Two commentary pieces from sector outlets warned that AI use in higher education is reshaping learning in ways that merit urgent scrutiny. One argued the greatest risk is not cheating but an erosion of learning as faculty and institutions outsource core instructional tasks to generative tools. Another warned that AI will ‘break’ traditional assessment models before replacement systems are ready. Authors flagged that AI already permeates nonclassroom systems—analytics that flag at‑risk students, automated scheduling, and administrative decision support—while also altering pedagogy as instructors and students use large language models for drafting, coding and synthesis. The pieces call for immediate redesigns of assignments, assessment frameworks, and learning outcomes to preserve academic integrity and learning quality. Provosts and assessment offices should accelerate policy development: adopt clear AI use guidelines, redesign summative assessment to require process evidence, and invest in faculty development for prompt engineering and AI‑integrated pedagogy. Institutional research offices must update learning analytics to distinguish tool‑assisted work from mastery. Regulatory attention and public scrutiny are increasing; universities that proactively define responsible AI practices and align them with curricular outcomes will be better positioned to defend academic standards and student learning in an AI‑enabled world.