American college students are turning against AI. According to Gallup, excitement is down 14% amongst Gen Z Americans in a year. Anger is up 9%. Princeton has just ended a 133-year honour code over AI cheating. Stanford literature graduates are refusing to use ChatGPT on principle. University of South Carolina students voted nine-to-one against an OpenAI partnership their administration signed without consulting them. The frustrations are real. The data backs them. But refusal as a strategy will lose. The students are right about what they see and wrong about what to do about it.
In this post I will:
Lay out what recent events actually show about Gen Z college student AI sentiment in 2026.
Name what the students are getting right (peer-reviewed research backs more of it than the industry is admitting) and where the refusal strategy breaks down.
Set out three concrete moves for educators, three for students, and three for the parents and carers watching them work through this, in the paid section.
I have waited to comment on this because I wanted to read the actual research before adding to the noise. I am writing from a slightly unusual position. I run a newsletter and a curriculum that argues for slowing AI down. I am openly critical of how it is being deployed in schools, universities and workplaces. Last month I published HEPI Policy Note 71, a study of what the AI policies of 96 UK universities actually do when you read them closely. Forty-one per cent of UK universities had no publicly discoverable AI policy at all. The ones that did wrote in the language of student support and operated, structurally, as compliance instruments. They promised critical thinking and delivered audit trails. They named support and delivered surveillance. The pattern is not unique to the UK.
So when I say the current college student rebellion against AI is the wrong strategy, I am not saying it from the cheering section of the AI industry. I am saying it from the place where, most weeks, I am the one calling out the rollouts.
What Gen Z is actually saying about AI in 2026
In April 2026, the Walton Family Foundation, GSV Ventures and Gallup jointly surveyed Americans aged 14 to 29. Strong agreement with “I feel excited about AI” dropped 14 percentage points in a single year, down to 22%. Anger rose 9 points to 31%. Hopefulness fell 9 points to 18%. Weekly use held roughly steady at 51% of the cohort, but the rate of new adoption stalled.
The Walton Family Foundation framed the shift in plain language. Gen Z resentment toward AI is growing as adoption stagnates and workplace fears mount. Fewer than half of the cohort (46%) agreed AI tools help them learn faster. Last year that figure was 53%.
A separate survey of Gen Z workers found 44% admitted to some form of sabotage of their employers’ AI rollouts. Refusing approved tools. Using unapproved ones. Generating deliberately low-quality output. The stated reason was fear of being displaced by the thing they were being asked to adopt.
This is not a niche minority. This is the demographic the entire AI industry was relying on as its early-adopter base. They were not supposed to be the resistance.
The named campus picture
On 28 May 2026, The Harvard Crimson reported that Harvard faculty are quietly giving up on detecting AI use and moving instruction back into the classroom. One professor tried to catch cheating by embedding hidden white text in an assignment that instructed any AI to insert a giveaway phrase. Students screenshotted the prompt, posted it to the anonymous campus app Sidechat, and got over 320 upvotes inside hours. Mary D. Lewis, Director of Undergraduate Studies in History, said that:
My understanding from the people who oversee the Honor Council is that it’s virtually impossible to prove, and so I think faculty are hesitant, not because they don’t think it’s AI, but because they think it will be a waste of their time.
Harvard’s 2024 Undergraduate Association report found 90% of undergraduates use generative AI. The institutional response is to phase out ChatGPT Edu after June 2026 and roll out Anthropic’s Claude in its place. Jesse McCarthy, an English professor, brought back blue book exams and acknowledged the approach is unsustainable as a long-term answer.
At Stanford, a recent graduate, Rosana Maris Arias, has publicly described refusing to use AI throughout her undergraduate years, even when her professors allowed it. She studied creative writing. Her position was direct: why would she outsource the very thing she came to learn?
At the University of South Carolina, undergraduate Brooklyn Tyner set up a booth at the university’s ‘AI Day’ event, during the rollout of its OpenAI partnership signed the previous summer. She surveyed students who stopped to vote on whether they approved of the deal. Among those who stopped, nine out of ten said they did not. Her label for ChatGPT is “a cheating machine.” Her university’s label for the same software is a strategic partnership.
In March 2026, Inside Higher Ed reported that the Conference on College Composition and Communication (CCCC), the largest professional body of writing educators in the US, is formally pushing back on the assumption that generative AI in the classroom is inevitable. Writing faculty are now actively organising for the right to refuse it in their teaching.
On 19 May 2026 Bloomberg reported on AI pushback, protests, and audible booing at US graduation ceremonies when AI partnerships were named in speeches.
And on 15 May 2026, Princeton faculty voted to require proctoring of all in-person exams, ending a 133-year-old honour code precedent. The change takes effect 1 July 2026. The trigger was AI. The story underneath the story is that one of the oldest formal expressions of academic trust in the United States has just been retired because the institution no longer trusts the students to police themselves.
What the students are getting right
I want to name this clearly because the industry response keeps missing it.
The students are right that the cognitive cost is real. Peer-reviewed work by Michael Gerlich, published in 2025 in Societies and replicated in adjacent samples across 2026, identified a clear negative correlation between frequent AI tool use and critical-thinking scores, mediated by cognitive offloading. Younger participants in his data showed higher dependence and lower critical-thinking scores than older ones. The literature is mixed, and the direction has shown up in several studies now. The data is consistent enough to take seriously.
The students are right that the labour market signal is dishonest. Sam Altman and Dario Amodei spent two years publicly warning of mass white-collar displacement. In late May 2026, both publicly softened the prediction within days of each other, with Altman telling the Commonwealth Bank of Australia conference he is “delighted to be wrong” about the jobs apocalypse. Both companies are preparing for major public offerings. Read the timing as you wish. Meanwhile the Oliver Wyman Forum / NYSE CEO Survey 2026, found 43% of CEOs plan to cut junior roles in the next two years, up from 17% a year earlier. The young workers being told to embrace AI are being told this by the same people preparing to remove their entry-level jobs.
The students are right that the consent question matters. The University of South Carolina did not poll its students before signing the OpenAI deal. Harvard did not consult its 90% of GenAI-using undergraduates before swapping one model for another. My HEPI study found that very few of the 96 UK universities surveyed could evidence any meaningful student involvement in shaping their AI policies. AI adoption in higher education is being negotiated by procurement, not pedagogy. The students were not in the room. The policies that emerged were written about them and without them.
And the students are right about something the industry actively resists. There are cognitive tasks where the right pedagogical answer is to opt out on grounds of skill formation. The creative writing student who refuses AI for the original draft is making a pedagogical case for skill formation. The undergraduate philosophy essay where the student wrestles a position into shape by writing it badly first is not improved by an AI that smooths the bad first draft into a competent second one. The argument that AI literacy is the new literacy and everyone has to engage misses the real point that some skills are built by going through the difficulty, not around it. The industry concedes the cognitive cost. It does not concede the right of the student to refuse the tool for a specific task on the grounds that the difficulty is the point. The students are right to insist on that ground.
The frustrations are reasonable. The data backs them. They are not making this up.
Where the refusal strategy breaks down
This is where my read parts company with the rebellion.
Abstention is not the same as critical engagement. The student who refuses to touch the tool gives the tool to the student who uses it uncritically. The faculty member who refuses to use AI in their classroom gives the curriculum to the faculty member who delegates the syllabus to it. The campus that boycotts AI gives the procurement decision to the campus that signs without consulting anybody.
The closest historical parallel is the teachers who refused to integrate the internet into their classrooms in the late 1990s. They did not stop the internet. They handed the framing of what good and bad internet use looked like to the students themselves and to the search companies. The library profession, working through the same cultural moment a few years later, eventually arrived at a more interesting move with Wikipedia: by the mid-2000s most academic libraries had engaged with it critically, building source-evaluation curricula around it rather than boycotting it. That second move was the right one. The institutions that refused to engage had less influence on how the next decade of information literacy was taught, not more.
The lesson is that refusal without an engagement strategy alongside it tends to lose. The Stanford literature graduate who refused AI for her creative writing has a defensible pedagogical reason. The blanket campus boycott that refuses procurement reform along with the tool has given up its hold on the deal.
Refusal looks like resistance. It functions as withdrawal. The decisions about how AI lands in education, work, and civic life are being made right now. They are being made by the people who showed up.
The cognitive offloading research also has a second half the abstention read keeps skipping. Other research has found that AI literacy and information literacy moderated the negative relationship between AI dependence and critical thinking. In other words, the students taught to use AI critically were not the ones losing the cognitive ground. The students who abstain entirely are not losing it either. They are also not learning the skill the rest of the workforce is being measured against.
The trust problem nobody wants to name
Trust is the missing variable. The HEPI study I published last month identified it as one of four foundational principles for AI policy in higher education, alongside student-centredness, relevance, and agency. The four principles come from the book on AI in higher education I co-authored with Rachel Forsyth. Of those four, trust is the one institutions concede in theory and surrender in practice.
The Princeton honour code did not end because students became less honourable. It ended because the institution lost confidence in its ability to verify them. The Harvard hidden-text trap got 320 Sidechat upvotes because students recognised, instantly, that the relationship had become adversarial. The UK universities that promise critical thinking in their AI policies and deliver audit trails are training students to expect the institution will act in bad faith, then asking them to act in good faith anyway. You cannot do both.
This is what Brooklyn Tyner is responding to at the University of South Carolina. It is what Rosana Maris Arias is responding to at Stanford. It is what the 44% of Gen Z workers actively sabotaging their employers’ AI rollouts are responding to. What they are rejecting is being treated as defendants in a case the institution decided to bring before it had any evidence.
Students and workers are not the problem. They are the people the technology is being done to. The educators, administrators, and managers who keep solving for detection are solving the wrong equation. The equation that gives a different answer is the one that starts with trust and works backwards from there.
The labour question cuts the same way. The Gen Z worker who refuses the workplace AI tool may protect their own dignity in the short term. They do not protect the entry-level job. The entry-level job is being eliminated for reasons that have nothing to do with whether they personally use the tool. Refusing to use it does not bring it back.
The version of the resistance I would respect is one that engages with the tool, builds the literacy, and uses that ground to demand consent, transparency, and procurement reform. The version that just refuses is the one I think gives up too much.
This is what the Curriculum for Critical AI Literacy is built to teach. It is a twelve-month programme, designed for the working professional and the educator who is being asked to make AI decisions without the framework to make them well. Monthly live sessions, peer-reviewed reading, structured exercises that change how you work with AI tools. It is also fully CPD accredited.
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What you can do next
Three concrete moves for educators. Three for students. Three for parents and carers. These are what I would do in each of those positions today, given the research and the campus picture above.
