It was a question that came up early during one of the sessions at the DIGITAfrica Summer School in IT at Strathmore University and it stayed with me. Not just because of its ambition, but because of what it revealed: a curiosity about how intelligent systems actually work beneath the surface. Coming into the programme, I was interested in Artificial Intelligence from both a technical and conceptual perspective. I wanted to understand not just what these systems do, but how they are structured, trained, and continuously improved through data and research. The Artificial Intelligence and Big Data track offered exactly that. Rather than focusing on end products, the programme emphasised the processes behind them—data collection, experimentation, model training, and refinement. It became clear that AI is not simply about building something impressive, but about understanding how data and research inform intelligent decision-making.

One session that stood out explored how models learn from data through iterative training. What had once felt abstract terms like deep learning and reinforcement learning started to make sense when broken down into practical examples. Seeing how a model improves over time, adjusting based on feedback and results, made the idea of “learning systems” feel far more tangible. This experience reshaped how I think about contributing to the field. Meaningful work in AI does not always mean creating entirely new systems; it can also mean applying existing tools thoughtfully to solve real-world problems. Another key takeaway was the role of research. AI, as I came to understand, is inherently iterative. It involves asking the right questions, testing ideas, and learning from the results, often repeatedly. This perspective made the field feel more accessible, especially as someone still at the beginning of the journey.

Beyond the technical sessions, the interactions with lecturers and fellow participants added an important dimension to the experience. In panel discussions on emerging technologies and career pathways, it became clear that many of us were navigating similar questions—about where to start, what to focus on, and how to grow in such a rapidly evolving field. That shared curiosity created a sense of both challenge and possibility. As someone with a growing interest in technology and education, I found myself particularly drawn to the role AI can play in shaping learning experiences. The potential to design systems that enhance accessibility, engagement, and personalisation feels especially relevant—not just globally, but within our own local context.

Looking back, my initial question has changed. I am no longer focused on how to build something as complex as a large-scale AI system from scratch. Instead, I am more interested in understanding how such systems work and how they can be applied responsibly and effectively. More importantly, I am interested in how AI can be used to develop solutions that address real challenges around us—in education, accessibility, and everyday life. In a rapidly advancing field, the ability to think locally while building with global tools may be one of the most valuable skills to develop.

The DIGITAfrica Summer School was not just an introduction to Artificial Intelligence. It was an introduction to a way of thinking—one grounded in curiosity, research, and continuous learning. And while I may not be building advanced AI systems just yet, I am asking better questions. More relevant ones. And in a field defined by constant evolution, that may be the most important place to begin.

By Hellen Nzisa