By Adeyemi Adedeji
Today, 27 May, 2026 is Children’s Day. It also happens to be a public holiday for us adults, too. So, I could afford to take a few minutes to write this piece that has been running around in my head for a while, about Owoeye Daniela Jesudunsi, the 16-year-old who scored 372 in the UTME and emerged as the highest scorer nationwide, whose story was trending just over a week ago. What had fascinated me was not merely her score itself, but how she reportedly prepared for the examination.
She studied her materials, wrote notes by hand, fed those notes into AI, asked the AI to identify gaps in her understanding, questioned the AI repeatedly, and then allowed the AI to question her back. Through that iterative process, she deepened her understanding layer by layer until the knowledge became internalized. By the time she entered the examination hall, she was not merely recalling information; she had processed and understood it deeply enough to perform at an extraordinary level.
Redefining Effort: Shortcuts vs. Deep Engagement
And this is where the story becomes especially interesting to me in light of the recent Oxford-style debate on Gen Z, intelligence, and intellectual rigour, convened by the Education Reform and Innovation Team (ERIT).
Because when many people hear “AI-assisted learning,” they instinctively assume intellectual laziness. They imagine shortcuts. Reduced effort. Dependency. Surface-level engagement.
But what this young lady demonstrated was not intellectual weakness. It was rigour.
Think about the discipline embedded in that process: writing notes by hand, subjecting her understanding to repeated interrogation, actively searching for gaps in comprehension, engaging in iterative questioning,
testing herself continuously, and refining understanding until mastery emerged. That is not passive learning. That is deep cognitive engagement.
In fact, one could argue that what she demonstrated resembles a more rigorous form of learning than what often happens in many conventional classrooms, where students passively receive information, memorise temporarily, reproduce it in examinations, and forget it shortly afterwards.
The Crisis of Legitimacy in Traditional Schooling
This raises a profound question. What exactly is learning? And perhaps even more importantly, what is school? Because if a young person can now build a deeply personalized, adaptive, intellectually rigorous learning system around herself using AI, then we may need to seriously rethink some of the assumptions upon which modern schooling has been built.
One of the speakers during our recent ERIT Oxford-style debate made a statement that has stayed with me. He argued that the traditional school system is gradually losing legitimacy because it no longer aligns with how modern humans learn, express intelligence, build identity, or access opportunity. That is a very serious claim. But increasingly, it is becoming difficult to dismiss.
For over a century, school has largely functioned as four things: a knowledge distribution system, a credentialing mechanism, a social sorting institution, and a workforce preparation pipeline. But artificial intelligence, ubiquitous internet access, creator economies, remote work, alternative credentials, simulation technologies, and decentralized learning are now destabilizing all four functions simultaneously.
From Classrooms to Human Development Networks
The implication is profound. The school of the future may no longer primarily be a place, a timetable, or a curriculum delivery mechanism. Instead, school may become something radically different: a human development network. Not a building where information is delivered, but a dynamic ecosystem where human capability is cultivated. Its core functions will likely shift from instruction to orchestration, from memorisation to cognition, from standardisation to personalization, from compliance to agency, and from credential accumulation to capability demonstration. Some things we currently consider normal in education may gradually disappear.
For example, age-based batch learning may increasingly look irrational. The idea that everyone born in the same year should learn the same thing, at the same pace, in the same way, may not survive the future. Learning pathways are likely to become competency-driven and adaptive.
The Evolution of the Educator and the Credential
The role of the teacher will also change dramatically. The teacher as the primary source of knowledge is already becoming obsolete. AI systems will outperform most human teachers in information retrieval, explanation, translation, personalization, assessment support, and adaptive tutoring. But that does not make teachers irrelevant. It makes them more important in different ways. The teacher of the future becomes a mentor, a cognitive coach, an ethical guide, a systems thinker, a motivator, and a curator of meaning.
Similarly, the monopoly universities once held over credential legitimacy is weakening. Employers are increasingly moving toward portfolios, demonstrable capability, simulations, micro credentials, apprenticeships, and performance evidence rather than simply degrees. Also, passive classroom instruction, the traditional lecture model, survives today largely because it is administratively efficient, not because it is cognitively optimal.
What AI Cannot Automate: The Indispensable Human Core
The future of learning will likely become more interactive, project-based, simulation-heavy, interdisciplinary, and deeply collaborative. But, even as all this changes, some things will remain absolutely essential.
Research must continue. Civilization still requires scientific discovery, medicine, engineering, ethics, governance, philosophy, and advanced inquiry.
Humanity will still need places where difficult ideas are pursued rigorously over long periods of time. So, school will not disappear entirely. But, its legitimacy will increasingly depend on whether it can do things the internet and AI cannot do alone.
The schools that survive will likely become research and innovation hubs focused less on content delivery and more on experimentation, invention, collaboration, and solving real-world problems. They will become environments for human formation because some of the most important human capacities cannot easily be automated. These include character formation, moral reasoning, emotional regulation, leadership, socialisation, and meaning-making. Ironically, as AI advances, these deeply human dimensions may become even more important, not less.
The scarcity of the future may no longer be information. It may instead be focus, depth, discernment, judgment, synthesis, and intellectual stamina.
Conclusion: A New Blueprint for Capability
The future school may therefore become less about knowing things and more about learning how to think well in environments of infinite information. And, even if learning becomes decentralized, society will still need trusted systems to verify competence, ethics, reliability, and expertise, especially in fields like medicine, engineering, aviation, law, cybersecurity, and scientific research.
So perhaps the school of the future can be defined this way: A flexible, technology-enabled, human-centred ecosystem designed to cultivate cognition, character, creativity, collaboration, and real-world capability through personalised, lifelong, and problem-driven learning. Not a factory. Not a holding bay for children. Not merely a certification pipeline. But, a capability development ecosystem. And this, is why conversations around Gen Z, intelligence, AI, and education matter so much. Because beneath all the arguments about attention span, technology, or generational change lies a deeper question, ‘Are young people failing existing systems? Or are existing systems becoming obsolete?’
