Science of Learning and Augmented Intelligence (SL)

The Science of Learning and Augmented Intelligence program is administered by the U.S. National Science Foundation through the Directorate for Social, Behavioral and Economic Sciences and the Division of Behavioral and Cognitive Sciences. The program supports potentially transformative research aimed at developing basic theoretical insights and fundamental knowledge about the principles, processes, and mechanisms of learning, as well as augmented intelligence—defined as the enhancement of human cognitive function through interactions with others, with technology, or through contextual variations. The funder has a broad mandate to advance scientific understanding and encourages studies that explore learning in both individuals and groups across multiple domains and levels of analysis. The program’s scope includes research at molecular and cellular levels, brain systems, cognitive, affective, and behavioral processes, and social and cultural influences on learning. It also supports research into augmented intelligence that articulates principled approaches to improving human learning, design, complex decision-making, and problem-solving through interaction with others or artificial intelligence. Examples include using knowledge about human functioning to enhance collaborative technologies capable of adapting to human needs. Special interest is given to collaborative and collective models of learning and intelligence enabled by rapid, large-scale technological connectivity, with a focus on how people and technology can achieve outcomes together that neither could achieve alone. Research topics of interest include mechanisms that enable transfer of learning across contexts and domains, generalization from limited experiences, and resilience of robust learning against interference from new experiences. The program also invites work on consolidation and reconsolidation of learning into stable memory, as well as studies on the interplay between individual and collaborative processes in knowledge co-creation and collective intelligence. Questions about integrating knowledge across different levels of analysis, from neuronal mechanisms to societal influences, are central to the program’s objectives. Proposals may adopt convergent or interdisciplinary approaches, though research within a single discipline or methodology is also appropriate. Connections to technological, educational, and workforce applications are considered valuable broader impacts but are not required for intellectual merit. The program welcomes methodologies including experiments, field studies, surveys, computational modeling, and artificial intelligence or machine learning approaches. Additionally, insights from biological learning systems are encouraged to inform and inspire innovations in artificial intelligence, neuromorphic engineering, materials science, and nanotechnology. The program operates with two annual target dates for full proposals: the second Wednesday in February and the first Wednesday in August, with the next upcoming due dates on February 11, 2026, and August 5, 2026. These deadlines recur annually. Applications must be submitted through Research.gov or Grants.gov, following the NSF Proposal & Award Policies & Procedures Guide or the NSF Grants.gov Application Guide, respectively. The opportunity is open to a wide range of applicants without restriction, meaning all eligible entities in the United States may apply. Program contacts include Soo-Siang Lim, Elizabeth F. Chua, Anna V. Fisher, and Laneisha Mayo, each of whom can be reached by phone or email for questions about the program. The program also hosts and participates in relevant workshops and webinars, such as the Telluride Neuromorphic Cognition Engineering Workshop and a weekly webinar series on augmented intelligence.