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

Area of Science:

• Neuroscience
• Artificial Intelligence
• Cognitive Science

Background:

• Large-scale brain data tools are increasingly available, yet researchers face challenges integrating them with artificial intelligence (AI).
• Existing training emphasizes tool proficiency over critical reasoning, potentially widening skill disparities.
• AI can accelerate workflows but may lead to over-reliance on automated outputs without understanding limitations, impacting competency acquisition.

Purpose of the Study:

• To propose a cognitive science-based framework for rethinking neuroscience training in the age of AI.
• To ensure AI tools scaffold, rather than replace, critical scientific reasoning.
• To address the gap between AI tool availability and effective researcher integration.

Main Methods:

• Development of five cognitive science-based principles for neuroscience training.
• Analysis of current training paradigms and their shortcomings.
• Case study demonstrating the application of proposed principles using EBRAINS training resources.

Main Results:

• Identified that current AI training in neuroscience prioritizes tool usage over cognitive skill development.
• Proposed principles aim to foster deeper scientific reasoning by integrating AI as a cognitive scaffold.
• Demonstrated the practical application of these principles within existing neuroscience educational frameworks.

Conclusions:

• Neuroscience training must evolve to incorporate cognitive science principles for effective AI integration.
• Rethinking training ensures AI enhances, not hinders, researchers' critical thinking and competency.
• The proposed principles offer a pathway to leverage AI for advancing scientific discovery in neuroscience.