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AI-Supported Learning Platforms for Students With Developmental Disabilities: A SWOT Analysis Approach
Aydın Bulut1, Sibel Oğuz Haçat1
1Kastamonu University, Kastamonu, Türkiye.
Journal of Intellectual Disability Research : JIDR
|April 23, 2026
Summary
Artificial Intelligence (AI) in special education is shifting towards inclusive learning, enhancing personalization and accessibility. However, realizing AI's full potential requires addressing ethical, infrastructural, and teacher competence challenges for equitable educational justice.
Area of Science:
- Education Technology
- Special Education
- Artificial Intelligence
Background:
- Examines the evolving role of Artificial Intelligence (AI) in developmental disability education.
- Focuses on the paradigm shift from diagnosis-centered to inclusive, learning-oriented approaches.
- Integrates frameworks such as STS, UDL, and TPACK.
Purpose of the Study:
- To analyze research trends, conceptual developments, and international collaborations in AI for special education.
- To evaluate the effectiveness and challenges of AI-supported learning platforms.
- To identify key factors for a sustainable and human-centered AI ecosystem in education.
Main Methods:
- Bibliometric analysis of 1221 publications from 2008-2025.
- SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of AI in education.
- Data sourced from Web of Science.
Main Results:
- AI-powered platforms improve personalized learning, accessibility, and data-driven pedagogy.
- Significant constraints include ethical, pedagogical, and infrastructural challenges.
- AI is a transformative but fragile innovation contingent on teacher skills, governance, and equity.
Conclusions:
- A sustainable AI ecosystem necessitates alignment in pedagogical development, governance, and global participation.
- AI can serve as a catalyst for educational justice in special education.
- Addressing challenges is crucial for maximizing AI's benefits in inclusive education.
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