The university must not become a supply chain for AI

Is AI going to be the answer to everything?

That seems to be the proposition of many commencement speakers at US universities this graduation season. Graduating students, however, have not always welcomed the message. At ceremony after ceremony, they have responded with boos and jeers.

Their reaction is not hard to understand. Students are leaving university at a time when AI is being promoted not only as a tool they must learn to use, but as a force that may transform the labour market they are about to enter. Yet the challenge goes beyond jobs. Universities are also being encouraged to remake themselves around AI, adopting it as a solution to budget pressures, administrative burdens and the demands of employers.

This is where the real danger lies. In the “age of AI”, universities risk becoming victims of their own uncritical embrace of the technology, especially at a time of deep financial strain. Industry stakeholders have strongly encouraged them to move in this direction.

A recent paper sponsored by Cisco, the US networking and technology giant, claimed that “forward-thinking institutions view AI as a solution to their resource constraints”, adding that “AI can automate routine tasks, improve student services and help universities operate more efficiently”. It also insisted that universities must embrace their “role as supply chains for AI-related skills”, explaining that “students entering the workforce expect AI integration, and employers increasingly demand AI literacy”.

This is a revealing way to talk about higher education. Universities are being told to see AI not only as a tool, but as an organising principle: their students imagined as future workers in need of AI literacy, their staff encouraged to streamline their labour, their institutions remade to be more efficient, more automated and more closely aligned with the labour market.

Several have accepted this logic. The University of Minnesota, Dartmouth College and Syracuse University have all signed deals with AI companies. In 2025, California State University (CSU) reached a $17m deal with OpenAI to provide the company’s “education-focused” chatbot to its more than half a million students and faculty.

Surveys show that many CSU faculty and students are not convinced by “AI’s dazzling promises”. Yet that scepticism did not prevent the agreement from being treated as a landmark. For OpenAI, signing up the largest public university system in the United States was proof of concept that AI could be embedded across higher education at scale. For CSU, it was a “huge branding opportunity”, since no other university in the world had adopted AI at this scale. The financial logic is harder to follow. Despite facing roughly $144m in budget cuts, CSU last month renewed the deal on costlier terms, committing to $13m a year over three years, about $39m in total, deepening its bet on AI at the very moment it was cutting elsewhere.

What happens when universities begin to treat more of their work as something to be automated, outsourced or made cheaper through AI? We saw a small but telling example at the graduation ceremony at Glendale Community College (GCC) in Arizona. The college’s leadership used an AI system to read the names of graduating students as they received their diplomas. The system was unable to match the correct names to the students walking across the stage, and the name on the jumbotron did not match the student receiving the diploma.

GCC President Tiffany Hernandez was booed by graduating students and their families when she explained what was happening. “Yep, yep. So that is a lesson learned for us,” she said. One graduating student told media outlets that Hernandez’s apology “didn’t feel sincere and it kinda felt like they didn’t care”, adding: “I would have liked a little more thought to have gone into it rather than pushing something as simple as reading some names off to an AI device.”

The problem becomes more serious still when AI moves from administration into teaching and assessment. Supporters argue that AI can ease administrative burdens, cut costs and, in time, get better at designing classes, marking work and summarising difficult texts. But those promises sit alongside concerns about privacy, bias and accountability, as well as a harder question: if so much of university life is to be streamlined and automated, what remains of the ecosystem of learning and mentorship on which these institutions depend?

The evidence on assessment is sobering. A University of Cambridge-led team tested three “frontier” systems and found that AI routinely undervalues “work awarded top marks by humans, or [overvalues] essays ranked among the lowest”. Unlike human examiners, all the systems were “oversensitive to linguistic features”: handing out higher marks for essay length, vocabulary range and sentence complexity, which are often unrelated to academic standards.

Deborah Talmi, who led the study, warned, “Assessment is not just a system for distributing marks. It is part of how educational meaning is made, so students feel seen, standards are upheld, and trust is maintained. Use of AI in assessment poses a risk to these values.”

This is the heart of the matter. Students attend university not just to receive a diploma or master a syllabus. When they enter campus, they want to be seen, their interests nurtured, and to be helped in making sense of the world and their place in it. If universities hand more of this work over to AI, they risk weakening the very relationships and forms of judgement that make higher education meaningful. Studies have shown that AI usage can hamper critical thinking and weaken the very cognitive skills students need to make their way in the world beyond university.

This is why universities should be wary of the narrative of the imminent AI revolution. The loudest voices pushing it are part of an ecosystem of corporations and tech figures that have invested heavily in the technology and its infrastructure.

Valuations have soared, but these investments have not yet generated the profits needed to justify the hype. Critics warning of an “AI bubble” say its profitability depends on AI being adopted everywhere, in everything, at an unprecedented pace. Universities are especially valuable in this project: they offer AI companies legitimacy, scale and access to future workers, and can be presented as proof that AI is not merely speculative but an essential part of public life. The problem is that they are now treated as a cog in machinery built to generate profits for Big Tech, while students and graduates are made to feel like pawns in the quest for AI’s financial viability.

Also being undermined is the core function of the university. Universities were not built as establishments of financial efficiency, nor should their primary purpose be to supply workers skilled only to serve the labour market. They were built as institutions of teaching and higher learning, meant to nurture critical-thinking citizens eager to make the world a better place.

Which returns us to those graduates and their boos. Their anger may not have been a fully formed critique of AI, Big Tech or the future of higher education. But it captured something real: a refusal to be told that they must simply accept a system that treats them less as students to be educated than as workers to be prepared, data to be processed, and consumers to be managed.

In the “age of AI”, this is the mission of the university that educators, students and the public must defend.

The views expressed in this article are the author’s own and do not necessarily reflect Al Jazeera’s editorial stance.

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