Generative artificial intelligence has entered classrooms with extraordinary speed, enabling students to produce assignments that appear refined yet bypass genuine learning. At the same time, social disconnection, amplified by parasocial relationships and excessive reliance on digital platforms, is weakening community bonds and undermining educational outcomes. This paper argues that music education policy must respond to these twin pressures by prioritizing AI-resilient pedagogy, which emphasizes listening, performance, collaboration, and interpretive reasoning. Such approaches preserve authenticity while affirming music’s role as a unifying force in an age of technological disruption and social fragmentation.Methodologically, the study frames policy considerations through three pedagogical case studies using the “AI Stress Test” model applied to classroom assignments. Each task was first uploaded to a generative AI system to determine whether it could competently complete the task. If the AI produced a credible response, the assignment was redesigned to require human-centered processes such as critical listening, performance, and interpretive reasoning. By positioning classroom practice as a testing ground, the study highlights both the risks of AI-generated work and the collaborative opportunities that arise when tasks prioritize discussion and teamwork, offering evidence to guide institutional and policy decisions on AI’s role in music education.Findings highlighted both the potential and the limitations of AI when applied to specific classroom tasks. In Jazz Theory, a stress test analysis of "The Eternal Triangle" (1957) revealed that while AI could generate plausible commentary, student groups produced more accurate results through timestamp mapping, negotiation, and contextual reasoning. A second case study, the “Chord Progression in a Hat” exam, demonstrated that embodied performance and real-time aural recognition remain inaccessible to automation. The third, a Pop Music History presentation, revealed that although AI could assist with efficient content preparation, success required AI literacy alongside students’ ability to present, defend, and theorize ideas in peer dialogue.These findings support a policy framework with three objectives: pedagogical adaptation that shifts tasks from reproduction to interpretation and practice; institutional policy on students’ use of AI that assumes its widespread use and embeds safeguards for authentic assessment; and digital literacy and institutional commitment that equip students and teachers to critically evaluate generative systems. Framed as policy, these objectives embed collaboration and critical engagement into curricula, ensuring that institutions preserve inclusivity and authentic human connection while adapting to an AI-saturated world.