Part two of seven in the Artificial Intelligence in Education series, beginning with AI Did Not Break Education. It Exposed It. Source cutoff: Oct 30, 2026.
When I have taught outside a university, the hardest part was rarely finding information. The work was deciding where to begin, connecting new material to what students already knew, asking questions that made them think, and leaving enough in their hands for the learning to become theirs.
The same was true when I discussed business, investing, and service with younger people. Teaching was attention, translation, challenge, and trust, not simply transmission.
Artificial intelligence can explain a concept several ways, generate practice, and answer follow-up questions at very low cost. That is an educational opportunity. It also directly challenges any university whose value depends mainly on controlling access to lectures, explanations, or credentials.
The question is what remains worth paying for when information is abundant and competent assistance is always available.
The short answer
The university’s strongest case is no longer information delivery. It is the combination of durable knowledge, sustained attention, demanding peers, mentorship, direct experience, and accountable judgment.
These benefits are expensive because they depend on time, relationships, facilities, and skilled human attention. A university that claims them as its value must make them central, not optional enrichment around a scalable delivery system.
A degree can still be economically useful, but credential value, learning value, network value, formation value, and access to people or facilities are different benefits. Institutions should say which ones they provide and show that students actually receive them.
The university is a bundle
Arguments about college often collapse several questions into one. A graduate may value the credential that opens a hiring process, the knowledge learned in courses, the network formed through classmates and alumni, the maturity developed through responsibility and disagreement, or access to laboratories, archives, studios, clinics, and research.
These benefits reinforce one another, but they can also fail on their own. A student may acquire a valuable credential without learning as much as the credential implies. Another may learn a great deal but receive a weak labor-market signal. A strong network may create opportunity without establishing competence. An inspiring residential experience may still be too expensive or unevenly delivered.
Artificial intelligence makes the separation urgent. If explanation and routine assistance become inexpensive, a university must show where organized human attention, shared practice, physical resources, and responsibility add something a learner is unlikely to assemble alone.
The bundle can matter precisely because its parts work together. Schedule, peers, advising, identity, mentoring, and credential incentives may help a student persist. Unbundling can expose waste, but it can also reproduce one output cheaply while weakening completion, relationships, or formation.
The expensive human goods are educational goods
Mentorship, belonging, disagreement, and community are sometimes treated as the soft side of college. They are part of the education. A mentor can notice a pattern a student cannot yet see, adjust the challenge, model standards, and connect one decision to a longer life. Peers expose assumptions and demand clearer reasoning.
The 2018 Strada-Gallup alumni survey found that only 25 percent of graduates strongly agreed that they had a mentor who encouraged their goals and dreams.[1] The institutional lesson is simpler: mentoring is an underdelivered promise. A relationship in a mission statement has no value for the student who never experiences it.
Human formation also has a less comfortable side. A university should bring students into contact with people who disagree, require them to defend claims, and give them responsibilities that affect others. This can happen in a seminar, residence hall, laboratory team, student organization, or service project. The repeated practice of listening, revising, deciding, and accepting consequences is the point.
Artificial intelligence can simulate disagreement and offer feedback. It cannot create mutual obligation. A classmate, teacher, client, or patient has interests of their own and will still be present tomorrow.
Some learning requires contact with the world
A fluent explanation of a laboratory procedure is not a laboratory. A simulated patient is not always an adequate substitute for a frightened person. A generated design does not teach how materials fail, how a client changes the brief, or how a team recovers when a plan meets reality.
Simulation matters. It lowers cost, permits repetition, exposes students to rare cases, and allows failure where real failure would be dangerous. Its best use is often preparation for experience, not replacement of the experience that contains the educational point.
Laboratories, studios, clinics, archives, fieldwork, and collaborative projects force students to encounter resistance. The instrument drifts. The source is incomplete. The patient’s priorities differ from the textbook. The teammate misunderstands. Those are often the moments when judgment begins.
Universities do not own direct experience. Employers, apprenticeships, community organizations, museums, and independent projects may provide it more authentically. The university’s advantage is the ability to connect experience with disciplinary knowledge, supervision, reflection, and increasing responsibility.
Work matters, but it is not everything
The post-college world has changed. Graduates will enter organizations where artificial intelligence participates in research, analysis, communication, design, software development, customer service, and management. Colleges that ignore this environment leave students to discover new working conditions on their own.
Work preparation is therefore a real obligation. Students should learn to use powerful tools, verify their output, explain their decisions, collaborate across expertise, and remain responsible when a tool contributes to the result.
Employment cannot be the only purpose. A university also prepares people to interpret evidence, participate in civic life, make ethical choices, understand histories they did not live, and keep learning when a current job or tool disappears. A curriculum tied only to today’s job descriptions may become obsolete faster than one built around durable knowledge and adaptable judgment.
A credential is useful, but it is not the education
A degree helps employers make decisions without investigating every course, project, and applicant. Tyler Cowen describes the credential as a low-cost screening device when direct assessment is expensive.[2] That does not make the signal fraudulent. It makes it compressed.
Compression saves time and hides variation. Two graduates with the same degree may possess different capabilities, while two people with similar capabilities may not receive the same opportunity if only one has the recognized signal.
Artificial intelligence may make work samples, simulations, structured interviews, portfolios, and direct skill tests cheaper. Those alternatives can still be coached, gamed, biased, inaccessible, or expensive. The goal is not to replace one proxy with several fashionable ones. It is to reduce the distance between the credential and the capability it claims to represent.
Apply the substitution test
A student may combine community college, online study, employer training, independent projects, apprenticeship, professional certification, and artificial-intelligence tutoring. Another may need the structure, relationships, facilities, and protected time of a residential university.
The substitution test is function by function. Can an alternative develop durable knowledge, provide timely feedback and mentoring, create direct experience and increasing responsibility, support completion, and produce a credible signal without rebuilding the same cost and gatekeeping?
Price also requires care. Published tuition, cost of attendance, student net price, and institutional net tuition revenue are different numbers.[3][4] College Scorecard data add information about debt, repayment, and earnings, but observed earnings do not isolate what the institution caused and do not capture the whole value of education.[5]
Affordability, completion, debt, field, learning, relationships, and opportunity cost belong in the same decision. No single number settles the value of college, but complexity is not permission to hide the price.
The burden of proof runs both ways
Universities often describe their most expensive and least measurable practices as essential. Community, tradition, exploration, and mentorship can become noble language wrapped around weak teaching, administrative growth, or benefits available mainly to students who already know how to find them.
That objection is fair. A university should show where each promised benefit appears in the student’s experience, who receives it, what it costs, and what students can do because of it.
The same test applies to disruption. An online platform, employer academy, or artificial-intelligence tutor is not an adequate replacement just because it is scalable or inexpensive. Low cost is an advantage only if the result is worth having.
A value test for universities
Every institution should be able to answer four questions:
What knowledge, capability, relationship, experience, and judgment does the institution promise, and where is each delivered?
What evidence shows that students receive and can use those benefits?
What does the benefit cost in money, time, debt, and foregone alternatives?
What result would cause the institution to redesign, replace, or stop the practice?
The bargain universities must earn
I believe the university remains worth defending where it creates a disciplined community of learning that develops knowledge, judgment, relationships, experience, and responsibility over time. Age, selectivity, prestige, and authority to issue a credential are not enough.
Human attention and direct experience are costly. Good mentoring does not scale like a video or chatbot. If these are the university’s distinctive contributions, they should receive more money, time, and management attention.
The task is to strengthen the parts of the university that produce learning and formation while changing the parts built around information scarcity, opaque signals, and inherited routines.
Part three, The Goal Is Better-Chosen Knowledge, asks what must still live inside the learner. When explanations are always available, what knowledge must a person command?
Questions, corrections, or disagreements are welcome. You can reach me directly at dave@aworkingmodel.com.
Sources
1. Strada-Gallup, “2018 Alumni Survey: Mentoring College Students to Success.” Published 2018. National retrospective alumni survey; useful for reported mentoring prevalence and association, not a causal estimate of mentoring effects.
2. Tyler Cowen, “Against credentialism.” Marginal Revolution, Mar 18, 2022. Argument about education, signaling, and the cost of direct evaluation.
3. National Center for Education Statistics, “Price of Attending an Undergraduate Institution.” Updated May 2024. Official definitions for cost of attendance, grants, and net price.
4. National Association of College and University Business Officers, “Most Undergraduates Receive Grants at Private Colleges, NACUBO Research Shows.” Jun 1, 2026. Preliminary estimates from participating private nonprofit colleges; discount rate is not student net price or educational value.
5. U.S. Department of Education, “College Scorecard Field of Study Data Documentation.” Jun 2024. Methodology for program-level earnings, debt, and repayment measures; coverage and cohorts vary, and observed earnings are not a causal estimate of institutional contribution.