Purpose As generative artificial intelligence (AI) becomes increasingly prevalent in medical practice and education, medical schools must equip students with skills to critically evaluate & ethically use these technologies. To address deficiencies in AI literacy, collaborative learning, & clinical reasoning associated with traditional didactic approaches, we implemented a collaborative AI-based peer learning project for medical students. Methods A team-based project was implemented in a pre-clerkship neuroscience course. Learners (n=61) worked in groups of 7-8 to develop peer learning resources using generative AI. Each group was assigned a learning objective in neuroanatomy, pathophysiology, or clinical presentation of disease & researched its topic & used AI to create (1) a novel teaching tool (e.g., mnemonic or visual aid) & (2) an NBME-style clinical reasoning question. Teams documented their prompt engineering process, iteratively refined AI-generated materials, verified accuracy, & developed rationales. The project culminated in interactive presentations showcasing their teaching tool, NBME question, & AI evaluation process. Results Presentations (n=8 groups) were evaluated qualitatively. Teams averaged 4-6 prompt revisions per resource to improve clarity and accuracy. Most groups reported using board-prep resources to validate AI-generated content. Peer evaluations & self-reflections were qualitatively assessed. Feedback indicated high engagement and perceived value in reinforcing neuroscience concepts & developing AI literacy. Conclusions Integrating generative AI into collaborative learning can simultaneously develop AI literacy, critical evaluation skills, teamwork, communication, content mastery, & clinical reasoning. Requiring students to document & present their AI refinement process promoted metacognitive reflection on AI limitations & accuracy verification. This scalable model provides a framework for integrating AI literacy into existing coursework.