The rapid proliferation of generative artificial intelligence within the United States educational landscape has outpaced established regulatory frameworks and pedagogical standards. Unlike previous technological shifts, the current integration of machine learning and natural language processing (NLP) represents a fundamental restructuring of how knowledge is mediated, assessed, and distributed. Göçen and Asan (2023) characterize this shift as a convergence of deep learning and voice recognition that creates unprecedented risks and benefits for institutional stability. Within the American context, this transformation is not merely technical; it is deeply embedded in the socio-economic fabric of a decentralized school system. The urgency of this examination stems from the reality that while AI tools offer personalized learning pathways, they simultaneously threaten to exacerbate existing achievement gaps if deployed without systemic oversight. Current scholarship reflects a growing preo