Innovation-First Learning 

Let’s be honest: what we teach in Computer Science and Software Engineering often doesn’t align with what the industry truly needs. Updating curricula for an AI-driven world is tough. Blending deep theory with modern tools into a single coherent learning path is a significant challenge. And to complicate things further, students now rely on AI both while learning and once they enter the workforce. These shifts raise difficult questions: Should we teach students to implement things they’ll never build without AI? Or should we ignore the fast-changing tool landscape and focus solely on deep theory?

After seeing this gap firsthand, both as a long-time educator and as someone with over 30 years of industry experience, regularly interviewing and hiring new graduates, I decided to try something new. Over the past year, I’ve built and taught courses in various AI disciplines (LLM, GenAI, Computer Vision) using an approach I call Innovation-First Learning (IFL). In my courses, theory and tools come together from day one, and students jump straight into substantial, technically deep projects that move far beyond simple exercises and into real innovation.