The rapid deployment of generative technologies has fundamentally disrupted the American pedagogical landscape, forcing a re-evaluation of long-standing instructional models. Bibliometric analyses covering the decade from 2013 to 2023 reveal a sharp trajectory of innovation, moving from niche computational applications to ubiquitous classroom instruments (Afzaal & Xiao, 2024). This shift is not merely technical but political (Li, 2025). The urgency of this evaluation is underscored by the speed at which automated systems have moved from experimental phases to core components of academic scholarship and writing (Ganguly & Johri, 2025). While proponents argue that machine learning enhances efficiency, the widespread availability of these instruments threatens the traditional development of student analytical skills. Systematic reviews suggest that the ease of producing synthetic text may bypass the cognitive friction necessary for deep learning (Nguyen & Trương, 2025). This challenge is