AI-Based Adaptive E-Learning to Enhance Inclusive Learning Accessibility

Abstract

The increasing diversity of learners in digital education highlights the need for flexible learning environments that can accommodate different learning needs, abilities, and levels of accessibility. This study examines the role of AI-based adaptive e-learning in improving accessibility and learning effectiveness within inclusive education. A quantitative approach was employed through a survey involving 223 respondents from diverse educational backgrounds. Data were collected using a five-point Likert-scale questionnaire and analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS) to examine the relationships between AI-based adaptive e-learning, learning accessibility, and learning effectiveness. The results indicate that AI-based adaptive e-learning has a positive and significant influence on learning accessibility and learning effectiveness. The adaptive features enable learning content, activities, and experiences to be adjusted according to individual learner needs, helping reduce accessibility barriers and creating a more inclusive learning environment. Learning accessibility also contributes positively to learning effectiveness, demonstrating that flexible and accessible digital learning environments can support better learning experiences and outcomes. These findings confirm that AI-based adaptive e-learning provides an effective technological approach for strengthening accessibility and inclusivity in digital education. The integration of adaptive AI technologies into e-learning platforms can therefore support more personalized, accessible, and effective learning experiences for diverse learners.