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AccessiLearnAI: An Accessibility-First, AI-Powered E-Learning Platform for Inclusive Education

Guardado en:
Publicado en:Education Sciences vol. 15, no. 9 (2025), p. 1125-1171
Autor principal: Stelea, George Alex
Otros Autores: Robu, Dan, Sandu Florin
Publicado:
MDPI AG
Materias:
Teaching
Pedagogy
Students
Usability
Handicapped accessibility
Training
Instructional design
Privacy
Inclusive education
Distance learning
Disability
Data integrity
Inclusion
Artificial intelligence
Personalized learning
Online instruction
Integrated approach
Compliance
Customization
Adaptive learning
Semantics
Influence of Technology
Distance Education
Addition
Academic Achievement
Learning Experience
Academic Accommodations (Disabilities)
Educational Technology
Management Systems
Developmental Continuity
Educational Change
Computer Oriented Programs
Access to Education
Learning Management Systems
Electronic Learning
Comparative Education
Comparative Analysis
Educational Demand
Opportunities
Educational Environment
Course Content
Learner Engagement
Educational Needs
Algorithms
Acceso en línea:Citation/Abstract
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Resumen:Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education.
ISSN:2227-7102
2076-3344
DOI:10.3390/educsci15091125
Fuente:Education Database