiety, autism spectrum disorders, or trauma. However, emotional support is inherently human. While AI can recognize emotional cues and provide prompts or suggestions, it lacks empathy, cultural context, and the capacity to interpret subtle emotional shifts. Over-reliance on AI for emotional intervention could unintentionally reduce opportunities for authentic human connection, which is often vital in therapeutic and educational settings.

4.1.5. Physical Independence and Mobility

AI-enhanced robotic devices and smart prosthetics contribute to physical independence for students with mobility impairments, enabling greater classroom participation and confidence (Kabudi et al. 2021; Ouherrou et al. 2019; Schindler et al. 2022). These tools can meaningfully improve quality of life and support holistic development. Still, such technologies can be prohibitively expensive and difficult to maintain. Customization is often required to meet individual physical needs—another area where human expertise is irreplaceable. Furthermore, overemphasis on AI solutions may draw attention away from other critical accessibility investments, such as inclusive design and physical environment adaptations.

4.2. Adoption and Barriers: A Balanced Perspective

Despite its promise, AI adoption in special education remains uneven. Major barriers include limited access to infrastructure, insufficient training for educators, and uncertainty about how to effectively integrate AI into existing curricula (Chemnad and Othman 2024). Many teachers report feeling ill-equipped to use these tools, especially in high-needs or resource-constrained environments. Moreover, AI’s effectiveness depends on the availability of high-quality, diverse, and inclusive datasets. In many special education contexts, such data is limited, leading to tools that may not generalize well across different disability types or demographic groups.

Ethical challenges also persist. As noted in Section 3, concerns around data privacy, algorithmic bias, and the dehumanization of educational experiences remain significant (Sharma et al. 2023). AI tools are only as unbiased as the data they are trained on, and students with disabilities, who often represent marginalized groups, are particularly vulnerable to unintended harms. Crucially, the responsibility for ensuring inclusivity and effectiveness in AI-supported education lies with educators, administrators, and policymakers, not the technology itself. AI is a facilitator—not a fix-all—and its integration must be guided by educational values, empirical evidence, and a commitment to equity and inclusion.

5. Limitations

Despite these promising findings, several limitations across the reviewed studies temper the current enthusiasm for AI integration in special education:

• Terminology and terms used: The search terms may not have captured all types of disabilities individually, as the strategy did not systematically include specific disability categories (e.g., autism, physical disabilities, learning disabilities). Additionally, synonyms for “students”, such as “pupils” or “learners”, were not included, which may have limited the comprehensiveness of the search. As a result, some relevant studies may have been unintentionally excluded, potentially leading to a narrower or biased perspective. Future research could benefit from employing a broader range of search terms to improve the generalizability of findings. Similarly, the use of “disabilities” as a primary search term, rather than broader terms such as “special educational needs”, may have constrained the scope of the review. Future work may consider incorporating both disability-specific and broader educational needs terminology to better capture the diversity of relevant studies.
• Controlled Research Conditions: A notable proportion of the studies were conducted in highly controlled experimental environments, limiting ecological validity. These settings do not accurately reflect the complexity of real classrooms, where multiple variables—ranging from diverse student needs to inconsistent access to resources—impact educational outcomes. Consequently, the practical applicability of these findings to everyday teaching contexts remains uncertain.
• Geographic Distribution: The review reveals a notable imbalance in the geographical distribution of studies, with the majority conducted in Europe and East Asia. Other regions—including the Arab world, Africa, and even North America—were less represented. This uneven distribution does not necessarily reflect economic capacity or technological development, as evidenced by the limited number of studies from countries like the United States and the UAE. Rather, it points to a broader need for diversified research efforts that capture a wider range of educational systems, cultural contexts, and implementation practices. Addressing this gap would strengthen the global relevance and applicability of AI in special education.
• Lack of Longitudinal Data: Current research heavily favors short-term studies, often focusing on immediate improvements in test scores, engagement, or behavioral metrics. There is a critical need for longitudinal studies that investigate how AI interventions impact students over the course of several academic years. Such research would offer deeper insight into how AI influences not only cognitive development but also social-emotional growth, independence, and life outcomes.
• Underexplored Ethical Implications: Ethical considerations, particularly surrounding data privacy, algorithmic bias, and the risk of dehumanizing the educational process, are underrepresented in the current literature. The potential misuse of student data, unintended reinforcement of biases, and reduction of education to automated routines raise serious concerns—especially when dealing with vulnerable populations such as students with disabilities. More focused research on these dimensions is essential to ensure AI is implemented responsibly and inclusively.

6. Implications for Practice and Future Research

The findings underscore that AI is not a standalone solution, but rather a powerful tool that must be guided by human expertise, ethical frameworks, and effective instructional strategies. Teachers, support staff, and school administrators bear the responsibility of ensuring that AI is used thoughtfully and inclusively. AI must augment—not replace—the human connection that is so vital in special education. To realize the promise of AI in this domain, future research should

• Conduct classroom-based, longitudinal studies to evaluate the sustained impacts of AI on students with varying disabilities;
• Include schools from diverse geographic and socioeconomic contexts to assess global applicability and scalability;
• Prioritize ethical guidelines and transparent decision-making processes to protect student welfare;
• Develop interdisciplinary collaborations between educators, technologists, ethicists, and policymakers to ensure the holistic development of AI systems.

7. Conclusions

This review underscores the growing potential of artificial intelligence to enrich special education through its ability to support individualized instruction, enhance communication, and foster cognitive, emotional, and physical development for students with disabilities. While AI presents a valuable set of tools for inclusive education, its effectiveness depends heavily on thoughtful, ethical implementation by educators and stakeholders.

Moving forward, it is essential that AI be positioned not as a replacement for human educators, but as a complement that enhances their capacity to meet diverse student needs. To realize its full benefits, AI technologies must be integrated in ways that prioritize equity, maintain human connection, and respect the complexities of special education contexts. Continued interdisciplinary collaboration, inclusive design practices, and a commitment to ongoing evaluation will be vital to ensure AI’s role in education is both innovative and responsible.