The swift progression of Artificial Intelligence (AI), especially generative models and large language models (LLMs), is reshaping the realm of higher education. This paper examines the pedagogical, epistemological, and institutional ramifications of incorporating AI tools into science curricula, specifically in the fields of physics, chemistry, and computing. This study synthesizes recent literature and conducts a thorough analysis of five notable case studies-AI-University, Course Assist, Auto Tutor, Kwame for Science, and India's Virtual Labs-identifying essential success factors such as epistemic alignment, transparency, contextual adaptation, and formative feedback. The results indicate that the integration of AI is most efficacious when based on constructivist and dialogic pedagogical frameworks, and when tools are intentionally designed to align with disciplinary structures and learner requirements. Issues including faculty readiness, algorithmic bias, infrastructural inequity, and assessment reliability are also addressed. The paper concludes with five pragmatic recommendations to assist institutions and curriculum designers in the responsible and effective implementation o
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