Instructors and researchers are pressing for reliable frameworks and tools to detect large‑language‑model use so that analytical writing assignments can survive the AI era. One prominent faculty member described eliminating take‑home analytical papers in favor of in‑class handwritten exams after widespread LLM use made grading and assessment unreliable. Scholars and technologists argue that robust detection protocols would let instructors choose whether to ban or incorporate AI. Others call for redesigned assignments, viva voce defenses, and project‑based assessments to preserve learning outcomes. The debate bridges K–12 and higher education, with advocates on both sides urging measured policies, audit‑grade detection, and pedagogical redesign. Universities that adopt detection tools and new assessment models first may preserve the integrity of degree credentials, but they must also navigate privacy, fairness, and technical accuracy in any AI‑use detection system.