Earlier this year, I argued that students were becoming AI fluent while many universities remained focused on AI literacy. Cautious institutions were taking little baby steps while students were making giant leaps of faith.
The intervening months have reinforced that concern and revealed something more profound. The issue is no longer whether students are using AI but whether AI has reshaped how students think, learn, and engage with the real world around them.
This fall, North American colleges and universities will welcome a new freshman class. On the surface, they will look much like every cohort before them. They will arrive carrying laptops, smartphones, anxieties about the future, and the hopes of families investing heavily in their success.
Yet many of these students are fundamentally different from those who came before them. They are the first generation to complete much of their secondary education with generative AI constantly available.
They have never known a world where a question could not be answered instantly, an essay could not be outlined in seconds, or a digital companion was unavailable to explain, summarize, brainstorm, or critique and advise on almost anything.
Asking the wrong question?
Last fall, while teaching an AI-themed first-year seminar, I was struck less by my new students’ AI proficiency than by their assumptions. They were not debating whether AI belonged in their future. They simply assumed it did.
Like Wi-Fi, smartphones, or social media, AI had become part of their environment. Their questions were no longer whether to use AI, but how, when, and why.
So, is higher education now asking the wrong question? While we continue debating how AI will change education, AI has already supercharged incoming students’ expectations.
Paying attention to that intellectual shift demands a new set of ABCs: abundance, belief, and choice.
Abundance
Universities were built for a world defined by scarcity: scarce information, scarce expertise, and scarce access. Knowledge was difficult to acquire, expensive to distribute, and concentrated among experts. AI is rapidly eroding that equation.
For the first time in human history, students have immediate access to something approaching limitless information. A student can ask an AI system to explain quantum mechanics, summarize a court decision, compare economic theories, generate practice questions, or curate a study guide in seconds.
It democratizes access to knowledge in ways previous generations could scarcely imagine. Yet abundance changes behavior. When something is scarce, people value it. When it becomes abundant, they often take it for granted.
That creates an uncomfortable tension for higher education. If information is no longer scarce, institutions will have to derive more of their value from helping students develop judgment, discernment, and wisdom rather than simply transmitting information.
Belief
If information from a myriad of sources is instantly available everywhere, how do we decide what sources deserve our trust? This may be the defining educational challenge of the AI era.
AI can generate impressive answers at extraordinary speed. It can also generate errors, distortions, biases, fabrications, and highly persuasive nonsense. The same systems capable of accelerating learning can also create confidence without understanding and certainty without truth.
In a world of informational abundance, trust itself becomes scarce. That reality changes the role of education.
The student who can find an answer is no longer exceptional. The student who can evaluate an answer, challenge it, verify it, and determine whether it deserves trust increasingly is.
We must teach students to challenge AI outputs, recognize bias, verify claims, compare competing models, and distinguish confidence from credibility. These skills are hardly new.
What is new is the urgency of teaching them in real time while students collaborate daily with emerging AI systems that increasingly influence how they think, learn and solve problems.
Choice
If abundance changes information dynamics and belief changes trust, choice changes everything else, especially as students have more choices about educational pathways than ever before.
As AI lowers the cost of knowledge, it raises the value of judgment. Students operate in environments where multiple answers appear plausible, recommendations arrive instantly, and machines perform tasks once considered evidence of expertise.
As a result, they will expect universities to accept AI collaboration as the norm rather than the exception.
In that world, the goal of education can no longer be knowledge acquisition. It must be the ability to make thoughtful choices amid the uncertainty of machine- versus human-generated knowledge.
The ability to weigh evidence, navigate ambiguity, understand consequences, balance competing interests, and making decisions aligned with human values and ethics are all critical to learn.
Ironically, as AI becomes capable of more dazzling feats of reasoning, the qualities that matter most are increasingly those that make us human rather than machine: wisdom, discernment, empathy, and ethical judgment. These are not technological skills but enduring human capabilities we must preserve.
AI-native class is not waiting
The reality of a co-intelligent presence has implications beyond the classroom. But boards, presidents, provosts, deans, and faculty must now rethink curriculum, assessment, institutional value, and learning outcomes.
The central challenge is no longer simply adopting AI institutionally but preparing students whose assumptions about knowledge, trust, and decision-making have already been shaped by it.
Those will be defining conversations. Yet they often remain stuck on technology adoption costs, model selection, and governance mechanics rather than vital questions about how AI is transforming student cognition and social norms in this emerging co-intelligent era.
Incoming students have already developed different assumptions about information, trust, and decision-making. To avoid an existential crisis, higher education must reexamine what educational value looks like in an era where information is abundant, trust is contested, and our ethical choices matter in the emerging competition between human and machine intelligence.
To thrive under these conditions, we cannot merely teach students how to use AI tools. We must steer students thoughtfully through questions of when AI should be trusted, when it should be challenged, and when and why human judgment must prevail.
The first AI-native class arrives this fall. They are not waiting for universities to decide whether AI belongs in higher education because, in their minds, that question has already been answered.
The real question is whether higher education is prepared for students whose relationship with knowledge, trust, and decision-making has already been shaped by AI.
Universities were built for an era of scarcity. The institutions that flourish in an age of abundance will be those that teach not simply how to use AI, but when to question it and why human judgment still matters most.
