Science of Learning and Augmented Intelligence

The Science of Learning and Augmented Intelligence (SL) program is administered by the National Science Foundation (NSF), an independent agency of the United States government that supports fundamental research and education across all fields of science and engineering. Within NSF, this program is housed under the Directorate for Social, Behavioral and Economic Sciences (SBE), specifically within the Division of Behavioral and Cognitive Sciences (BCS). The SL program advances the understanding of learning principles and processes, as well as the integration of human and artificial intelligence, by supporting transformative, interdisciplinary research that spans individual, group, and societal levels. The primary objective of the SL program is to generate basic theoretical insights and fundamental knowledge about learning mechanisms in humans and how these processes can be enhanced through technology and social collaboration. It emphasizes the study of learning at multiple levels, including molecular and cellular processes, brain systems, cognition, emotion, behavior, and socio-cultural influences. The program also encourages research in augmented intelligence, defined as improving human cognition through interactions with others, technology, and varying contexts. This encompasses human-machine collaboration, artificial intelligence-enhanced decision-making, and the development of adaptive and collaborative technologies. The SL program is especially interested in how technological connectivity enables collective models of learning and intelligence that surpass individual capabilities. It explores the emergence of group, organizational, and network intelligence, and their relationship to individual learning and cognition. Research may address the co-creation of knowledge, robust learning models, cognitive resilience, learning transfer across domains, and methods that integrate levels of analysis from biological to behavioral. Convergent and interdisciplinary approaches are encouraged, but proposals focused within a single discipline are also welcome, provided they contribute significantly to the program's goals. Applications are evaluated for both intellectual merit and broader impacts. Projects can employ various methodologies, including experiments, field studies, computational modeling, surveys, and AI or machine learning techniques. While technological or workforce applications are not required, they are considered favorable in broader impacts. Specific research questions may include learning generalization, cognitive resilience, human-AI task performance, human-machine interface design, and bio-inspired models for artificial systems. Proposals must adhere to the NSF Proposal & Award Policies & Procedures Guide (PAPPG) and be submitted through Research.gov or Grants.gov, following the standard preparation and submission protocols. There is no required pre-application step such as a letter of intent or concept paper. The program operates on a recurring schedule, with proposals accepted biannually. Target dates for submission are the first Wednesday in August and the second Wednesday in February each year. The next target deadlines are August 5, 2026, and February 10, 2027. Recurrence follows an annual pattern, with the next cycle projected for February 2027 based on the current timeline. The program is supported by multiple program directors, including Soo-Siang Lim, Elizabeth F. Chua, and Anna V. Fisher, with contact information provided for inquiries. There is no publicly available PDF solicitation for this opportunity; applicants must refer to the NSF website for the most up-to-date guidelines and announcements.