In today’s world, digital transformation is no longer a buzzword—it is the defining reality of our age. From how we work to how we communicate, navigate, and learn, digital technologies shape every aspect of our daily lives. And yet, many educational systems are still preparing students for a world that no longer exists.
Whether you're debugging a tricky piece of code, designing an experiment, or writing your first scientific article—how you think matters. In academic research, especially in computer science, understanding different forms of reasoning helps you ask better questions, build stronger arguments, and spot weak logic.
In computer science research, we're not just writing code—we’re building knowledge. That’s why every well-structured research project relies on more than just experiments or data: it’s grounded in hypotheses, models, and theories.
Computer science is a field of dual identities. On the one hand, it’s abstract, mathematical, and conceptual. On the other, it’s deeply applied, data-driven, and user-focused. These two identities are reflected in the two primary modes of research in computer science: theoretical and empirical.
When we talk about research in computer science, we often imagine clean mathematical proofs or large-scale empirical evaluations. But what about when you're working in a classroom, designing an AI-based tutoring system, or building software to support collaborative learning?