Enhancing Adaptive Learning with Generative AI for Tailored Educational Support for Students with Disabilities
Nesren Farhah, Asim Wadood, Ahmed Abdullah Alqarni, M. Irfan Uddin, Theyazn H. H. Aldhyani
Abstract
This paper explores the integration of generative artificial intelligence (AI) into adaptive learning systems to create customized learning support aids for students with disabilities, as traditional educational aids commonly fail to meet the diversity in the needs of these learners, and generative AI has shown innovative solutions that can offer real-time adaptation to content with personalized learning experiences. This paper introduces ALGA-Ed, a novel adaptive learning system utilizing generative AI that includes (i) a user profile module that captures cognitive, sensory, and behavioral profiles; (ii) generative AI models that create personalized multimodal content in real-time; (iii) an adaptive feedback mechanism using reinforcement learning to adjust content delivery based on real-time engagement metrics dynamically; and (iv) a real-time monitoring system that tracks progress and adapts learning pathways accordingly. The framework leverages heterogeneous datasets, including real and synthetic data, to effectively address diverse disability profiles. Pilot studies demonstrate the effectiveness of the framework in improving participation, retention, and learning outcomes for students with disabilities. This study enhances adaptive learning by encouraging inclusion via AI-driven tailoring and providing a basis for further advancements in AI-powered education catered for children with impairments. The source code for this research will be publicly available at https://github.com/aasimwadood/ALGA-Ed .