Faculty in an AI University
AI can generate practice sets and surface patterns in student data, but it cannot decide what matters, navigate a discouraged classroom, or guide learners into the heart of a discipline. In post four of our series, we examine how the faculty role is expanding—from content delivery to intentional mentorship, complex problem design, and contextual judgment.
The New Curriculum: What Should Every Graduate Know?
A new curriculum won’t be defined by a checklist of fashionable skills. It requires deliberate choices about what graduates should know, how they connect ideas across fields, and the responsibilities that come with that knowledge. As technology evolves, here is how higher education can build a foundation that endures.
What Should Assessment Become?
As AI makes it easier to generate polished assignments, a finished product no longer reveals what a student truly understands. To measure meaningful capability, assessment must shift from grading final outputs to evaluating how students reason, adapt, and make critical decisions along the way.
From Teaching Content to Cultivating Capability
When AI can explain, summarize, and suggest solutions on demand, what should colleges help students learn? Knowledge remains essential, but access to information is only a beginning. Higher education must also help students evaluate evidence, practice decisions, and apply what they know with care. This post explores a shift from teaching content alone toward cultivating capability.
If AI Can Do Almost Everything, What Should Education Refuse to Outsource?
AI can take on more and more of the visible work: drafting, assessing, giving feedback, and adapting activities. But preparing someone to complete tasks efficiently does not by itself prepare them to make sound judgments about which tasks matter or how their decisions affect others. What education should refuse to outsource is not a fixed list of assignments—it is the responsibility for helping students develop judgment, agency, and the capacity to act thoughtfully in a changing world.
If AI Becomes Everyone’s Cognitive Partner, What Is the Purpose of Learning?
If AI becomes everyone’s cognitive partner, what is the purpose of learning? One answer is that learning helps people work well with tools. Students will need to know how to ask useful questions, evaluate AI responses, and combine information from different sources. Those abilities matter. But if learning stops there, students may become skilled at using AI without developing the knowledge and judgment they need to decide whether its help is appropriate.
If AI Can Complete Coursework, What Is the Purpose of Effort?
Effort matters when it helps students become more capable.
AI can help students finish coursework faster, explain difficult ideas, or supply instant answers. But when a system does the thinking an assignment was designed to cultivate, completing the task no longer means developing the capability. Learning requires what cognitive scientists call desirable difficulties—retrieving ideas from memory, trying solutions, spotting mistakes, and revising an approach.
The real question isn’t whether students do work the hard way, but whether they have the chance to practice the reasoning that builds lasting understanding.
If AI Can Answer Every Question, What Becomes of Curiosity?
When AI can answer questions in seconds, curiosity still matters. Students need opportunities to notice what they don’t understand, follow their questions, and use AI to explore rather than end the inquiry.
If AI Can Personalize Learning, What Is the Value of a Classroom?
Personalized AI support can help students work at their own pace, but learning also happens through conversation, disagreement, and shared problem-solving. The value of a classroom lies in how students learn with and from one another.
If AI Can Provide Feedback, Why Does Human Feedback Still Matter?
AI can polish a sentence in seconds, but good feedback is never just about clean phrasing. While automated tools offer immediate, on-demand corrections, they often miss the deeper purpose of learning: helping students understand their own ideas, navigate misconceptions, and develop critical judgment. AI may show a student how to revise, but human mentorship connects that revision to why it matters and where they need to go next.
If AI Can Complete Assessments, What Is Assessment For?
A student turns in a flawless essay—clear argument, strong organization, credible sources. But how much reflects genuine comprehension, and how much was shaped by an algorithm? If AI can complete the tasks we traditionally grade, the goal isn't to police the tools, but to shift our focus from the final product to the process: the choices students make, the reasoning they defend, and the judgment they bring to their own work.
If AI Can Teach, What Becomes the Role of Faculty?
A student uses AI to get unstuck on a difficult reading. That can help—but a clear explanation alone doesn’t guarantee learning. As AI takes on more instructional tasks, faculty’s judgment, course design, and support for students’ deeper understanding matter even more.
Post 8: What Kind of Professional Do You Want to Become?
As AI takes on more of the visible work associated with a profession, what helps a person remain connected to the work they do? This post explores how students can build a professional identity grounded in expertise, judgment, agency, and responsibility—not just in the products they create.
Preparing to Work Alongside AI Agents
As AI agents move from generating answers to taking action, students will need more than prompt-writing skills. They’ll need to learn how to delegate thoughtfully: setting boundaries, limiting access, monitoring decisions, and keeping consequential choices under meaningful human control.
Knowing When Human Oversight Is Necessary
“Keep a human in the loop” sounds reassuring, but the presence of a person does not guarantee meaningful oversight. A reviewer can investigate an AI recommendation, compare it with other evidence, and stop a harmful outcome—or simply glance at it and click approve. Both workflows include a human. Only one preserves human judgment.
As AI moves from generating information to taking actions across workplace systems, we need to ask a more useful question: What must the human be able to see, understand, decide, and stop?
Working With AI Without Losing Professional Expertise
AI can improve a novice’s performance without building the expertise needed to work, evaluate, and recover independently. As AI takes on more entry-level tasks, educators and employers need to ask which capabilities people must retain—and how learning experiences can help them develop those capabilities before efficiency becomes the only goal.
Protecting Data in an AI-Enabled Workplace
AI tools can be genuinely useful when we provide context—but more context can also mean greater exposure. A customer note, student email, résumé, troubleshooting log, or internal document may contain information entrusted to us by someone else. The professional question is not simply, “Is this private?” It is, “Am I authorized to use this information in this way?” Before sharing anything with AI, we need to pause, minimize what we provide, and consider whose information is at stake.
AI Can Recommend. Humans Still Have to Answer
AI can generate recommendations, identify risks, and help professionals work through complex information. But it cannot carry the consequences of the decisions that follow. As AI becomes part of workplace decision-making, students need more than technical skill: they need the judgment, authority, and professional courage to question a recommendation, recognize its limits, and take responsibility for how it is used.
Making AI Use Visible in Professional Work
As AI becomes part of everyday professional work, simply saying that it was used is no longer enough. Responsible practice requires documenting what AI contributed, how its output was evaluated, what decisions remained human, and who accepts responsibility for the final result.
AI Is Changing Tasks Before It Changes Jobs
AI is changing work, but not simply by replacing entire jobs. More often, it is reshaping the individual tasks, decisions, and responsibilities within them. Preparing students for this future means helping them understand what AI can support, what still requires human judgment, and where accountability remains.