This research presents a comprehensive framework for AI-Enhanced Open Educational Resources (OER) Observatory that transforms static repositories into dynamic, personalized learning ecosystems. The Observatory integrates three core components: AI-driven recommendation algorithms for adaptive learning pathways, real-time learning analytics for continuous assessment, and Universal Design for Learning (UDL) principles to ensure accessibility across diverse learner populations. The research employs a mixed-methods approach combining theoretical framework development with simulation-based validation using synthetic educational data. Three implementation scenarios (Conservative, Intermediate, and Advanced) were systematically evaluated across utility, fairness, privacy, and governance dimensions using established datasets (OULAD, EdNet) as reference models. Each scenario underwent 100 bootstrap simulation replicates to assess performance characteristics and institutional viability. Results demonstrate that the Intermediate scenario optimally balances educational effectiveness with institutional feasibility, achieving significant improvements in predictive accuracy while maintaining manageable governance overhead and high institutional acceptability. The framework addresses critical gaps in current AI-Enhanced educational systems by preserving open, collaborative OER principles while leveraging AI capabilities for personalization and analytics.
