The rapid proliferation of generative artificial intelligence within American classrooms has forced an immediate recalibration of instructional methodologies. Unlike previous technological shifts, the integration of large language models fundamentally alters the cognitive processes associated with student learning, particularly in literacy education and specialized fields such as medicine (Schrag & Short, 2025; Rui Li & Wu, 2025). Recent surveys indicate that college students are already embedding these tools into their personal and academic workflows, often outpacing the development of formal institutional guidance (Basch & Hillyer, 2025). Such widespread adoption necessitates a rigorous evaluation of how automated systems redefine the relationship between student, educator, and knowledge. While AI offers significant pedagogical affordances, its presence complicates traditional metrics of academic integrity and student autonomy. The US educational system faces a dual challenge: levera