The acceleration of generative artificial intelligence (AI) integration across United States educational institutions has transitioned from a peripheral innovation to a central systemic challenge. Meng and Luo (2024) observe that while AI enhances teaching strategies through comparative efficiency, the American context specifically grapples with decentralized implementation and varied institutional readiness. This technological surge is not merely a pedagogical shift but a fundamental restructuring of how knowledge is produced and verified within the digital economy. Goralski and Górniak-Kocikowska (2022) suggest that the domestic landscape's reliance on private-sector innovation forces public institutions into a reactive stance, often prioritizing software adoption over comprehensive ethical frameworks. The core problem involves an escalating conflict between the efficiency of AI-mediated learning and the preservation of rigorous academic standards. Beneath the promise of personalized