Universities are making a consequential concession: allowing privately owned generative AI systems into the very processes through which students learn to read, write, reason, and exercise judgment. Their adoption is increasingly presented as inevitable. AI exists, so higher education must adapt. Under this logic, adoption is expected, while refusal requires an explanation. But technological availability is not educational justification. Before reorganizing teaching and learning around these systems, universities should answer the question that advocates of adoption too often avoid: Why should AI be there at all?

The language of inevitability obscures who is responsible. A recent Forbes headline declared, “MIT Says AI Is Forcing a Rethink of College Itself.” AI is not forcing universities to purchase licenses, revise courses, shift budgets, or surrender influence over educational infrastructure to private vendors. People make those decisions. They should have to explain and defend them. Who was consulted? What alternatives were considered? And whose interests are being served? California State University signed a $16.9 million OpenAI agreement in 2025. A statewide student leader said her association wasn't consulted; officials cited an advisory committee with student and faculty members.

A university exists to cultivate independent thought, critical judgment, curiosity, and the ability to work through difficult questions. Reading closely, developing an argument, recognizing errors, and revising one's thinking are not incidental steps on the way to an answer. They are part of what education is for. What happens when students are encouraged to rely on systems that can perform much of this work for them?

Public universities owe Big Tech no duty of adaptation. Their obligation is to students, workers, and the public.

The available research offers no warrant for treating adoption as an educational obligation. A 2026 meta-analysis of 35 studies reported moderate gains across varied educational settings. A review of 67 higher-education studies found better outcomes when AI was used within carefully structured, inquiry-based teaching. Those findings deserve consideration, but they do not settle whether AI belongs in the learning process. In the latter review, stronger results depended on deliberate human instruction, and neither review establishes that AI reliably develops lasting, independent learning or provides benefits that effective teaching without it cannot achieve.

Better assignments are not necessarily evidence of better learning. A polished paper produced with ChatGPT does not show that its author has become a better writer. Short-term performance measures tell us little about what students may be losing when they outsource reading, writing, and reasoning. Assisted performance is not intellectual development, and making learning easier does not necessarily make education better. Nor can learning-outcome studies settle the questions that institutions routinely sidestep: Who should own educational infrastructure, what student work may be collected, whose labor may be automated, and whether students and faculty can refuse participation. Those are ethical and political decisions, not matters of test scores alone.

Yet narrowly demonstrated benefits are being used to support sweeping institutional ambitions. Universities must consider training, faculty workload, verification, privacy, dependence on proprietary systems, and what happens when a vendor changes its prices or rules. Once courses depend on a platform, leaving can become disruptive and expensive. Public money finances the infrastructure, faculty labor makes it part of everyday teaching, and the resulting dependence can give vendors greater leverage over academic decisions. Public institutions........

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