As a chief information officer, I have the privilege of interacting with students in many different settings, and conversations about artificial intelligence have become increasingly common.
Recently, I read several student reflections explaining why they refuse to use generative AI in their coursework. Some described it as environmentally destructive, pointing to water and energy consumption that disproportionately harms vulnerable communities.
Others called it a form of cheating, no different than passing off someone else’s work as your own. Several argued that the entire point of education is the struggle itself, and that outsourcing that struggle to a language model defeats the purpose of being in school.
A few expressed something closer to moral revulsion, framing AI not as a tool but as a threat to creativity, originality, and human dignity.
These reactions are worth paying attention to, not because they are right or wrong, but because they reveal a deeper challenge for higher education: how we help students engage critically with powerful new tools without turning learning into either compliance or avoidance.
The problem is not that students are critical of AI. Skepticism is healthy. Ethical concern is essential. In fact, those instincts are exactly what we want students to develop.
The problem arises when critique hardens into refusal; when a student decides in advance that a tool, an idea, or an emerging reality is not worth examining, not worth engaging, not even worth understanding.
That posture is not principled resistance. It is intellectual closure.
Engagement does not mean endorsement
Too often, the debate around AI in education collapses into a false binary: use it uncritically or reject it entirely. But learning has never worked that way.
We do not teach students to accept technologies as neutral or benevolent. We teach them to interrogate them; to understand tradeoffs; to ask who benefits, who bears the cost, and under what conditions value is created or lost.
Refusing to engage short-circuits that process.
Engagement, however, does not mean endorsement, nor does it obligate use. The goal is not to compel students to adopt AI or to measure learning by proficiency. The aim is exposure in context and the development of critical literacy.
Students should have opportunities to examine how these systems work, where they fail, how bias and error emerge, and when their use may be inappropriate or unethical.
That might look like students prompting a language model with a question from their discipline and auditing the output for errors and hallucination or comparing an AI-generated response to their own work and articulating what the model missed.
It might mean calculating the actual energy cost of a query and weighing it against the environmental cost of tools they already use without hesitation.
These are not exercises in adoption. They are exercises in judgment.
If, after serious engagement, a student decides that AI is not aligned with their values, learning goals, or creative process, that decision should be respected. What higher education should not accept is refusal without examination.
There is also a misconception embedded in many objections to AI: that using it necessarily means outsourcing thinking, or that learning only happens when struggle is unaided and solitary. History suggests otherwise.
Calculators did not eliminate mathematical reasoning. Search engines did not end research. Spellcheck did not destroy writing.
Tools may change the shape of work, but they don’t eliminate the need for discernment. What matters is whether students retain agency over how and why a tool is used or not used at all.
Turning resistance into reasoning
Some may be tempted to dismiss student resistance to AI as uninformed or reactionary. That would be a mistake.
These responses often reflect genuine anxiety about power, labor, creativity, and harm. Higher education should take those concerns seriously.
But taking them seriously does not require endorsing absolutism. It requires creating space for analysis, dialogue, and ethical reasoning.
This is where moral absolutism becomes an educational issue, not just a cultural one. When complex systems are framed as wholly good or wholly bad, learning narrows.
Absolutism replaces inquiry with certainty and discourages students from wrestling with ambiguity, tradeoffs, and incomplete information. Universities exist precisely to help students hold strong moral commitments while still engaging complexity rather than opting out of it.
We already live with imperfect technologies that carry environmental, social, and cognitive costs, and we rarely hesitate to use them.
We binge streaming media that consumes massive energy without a second thought. We drive cars that reshape cities and climates because the alternative is inconvenience.
We should not ignore these harms, but instead negotiate them as we critique, regulate, and adapt. AI should be no different. Holding it to a standard of purity we apply to nothing else does not advance ethics; it avoids the harder work of discernment.
This is the role higher education must claim. Not to mandate adoption or silence dissent, but to teach students how to engage thoughtfully with consequential tools; not to tell them what to think, but to ensure their conclusions are informed rather than reflexive.
As a CIO, I sit at the intersection of infrastructure and instruction. I see the systems being built, the decisions being made, and the speed at which AI is reshaping how institutions operate, how employers hire, and how knowledge is produced.
The students who wrote those reflections will graduate into that world whether they engage with it now or not. Their skepticism is an asset. Their moral seriousness is rare and valuable.
But conviction without examination leaves them less prepared, not more. Higher education owes them the opportunity to turn resistance into reasoning, and the respect to let them decide what comes after.
