If information is easier to access than ever, what should colleges help students learn?
A student in an introductory networking course can ask an AI assistant to explain how a router forwards data, compare two wireless standards, or suggest steps for troubleshooting a connection. The answer may be useful and it may arrive before the student has opened the textbook or tried the lab.
That changes the role of teaching. It does not make knowledge less important. Students still need to understand how networks function, what different components do, and how technical ideas connect. Without that foundation, they may not recognize when an AI explanation is incomplete or when a suggested fix could create a bigger problem.
But knowing information is only part of being capable. In a lab, students also need to notice what is happening, decide which evidence matters, test possible causes, and explain why they chose a particular solution. An AI tool can suggest steps. The student still needs to judge whether those steps fit the situation.
This distinction matters across disciplines. A business student may use AI to summarize customer feedback, then decide which patterns deserve attention. A nursing student may review an AI-generated explanation, then connect it to a patient’s circumstances and the limits of the available information. A writing student may use suggestions to revise a draft, while making decisions about purpose, meaning, and audience.
In each case, the goal is not to separate knowledge from practical work. Students need both: enough knowledge to understand a situation and enough practice to use that knowledge with care.
Colleges have often organized learning around the delivery of information. A course has content to cover; an instructor explains it; students study it and demonstrate what they remember. That model still has a place. Clear explanations and well-organized resources can help students build a foundation. But when information can be retrieved, summarized, and restated on demand, the value of a course cannot rest on access to information alone.
A student who can find an answer may not yet know how to use it. They may not be able to tell whether the answer applies to the problem in front of them, identify what is missing, or explain the consequences of acting on it. These capabilities develop through guided practice: trying, comparing, discussing, revising, and reflecting.
In the near future, this shift may appear in small changes to ordinary classes. Instead of asking students only to define a term, an instructor might provide a realistic scenario and ask them to choose among several possible actions. Students could consult AI for suggestions, then identify which advice they would follow, what they would verify, and what they would reject. The instructor can assess the student’s reasoning alongside the final choice.
In a computer hardware class, for instance, students might receive a troubleshooting scenario with several possible causes. An AI assistant can propose a diagnostic sequence. Students then decide what to test first, explain why, record what each result rules in or out, and adjust their next step when the evidence changes. They learn technical concepts while practicing how to use them.
These activities do not require every course to become a simulation or every assignment to include AI. They begin with a straightforward question: What should a student be able to do with the knowledge after the lesson is over?
That question may become even more important over the next decade. As AI tools become more capable and more closely connected to workplace systems, students may practice in environments that respond to their decisions. A business student could test how different choices affect a simulated organization. A health professions student might work through a changing case that reveals new information as they ask questions. A trades student could troubleshoot a virtual system before working on physical equipment.
These possibilities are speculative, and access will vary. A simulation cannot fully reproduce the complexity of a workplace, the needs of a patient, or the experience of collaborating with other people. Still, it points toward a possible change: courses could give students more chances to practice decisions, see consequences, and try again with guidance.
The instructor’s work in that setting would include deciding which experiences are worth creating, helping students connect actions to underlying concepts, and noticing where a student needs a different kind of support. The technology might supply responsive scenarios or explanations, but it would not decide what a learner ought to become capable of doing.
That responsibility belongs to educators and institutions. They must choose what knowledge matters, which abilities students should develop, and what evidence will show that learning has taken place. The choices will differ by field. A laboratory course, a writing course, and a workforce program will not define capability in the same way. Each will need to connect disciplinary knowledge to the decisions and actions students will face.
This also means capability should not become a substitute for knowledge. Students cannot evaluate what they do not understand, and they cannot make sound decisions from a foundation of guesses. Teaching content remains essential. The larger opportunity is to make the connection between content and use more visible: to help students move from learning about an idea to recognizing when and how it matters.
The question for higher education is not whether students should learn information. They should. It is whether access to information is enough. In an AI-rich world, colleges can help students build knowledge and develop the capacity to apply it, question it, and act responsibly with it.
That shift from teaching content alone toward cultivating capability raises another question: If a degree represents what a student can do, how should colleges assess and demonstrate that learning?
Continuing the Conversation
Series 1: AI Is Exposing Existing Problems ✓ Completed
Series 2: What We Do About It ✓ Completed
Series 3: Cultivating Human Thinking in an AI World ✓ Completed
Series 4: Learning Alongside AI ✓ Completed
Series 5: Preparing Students for an AI World ✓ Completed
Series 6: The Purpose of Education in an AI World
Series 7 — The Future of Higher Education
Current Post (1 of 8): From Teaching Content to Cultivating Capability
Next Up: What Should Assessment Become?