Science of Learning and Augmented Intelligence

The Science of Learning and Augmented Intelligence (SL) program, administered by the National Science Foundation (NSF), supports groundbreaking research into the fundamental principles and mechanisms underlying learning and augmented intelligence. As part of the Directorate for Social, Behavioral and Economic Sciences, specifically within the Division of Behavioral and Cognitive Sciences, this initiative targets scientific advancements that can deepen our understanding of how individuals and groups learn, adapt, and augment their cognitive capabilities through interaction with others and technological systems. SL funding supports a wide range of theoretical and empirical research focusing on learning at multiple levels—from molecular and neural processes to cognitive, social, and cultural dimensions. The scope encompasses various methodological approaches including experiments, field studies, computational modeling, and artificial intelligence. Research topics may explore the transfer of learning across contexts, the resilience of memory, and the consolidation of learning into long-term cognitive structures. The program places high value on interdisciplinary and convergent research that can bridge insights across different levels of analysis, such as linking cellular mechanisms with behavioral outcomes or integrating cognitive processes with large-scale technological systems. A particular emphasis is placed on augmented intelligence: the enhancement of human cognitive performance through collaboration, technological assistance, and artificial intelligence. The SL program supports research that examines how human cognition and technology interact in processes like complex decision-making, design, and problem-solving. Proposals might address, for example, how AI tools can adapt to human behaviors or how human–AI collaboration can enhance learning outcomes and efficiency. Moreover, the program is keen on understanding how collective intelligence emerges in groups and networks and how such phenomena relate to individual learning and cognition. The program encourages proposals with broader impacts, especially those that link research to potential applications in technology, education, and workforce development. However, while such applications are appreciated, they are not central to the intellectual merit criterion. Research purely within a single discipline is also acceptable, provided it contributes meaningfully to the program's core objectives. Interdisciplinary work may receive special consideration, particularly when it leverages advanced tools like big data analytics or machine learning. Proposals must follow the guidelines of the NSF Proposal & Award Policies & Procedures Guide (PAPPG), adhering strictly to the submission procedures via Research.gov or Grants.gov. Currently, there are no specific deadlines posted; the solicitation (PD 19-127Y) is awaiting new publication. As such, applicants are advised to check back for updated due dates. No pre-application such as Letters of Intent or Concept Papers are required based on the current information available. For inquiries, potential applicants may contact the listed program directors: Soo-Siang Lim, Elizabeth F. Chua, and Anna V. Fisher, or reach out to Laneisha Mayo for business operations support. Each is affiliated with the SBE/BCS division and can be contacted via the provided phone numbers and emails. The program’s future cycles remain unannounced, but the recurring nature of the opportunity indicates that updated deadlines are expected. Applicants should monitor NSF’s site accordingly to align their submission with the program's evolving requirements.