<![CDATA[Growing adoption of hybrid learning has accelerated the integration of Artificial Intelligence (AI) to deliver personalized educational experiences that accommodate diverse learner characteristics, enhance instructional effectiveness, and support data-informed pedagogical decisions. Increasing reliance on AI-driven personalization has simultaneously introduced concerns regarding algorithmic transparency, learner autonomy, data privacy, and educational equity, creating a need for balanced evaluation of its educational potential and associated risks. This study aimed to examine the opportunities, risks, and pedagogical implications of AI-driven personalization within hybrid learning environments. A mixed-methods sequential explanatory design was employed, combining quantitative analysis of learning analytics and academic performance with qualitative investigation through interviews, classroom observations, and reflective journals. Participants included undergraduate students and lecturers engaged in AI-supported hybrid learning across multiple disciplines. Findings revealed that AI-driven personalization significantly improved academic achievement, learner engagement, self-regulated learning, learning satisfaction, and course completion while maintaining high recommendation accuracy and adaptive instructional responsiveness. Qualitative evidence further demonstrated that effective implementation depended on meaningful teacher facilitation, transparent algorithmic support, and responsible institutional governance addressing privacy and ethical concerns. Results indicate that Artificial Intelligence enhances hybrid learning most effectively when functioning as a pedagogical partner rather than a replacement for educators. Balanced integration of adaptive technologies, human expertise, ethical governance, and learner-centered instructional design provides a sustainable framework for responsible educational innovation and long-term digital transformation.]]>
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