Deadline: The Science of Learning and Augmented Intelligence Program (NSF)

Proposals for NSF’s The Science of Learning and Augmented Intelligence Program are due Wednesday, Jan. 19, 2022, at 5 p.m. submitter’s local time.

Synopsis

Science of Learning and Augmented Intelligence (SL) supports potentially transformative research that develops basic theoretical insights and fundamental knowledge about principles, processes and mechanisms of learning, and about augmented intelligence – how human cognitive function can be augmented through interactions with others, contextual variations, and technological advances.

The program supports research addressing learning in individuals and in groups, across a wide range of domains at one or more levels of analysis including: molecular/cellular mechanisms; brain systems; cognitive, affective, and behavioral processes; and social/cultural influences.

The program also supports research on augmented intelligence that clearly articulates principled ways in which human approaches to learning and related processes, such as in design, complex decision-making and problem-solving, can be improved through interactions with others, and/or the use of artificial intelligence in technology. These could include ways of using knowledge about human functioning to improve the design of collaborative technologies that have capabilities to learn to adapt to humans.

For both aspects of the program, there is special interest in collaborative and collective models of learning and/or intelligence that are supported by the unprecedented speed and scale of technological connectivity.  This includes emphasis on how people and technology working together in new ways and at scale can achieve more than either can attain alone. The program also seeks explanations for how the emergent intelligence of groups, organizations, and networks intersects with processes of learning, behavior and cognition in individuals.

Projects that are convergent and/or interdisciplinary may be especially valuable in advancing basic understanding of these areas, but research within a single discipline or methodology is also appropriate. Connections between proposed research and specific technological, educational, and workforce applications will be considered as valuable broader impacts but are not necessarily central to the intellectual merit of proposed research. The program supports a variety of approaches including: experiments, field studies, surveys, computational modeling, and artificial intelligence/machine learning methods.