Bridging the neuro-AI chasm: a framework for scalable, contextually adaptive training resources in large-scale brain data science

Despite the increasing availability of large-scale brain data tools, many researchers struggle to use them effectively alongside AI. This is not due to a lack of access, but because existing training resources emphasize proficiency with these tools over critical reasoning. AI accelerates workflows but also risks deepening skill disparities: researchers with strong foundational knowledge can integrate AI-generated insights, while others become dependent on automated outputs without fully understanding their limitations, inducing risks to competency acquisition. Conventional training approaches assume that exposure to AI tools naturally translates to expertise, overlooking the need for structured cognitive engagement. We propose five cognitive science-based principles to rethink neuroscience training, ensuring AI serves as a scaffold for deeper scientific reasoning rather than a passive automation tool. We demonstrate the applicability of these principles in neuroscience education through a case study of EBRAINS training resources.