Abstract

This study examines the integration of artificial intelligence (AI) and neuroscience principles in education, focusing on opportunities, challenges, and implications for enhancing learning outcomes. Neuro-education combines neuroscience principles like neuroplasticity, cognitive load theory, and memory formation with AI tools for personalization, engagement, and cognitive optimization. Following PRISMA guidelines, a systematic review of 518 studies was conducted, narrowing to 35 peer-reviewed papers published in the last decade. These papers analyzed AI applications in neuro-education, including adaptive platforms, neurofeedback tools, and ethical considerations. AI tools like adaptive learning systems, neurofeedback interfaces, and gamified environments were found to enhance brain-based learning strategies. Future technologies like VR and AR show strong potential for immersive learning. Key challenges include high costs, data privacy concerns, and algorithmic bias. Interdisciplinary collaboration and affordable, scalable solutions are essential for addressing ethical and technological barriers, enabling equitable and transformative applications of AI in neuro-education.