What question did this study set out to answer?

The research aims to develop an AI-driven recommender system that enhances accessibility and personalization for MOOC learners, particularly those with disabilities.

February 28, 2026Open Access

RECMOOC4ALL: an AI-driven recommender system for personalized and accessible MOOC recommendations

Key Points

The research aims to develop an AI-driven recommender system that enhances accessibility and personalization for MOOC learners, particularly those with disabilities.
Developed an AI-enhanced hybrid recommender system called RECMOOC4ALL.
Integrated content-based filtering, collaborative filtering, sentiment analysis, and dropout-risk modeling.
Used a multi-platform dataset with thousands of courses and interaction sessions for evaluation.
Conducted a user study to assess satisfaction and accessibility.
Achieved lower error metrics (RMSE 0.80, MAE 0.63) compared to baseline systems.
Improved ranking quality (Precision@5 0.33, Recall@5 0.36, NDCG@5 0.34).
User study participants reported high satisfaction with an average score of 4.28 for overall satisfaction.
Log-based analyses showed increased click-through rates and higher course completion compared to baseline.

Abstract

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.

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