The Vanishing Ladder: Rethinking Academic Training in the Age of AI

By Richard Sebaggala

 

An article I recently came across contains a quiet but significant observation: AI is not only replacing jobs, it is also replacing learning opportunities. The types of tasks that helped young people gain experience (entry-level administrative work, basic data tasks, support functions) are being quietly handed over to machines. And while this trend is already causing alarm in the developed countries, I couldn’t help but think how much more disruptive it could be in countries like Uganda, where graduate unemployment is already painfully high and where academic training often lacks the necessary practical immersion.

In Uganda, almost every university has relied on internships as a bridge between theory and practise. It is the only moment in a student's academic career when they can actually enter a workplace and apply what they have learnt in class. Of course, these internships are not enough (the number of opportunities is often much less than the number of students who need them), but at least they exist. In my experience supervising interns across various organizations, I've observed that these placements largely served a dual purpose: interns handled repetitive administrative tasks while also quietly observing and learning the ropes.

Imagine what will happen when the tasks that are critical to learning such as filing reports, organising data, writing summaries and composing simple letters are done faster, cheaper and more consistently by AI tools. This is not a distant scenario. It is already happening in global companies and is slowly finding its way into companies and NGOs in Uganda. If we are not careful, AI will not only destroy jobs, it will also displace the basic tasks where students can observe, try, fail, ask questions and grow. If these tasks disappear, what will happen to internships as we know them?

To me, this is the more pressing threat: not mass unemployment overnight, but the silent erosion of educational opportunities. The risk isn't that students don't find a job after graduation, but that they never get a fair chance to prepare for the labour market at all. We often talk about AI taking over "low-skilled" jobs, but these tasks, as mundane as they may seem, are where many professionals actually start.

What worries me most is how little our academic systems have adapted. Universities are still producing graduates who are trained for jobs that may no longer exist. Curricula still focus heavily on theory and make little effort to incorporate AI skills, digital skills or simulations of real-world work environments. Meanwhile, students are entering a labour market that is rapidly changing under their feet without the support of institutions to help them find a balance.

At this point, universities and faculties must now ask themselves a new question: What will your graduate look like in the age of AI? A law school should ask how a future lawyer will work with or alongside AI? An economics department should ask what tools an economist will need to stay relevant when forecasting and modelling are now supported by AI. Business departments, medical schools, engineering programmes, even social science departments - all need to think of their graduates not as a competitor to AI, but as someone who is able to lead in a world shaped by it. Once we start asking these kinds of questions, we stop seeing AI as a threat and start seeing it as a force to be harnessed. But more importantly, we start to orientate education towards a future that is already upon us.

If faculties don't start this rethink now, we risk something much more subtle but deeply damaging. We could end up with a generation that is well-educated on paper but excluded in practise: graduates who are equipped with knowledge but lack the ability to apply it. They will enter a world that demands real-world experience and digital adaptability, yet the very systems that trained them have offered neither. This is not an alarm bell for a distant future, but a call for urgent reflection. The cost of ignoring these changes now will be far greater when the full consequences unfold.

We need to completely rethink the purpose of academic education. It is no longer enough to prepare students for exams. We need to prepare them to collaborate with AI, to learn quickly, to solve problems in environments where humans and machines will work side by side. This means that we need to revise the way we teach, but also that we need to create new types of internships and apprenticeships where students are exposed to AI-powered workflows rather than being displaced by them.

If traditional internships are shrinking, then universities should start using AI to simulate work tasks. If there are fewer and fewer entry-level positions, companies should be encouraged to keep some open, not for the sake of efficiency, but for the future of human learning. We may not be able to stop automation, but we can at least make sure it doesn’t flatten the stepping stones that young people need to grow into the skilled workers we will one day depend on.

AI will continue to change the landscape. That is a fact. But how we prepare our students to traverse this changing terrain is still a choice. The challenge now is not to resist AI, but to make sure it doesn’t steal our opportunity to learn. Because no matter how powerful machines become, it is still people who ensure that institutions function, societies develop and ideas grow. And they all have to start somewhere.

The future of academic education is not about choosing between people and machines. It’s about creating space for people to grow, even if machines do more. If we get this balance right, we’ll be fine. But if not, we’ll look back and realise that we built a smart future but forgot to educate the people who will inherit it.