Universities laid the intellectual groundwork for the development of artificial intelligence and have traditionally served as centres of cognitive authority. Universities’ financial strength has long rested on their authority over knowledge creation, preservation and transfer, and their positioning within the broader knowledge economy. However, this central role may be challenged by the rapid advancement of AI, which could easily outcompete universities in knowledge production, preservation and even transfer.
While universities can purchase AI tools, the problem is that they will no longer have preeminent control over knowledge production, preservation and transfer as they used to do. AI will weaken University’s dominance by enabling: Alternative learning pathways (self-directed AI-assisted learning), private knowledge repositories and platforms and non-university research labs with superior AI infrastructure. Even if universities purchase AI tools, they do not control: the underlying models, the training data and the rules governing access and use. This undermines their historical pre-eminence over the knowledge ecosystem.
There is a need therefore for Universities to have a serious conversation about Artificial Intelligence (AI) and its implications for the future of teaching, learning and research.
New methods of knowledge delivery ought to be developed and embraced. The Lecture method will be less attractive because AI will outcompete university professors in knowledge delivery.
The current testing methods through written assignments, dissertation or thesis may require rejigging. Traditional written assessments (essays, dissertations, theses) are increasingly vulnerable to AI assistance. AI will become so ubiquitous that universities may not be able to stop their students from using them. Policing AI use is already proving impractical at scale; detection tools are unreliable and easy to circumvent. Historically, when a technology becomes: cheap, widely accessible, and productivity-enhancing,
institutions adapt rather than prohibit (e.g. calculators, internet, statistical software).
The need for oral defence of assignments, dissertation or thesis may become even more important and research concepts such as originality may require a new definition. In an age where AI can write a Novel, compose poems, write academic textbooks and compose music all within minutes, the effect on intellectual property law and human intellectual creativity raises profound and unresolved questions.
Thankfully, not all disciplines are equally affected. The impact varies: Humanities and social sciences face major assessment challenges while experimental sciences retain strong safeguards through labs, data collection, and methods-based evaluation. Also AI will not easily outcompete universities in all domains. For the time being, universities still hold advantages in: legitimacy and credentialing, peer review and epistemic validation, ethical oversight and norm-setting, as well as basic and curiosity-driven research. AI currently depends heavily on university-generated knowledge and expertise.
Universities will remain central to: framing research questions, interpreting and contextualising knowledge, teaching critical thinking, judgment, and values. These are areas where AI complements rather than replaces universities, and these are the areas universities should focus going forward.
In conclusion, if universities change how they deliver knowledge, how they test their students, and how they integrate AI as tools within the learning and research infrastructure, the University system can and will survive the AI revolution.
Dr. Victor Oluwasina Ayeni
1 February 2026.
