AI in university courses has raised an awkward question that cannot be settled by banning chatbots or teaching everybody to write prompts: what is a degree supposed to prove when software can perform some of the tasks students once had to complete alone? Stanford University is putting money behind a departmental answer. On 3 November 2026 it announced a $10 million programme to help academic departments redesign undergraduate majors, including courses, assessments and the skills students should demonstrate before graduation.
The most revealing feature is that Stanford is not asking every department to embrace AI unconditionally. Its announcement expressly leaves room for teaching and assessment that excludes AI, including in-class activities and oral examinations. That makes the programme more interesting than a new university chatbot. It asks how a university can tell the difference between a student’s understanding and work performed on the student’s behalf.
Why AI in university courses now affects the entire degree
Universities have spent years adjusting individual assignments. One lecturer might permit the use of an assistant for research planning while another bans it for essays. Such rules can make sense locally, but they may leave students moving between contradictory expectations. A department-level redesign tries to look at the complete journey from introductory classes to the final project.
Stanford says the initiative will be administered through its undergraduate education leadership alongside the Stanford Accelerator for Learning and related teaching programmes. Departments will be able to apply from the winter quarter of 2027 for grants of up to $750,000, designed to support work over one or two years. The larger $10 million sum is the total fund, not an award promised to each student or an immediate change to every course.
The university expects to work with approximately 15 departments through a process running into the 2028-29 academic period. Those are plans and expectations stated by Stanford, not results already achieved. Applications and actual curriculum changes still lie ahead. Students should not read the announcement as proof that the institution has completed a new model of education.
The scope is significant because a major connects skills in a sequence. An early computing course may teach principles that students apply in later software projects. A history course may develop close reading and source evaluation before students undertake independent research. If AI changes what is easy to produce, educators need to decide which underlying capabilities still deserve dedicated practice.
The questions teachers can no longer avoid
One question is whether producing the answer is still a good way to demonstrate the skill. A polished essay could reflect careful thinking, an excellent AI rewrite or a mixture of both. A working software project might show genuine engineering ability, or it might conceal that the student cannot explain the choices made by the program. The finished object alone may reveal less about the process than it once did.
Another question concerns what students must be able to do when tools fail. A graduate using an AI system may need enough knowledge to spot a mistaken calculation, challenge a fabricated citation or reject unsafe advice. Understanding the principles of a discipline is not made obsolete merely because software can generate plausible answers. In some fields, the ability to detect an error may become even more important as routine production gets easier.
Stanford’s announcement recognises that disciplines will need different answers. A creative writing course may use AI as material for critique, while a mathematics course might insist that certain proofs be completed without assistance. The right assessment is likely to depend on what the course promises to teach. A university-wide slogan would be a poor substitute for that judgement.
There is also a danger in treating tool familiarity as equivalent to education. Being able to request a summary is useful. Being able to decide whether the summary accurately represents its source is a different achievement. That difference matters when the material concerns medicine, engineering, law, public policy or other work where an apparently minor error could carry serious consequences.
What a redesigned assessment could look like
Stanford explicitly mentions options such as oral examinations and in-class learning where AI may be excluded. An oral assessment can reveal whether a student can explain why an answer is correct, respond to a follow-up and defend a judgement under questioning. It is not automatically fairer or easier to administer, and it must be designed to accommodate different student needs. Its value is that it can assess understanding rather than only the appearance of the final text.
Project work could also change. A department might allow AI during an early research phase and then require students to document how they checked sources, revised the result and handled errors. This is a possible assessment design, not a Stanford policy already announced. It illustrates how permission to use a tool can coexist with a serious requirement to demonstrate independent reasoning.
Some tasks may need to remain deliberately unaided. People who rely on software to do every introductory exercise could reach advanced courses without the mental foundations needed to notice when the software is wrong. Other tasks may become more ambitious because the same tools remove repetitive work. A well-designed programme will have to weigh both possibilities rather than treating every computer-generated sentence as cheating or progress.
UNESCO’s guidance on generative AI in education and research supports a human-centred approach to these decisions. It is guidance rather than a legal requirement for Stanford. Its relevance lies in the principle that educational goals, human capacity and institutional responsibility should determine how AI is used, not simply the availability of a new product.
Why the experiment matters outside Stanford
The programme is based at a wealthy American university, so its funding model cannot simply be copied by schools with smaller budgets. Yet the problem it addresses is not exclusive to elite campuses. Employers still need to know what a qualification tells them. Students need confidence that their work develops skills with a life beyond the next software release. Teachers need assessment methods that do not reward students merely for obtaining the most fluent generated text.
LiveAIWire has previously reported on students looking for clearer guidance about university AI use. A programme organised across a complete degree could address that uncertainty more coherently than disconnected assignment rules. Whether it succeeds will depend on the details departments choose and whether those details remain workable once staff and students have to use them.
The issue also connects with differences in university AI policies across disciplines. Rules are more credible when they are tied to a specific learning objective. A blanket ban may be difficult to explain in a course about using AI systems responsibly; compulsory AI use may be equally hard to justify when the learning goal is foundational understanding.
A third question is how students actually learn from assistance. LiveAIWire covered research distinguishing better finished work from deeper independent thinking. That distinction lies near the heart of Stanford’s initiative. The grades attached to a polished output may tell an institution less than a student’s capacity to reason without being led to a convenient answer.
There are practical costs to redesigning a curriculum. Staff need time to agree outcomes, create fresh assessments, explain rules and evaluate whether students have learned more. Departments must also consider accessibility, inconsistent access to paid tools and the risk that an assessment rewards confidence in public speaking more than the knowledge being tested. Money may enable experimentation, but it does not remove those decisions.
One important measure of success will be whether students can explain what assistance was permitted and why. Another will be whether employers and postgraduate courses can still trust the capabilities the degree claims to represent. Those outcomes cannot be read from a funding announcement. They will require evidence after programmes are redesigned and students have gone through them.
Students themselves also have a stake in clarity. If rules are contradictory, cautious students may avoid useful permitted tools while others take risks they do not understand. An agreed sequence across a degree could make expectations easier to explain at each stage. Departments would still need to communicate changes, provide support for students with different needs and decide how to handle disputes fairly. Good curriculum design involves those ordinary procedures as well as the bigger question of what knowledge graduates must possess.
Stanford’s move is therefore a useful signal, not a completed solution. Rather than adding another AI option to a course website, the university is asking departments to reconsider the purpose of entire majors. The strongest answer may preserve activities that have to be done independently while teaching students when assistance is appropriate. A meaningful degree should show that a graduate can use powerful tools and still think when those tools are taken away.
About the Author
Stuart Kerr is Technology Correspondent at LiveAIWire, covering artificial intelligence, cybersecurity and the social impact of emerging technology. LiveAIWire is an independent, human-led technology publication using AI-assisted research, editorial production and original AI-assisted editorial illustrations under his direction.