This study analyzes how U.S. universities reconfigure academic integrity during the 2024–2025 cycle in response to widespread generative AI adoption. The analysis foregrounds three loci: student ignorance and metacognitive blind spots; the expanded remit of Academic Integrity Officers prioritizing education over punishment; and deliberate AI-enabled misconduct that exposes the evidentiary limits of detection technologies. A mixed-methods design integrates a multi-site review at Arizona State University, Montclair State University, and Cornell University with synthesis of surveys, policies, and faculty development guidance. Findings show that detector outputs function as conversational prompts rather than adjudicative proof, necessitating dialogic resolution standards, process evidence, and due-process safeguards to reduce false positives and bias. Institutions that center syllabus clarity, assignment-level AI permissions, and transparent attribution norms report fewer gray-area violations and higher student comprehension of expectations. Pedagogical redesign—personalized, context-bound prompts; scaffolded drafting with reflections; inclass writing and oral defenses; and structured ―AI-in-the-open‖ tasks that demand critique and verification—reduces incentives to outsource cognition while strengthening targeted learning outcomes. The study maps integrity work to labor-market demands for AI fluency, arguing for frameworks that cultivate ethical AI competence rather than prohibitions that suppress skill formation. Attention to accessibility and neurodiversity remains pivotal; integrity regimes that ignore assistive use cases risk exacerbating inequities and chilling legitimate accommodations. The article proposes a sustainable governance model coupling principled authorization and attribution with evidence-based adjudication, faculty training aligned to curricular cycles, and continuous assessment improvement. Collectively, these strategies reposition academic integrity as a design problem aligned with AI literacy and graduate employability.