Abstract Massive Open Online Courses (MOOCs) have widened access to education globally; however, they continue to face course overload, limited guidance, insufficient context-aware personalization, and high dropout rates. These challenges are even more pronounced for learners with disabilities, due to limited accessibility support and the absence of recommendation systems that adapt to individual accessibility needs. This study introduces RECMOOC4ALL, an AI-enhanced, hybrid multi-signal recommender system that embeds accessibility as a first-class computational feature within its ranking logic. Guided by Universal Design for Learning principles, the system integrates content-based filtering, neural collaborative filtering, sentiment analysis, and dropout-risk modeling, while encoding course metadata such as caption availability, screen-reader compatibility, and keyboard navigability. Evaluation using a multi-platform dataset (11,600 courses, 42,000 interaction sessions, and 35,000 textual reviews) shows that the accessibility-aware hybrid achieves lower error (RMSE 0.80, MAE 0.63) and higher ranking quality (Precision@5 0.33, Recall@5 0.36, NDCG@5 0.34) than CBF and CF/NCF baselines. A user study (n = 50) reported satisfaction of 4.28 ± 0.47 and accessibility satisfaction of 4.36 ± 0.39, and log-based analyses indicated increased click-through, longer dwell time, and higher course completion for recommendations produced by RECMOOC4ALL compared with baseline configurations. These gains were achieved while prioritizing accessibility-compliant courses via calibrated ranking weights. RECMOOC4ALL demonstrates that accessibility can be operationalized at the computational core of recommendation, offering a scalable and empirically validated blueprint for equitable AI in MOOCs.
Mrayhi et al. (2026) studied this question.