The proliferation of Large Language Models across the American educational landscape has catalyzed a shift from traditional instructional methods toward automated, data-driven learning environments. Schrag and Short (2025) demonstrate that computational linguistics are already reshaping secondary literacy education, suggesting that AI integration is no longer a futuristic prospect but a present reality. The velocity of this adoption often outpaces traditional curricular reform. Meng and Luo (2024) observe that teaching strategies in the United States increasingly emphasize technological fluency compared to international counterparts, yet this transition is unfolding unevenly. Taylor and Stan (2024) identify a stratified nature in AI research funding within U.S. systems, indicating that institutional capacity to adapt often depends on existing financial resources. These disparities necessitate a rigorous evaluation of how AI-driven tools influence educational equity and institutional st