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Universities are being asked to graduate students who can reason about AI beyond the boundary of computer science. The ask arrives at a moment when the surrounding conversation is pulling in the opposite direction: companies are reducing junior hiring and betting on agents to route around the cost of building human judgment. Against that backdrop, the dominant pedagogical responses embody an unexamined assumption: that breadth and depth trade off, and that technical rigor requires prerequisites most undergraduates lack. This Perspective argues that the assumption is inherited rather than inherent, and that its institutionalization risks sorting a generation into those who can use AI tools and those who can reason about AI systems, at the moment when those systems are shaping healthcare, hiring, sentencing, and democratic discourse. Drawing on UNIV 182, a cross-disciplinary AI literacy course the author designed and taught at George Mason University, this Perspective offers an existence proof that the trade-off dissolves under deliberate design, and an argument for why universities, at this moment, carry distinct responsibility for that charge.
Amarda Shehu (Wed,) studied this question.