From first lines of code to peer-reviewed science: what high schoolers can build with AI research

When I began teaching at Veritas AI, I first worked in the AI Scholars program, helping students build a foundation in Python, data science, and core machine learning. Later, I designed and taught AI + Medicine, which moves quickly from those fundamentals into clinical datasets, medical imaging, and model evaluation. These courses give students shared tools and a common starting point. From there, some students choose to continue their work in a more individualized research mentorship. In that setting, the student takes ownership of the question, and we work together to develop the methods, run experiments, and understand what the results mean. It is simply a different mode of learning: more open-ended, more iterative, and shaped by the student’s curiosity. In the fellowship program, students learn by doing. They explore datasets that are noisy and imperfect. They test baselines, refine architectures, troubleshoot errors that make no sense at first, and get comfortable asking, “Why did the model behave that way?” Research becomes less about performing correctness and more about learning how to think. The reward is not just a result: it is the moment the student realizes that they are capable of building knowledge, not just consuming it. Over the past two years, several of my mentees have taken their research from idea to publication. Five of these projects were published in the Journal of Emerging Investigators, each one shaped by a student’s personal curiosity and their persistence in solving a problem that mattered to them. What stands out is how different these problems are: ocean sustainability, global health, oncology. The diversity reflects the students, not any instruction to pursue a particular theme.