Document Type : Research Paper
Authors
1 M.A. in Educational Technology, Hamedan, Iran. E-mail: sinacfu.sb@gmail.com
2 Department of Educational Administration, Farhangian University, Tehran, Iran. E-mail: kh.zandi@cfu.ac.ir
3 Corresponding Author, Department of Educational Sciences, Farhangian University, Tehran, Iran. E-mail: A.allahkarami@cfu.ac.ir
Abstract
ABSTRACT
The present study aims to conduct a qualitative content analysis of research examining the role of artificial intelligence (AI) in the education of students with special needs. In this regard, 18 scholarly articles published between 2017 and 2025 were reviewed using qualitative content analysis and assessed for scientific quality through the CASP checklist. The analysis was conducted in two dimensions: formal and substantive. In the formal dimension, article characteristics such as research methodology, year of publication, type of disability studied, sample size, academic rank and disciplinary background of authors, and countries where the studies were conducted were examined. The results indicated that most studies were conducted in 2023 and 2024, predominantly employed quantitative methods, and mainly focused on disabilities such as autism spectrum disorder and reading disabilities. In the substantive dimension, findings revealed that AI applications are primarily categorized into three main areas: 1) diagnosis, screening, and treatment of learning disabilities, 2) support for emotional and behavioral needs, and 3) skill enhancement and improvement of academic performance. Additionally, three significant outcomes of AI use in educating students with special needs were identified: improved learning and personalized instruction, increased teacher efficiency, and enhanced educational equity. Nonetheless, challenges such as data scarcity, high costs, and ethical considerations remain pertinent in AI applications. The findings of this study can guide educational policymakers in developing inclusive and technology-driven strategies.
Keywords: Special education technology, artificial intelligence, qualitative content analysis, students with special needs
Cite this Article: Barak, S., Zandi, K., & Allahkarami, A. (2025). A Qualitative Meta-Analysis of Studies on Artificial Intelligence in the Education of Students with Special Needs. Psychology of Exceptional Individuals, 15(60), 57-83. https://doi.org/10.22054/jpe.2026.86180.2819
© 2025 by Allameh Tabataba'i University Press
Publisher: Allameh Tabataba'i University Press
Extended Abstract
Introduction
Students with special educational needs frequently encounter physical, cognitive, or sensory challenges that severely limit their access to high-quality learning environments. Artificial intelligence (AI) possesses significant potential to mitigate these barriers through tools such as adaptive learning software, smart tutoring systems, and personalized feedback mechanisms. As noted by academic researchers, the integration of AI can bridge the gap in educational equity for vulnerable groups (Zawacki-Richter et al., 2019). While AI's overarching impact on education is widely acknowledged, its specific applications for diverse disabilities require a more nuanced exploration. The present study addresses this gap by conducting a qualitative meta-analysis to systematically review, evaluate, and synthesize the role and performance of AI in special needs education.
Research Questions
What are the formal characteristics of the published studies regarding the application of AI for students with special needs?
For which specific challenges and problems of students with special needs has AI been employed?
What are the benefits and outcomes of using AI in the learning environments of students with special needs?
Literature Review
Previous literature extensively demonstrates the transformative potential of AI in educational settings. Studies have highlighted that AI technologies, such as robotics and natural language processing, are effective in educating and treating children with cognitive or physical limitations (Momeni & Majzoobi, 2011). Research indicates that AI-driven interactive tools enhance daily activities and foster independence for individuals with disabilities. For instance, recent studies have developed machine learning-based classification models to support university students with dyslexia using personalized tools (Zingoni et al., 2024). Furthermore, adaptive learning platforms have been shown to optimize educational strategies by identifying learning patterns. However, despite these promising advancements, a critical reflection reveals a lack of comprehensive focus on the diverse spectrum of special needs. Most existing studies address general AI applications, leaving a significant gap regarding the specific requirements of student-teachers and practitioners in this field (Goli, 2024).
Methodology
This study employed a qualitative meta-analysis methodology, which is a systematic study of prior research that allows for the comparison and synthesis of findings. The statistical population included research articles focusing on AI and the education of students with special needs. A comprehensive search was conducted across international databases (Wiley, Taylor & Francis, EBSCO, ERIC) and regional databases, covering the period from 2017 to 2025. Search queries utilized logical combinations of keywords such as "Artificial intelligence" and "Special Needs". The selection process involved screening titles and full texts against predefined inclusion criteria. The Critical Appraisal Skills Programme (CASP) checklist was utilized by two independent reviewers to evaluate scientific quality. Ultimately, 18 peer-reviewed articles met the criteria and were included in the final qualitative content analysis.
Results
The analysis of the formal dimension revealed that 50% of the reviewed studies were published in 2024, indicating a surge in recent interest. Quantitative research designs were predominant (66.6%). Regarding disabilities, autism spectrum disorder (38.89%) and dyslexia (33.33%) were the most frequently studied. The substantive analysis identified three primary areas of AI application: 1) diagnosis and screening of learning disabilities; 2) emotional and behavioral support; and 3) skill enhancement. Furthermore, the synthesis of outcomes indicated that AI leads to improved personalized instruction, increased teacher efficiency, and enhanced educational accessibility.
Discussion
The findings highlight that AI in early diagnosis significantly improves accuracy. Machine learning algorithms can increase the accuracy of identifying conditions to between 85% and 95%. In terms of emotional support, AI tools like chatbots offer real-time interventions, potentially reducing emotional distress. Additionally, AI facilitates skill enhancement by generating personalized mathematical writing models. Real-time transcription tools significantly improved communication in virtual classrooms for deaf students. However, successful implementation faces obstacles such as data scarcity and ethical concerns regarding data privacy.
Conclusion
Artificial intelligence serves as a critical catalyst in transforming special needs education. By acting as an intelligent, personalized tutor, AI tailors educational content to the unique learning pace of each student. As emphasized in recent research, the impact of AI on educational planning for children with special needs is profound (Azimfar et al., 2024). This individualization elevates academic performance and liberates teachers from repetitive tasks. Ultimately, AI promotes educational equity by ensuring that learners with diverse disabilities have access to supportive environments. Future research should prioritize investigation into inclusive, technology-driven strategies to overcome ethical challenges.
Ethical Considerations
Adherence to Research Ethics Principles
The authors have observed all ethical principles in conducting and publishing this scientific research, and this is confirmed by all of them.
Author Contributions
All authors contributed equally to the conceptualization of the article and the writing of the original and subsequent drafts.
Conflict of Interest
“The authors declare no conflict of interest.”
Funding
This article was conducted independently by the authors without any financial support.
Acknowledgements
We hereby express our sincere gratitude to all the professors, colleagues, and researchers who assisted in the development of this research through their insights, resources, and shared experiences.
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