The rapid proliferation of Large Language Models and generative algorithms has catalyzed an unprecedented transformation within the American educational sector. This shift represents more than a mere technological update; it signifies a fundamental restructuring of how knowledge is produced, disseminated, and validated. As educational institutions in the United States grapple with these advancements, the tension between traditional pedagogical values and the efficiency of automated systems has become a central concern for administrators and faculty alike. Research conducted by Basch and Hillyer (2025) underscores this transition, revealing that undergraduate students possess complex, often contradictory, attitudes toward the ethical implications of artificial intelligence (AI). While students recognize the utility of these tools for enhancing productivity, their understanding of the underlying algorithmic biases remains fragmented. This gap between technological adoption and ethical li