The integration of generative artificial intelligence (AI) into United States higher education has moved beyond experimental pilot programs to become a structural reality within academic departments. Tsunagu Ichikawa and Elizabeth Olsen (2025) observed this shift even in specialized fields like medical education, where policies are rapidly evolving to address the presence of AI in clinical training and research. This technological infusion challenges long-standing pedagogical frameworks, as institutions grapple with the necessity of fostering AI literacy while preserving the cognitive rigor traditional education demands. Unlike previous digital transitions, the generative nature of these tools disrupts the assessment of student competence, making the evaluation of pedagogical consequences a matter of systemic urgency. While the potential for differentiated instruction—an area Muhammad Nanang Suprayogi and Suwarno (2025) identify as crucial for inclusive education—remains high, the prac