Science of Learning and Augmented Intelligence
🏛 U.S. National Science Foundation (NSF)
✓ Free, no account · Source: Grants.gov · Last verified Jul 16, 2026
Can you apply?
This grant is for academic institutions, research organizations, and scientists seeking to fund basic research on learning mechanisms and augmented intelligence. Eligible applicants typically include universities, colleges, research institutes, and nonprofit research organizations. The program supports theoretical and empirical research across disciplines examining how humans learn individually and collectively, and how human cognitive function can be enhanced through technology or collaboration. Projects may span molecular mechanisms, brain systems, cognitive processes, and social influences on learning.
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Program description
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 or with technology, or through variations in context.
The program supportsresearch addressing learning in individuals and in groups, across a wide range of domains at one or more levels of analysis, including molecular and cellular mechanisms; brain systems; cognitive, affective and behavioral processes; and social and 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 or through 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 the capacity to learn to adapt to humans.
For both aspects of the program, there is special interest in collaborative and collective models of learning and 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 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 or machine learning methods.
Examples of general research questions within scope of Science of Learning and Augmented Intelligence (SL)include:
- What are the underlying mechanisms that support transfer of learning from one context to another or from one domain to another?How is learning generalized from a small set of specific experiences?What is the basis for robust learning that is resilient against potential interference from new experiences?How is learning consolidated and reconsolidated from transient experience to stable memory?
- How do human interactions with technologies, imbued with artificial intelligence, provide improved human task performance?What models best describe the interplay of the individual and collaborative processes that lead to co-creation of knowledge and collective intelligence? In what ways do the capacities and constraints of human cognition inform improved methods of human-artificial intelligence collaboration?
- How can we integrate research findings and insights across levels of analysis, relating understanding of cellular and molecular mechanisms of learning in the neurons, to circuit and systems-level computations of learning in the brain, to cognitive, affective, social and behavioral processes of learning? What is the relationship between assembly of new networks (development) and learning new knowledge in a maturing or mature brain? What concepts, tools (including Big Data, machine learning, and other computational models) or questions will provide the most productive linkages across levels of analysis?
- How can insights from biological learners contribute and derive new theoretical perspectives to artificial intelligence, neuromorphic engineering, materials science and nanotechnology? How can the ability of biological systems to learn from relatively few examples improve efficiency of artificial systems?How do learning systems (biological and artificial) address complex issues of causal reasoning?How can knowledge about the ways in which humans learn help in the design of human-machine interfaces?
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- NSF Standard Form 424 (SF-424)
- Project Narrative
- Budget and Budget Justification
- Biographical Sketches (for senior personnel)
- Current and Pending Support documentation
Program contact
- 👤 U.S. National Science Foundation
- 📧 grantsgovsupport@nsf.gov
- 📞 703-292-4203
Funding track record
Recent awards under CFDA 47.075 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
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$38,357,018
-
$18,499,999
-
$13,999,656
-
$10,999,998
-
$8,043,354
-
$7,998,747
-
$5,500,000
-
$5,237,549
-
$5,200,000
-
$5,047,151
Top States by Funding
- MI 9 awards $94.1M
- DC 6 awards $20.0M
- AZ 7 awards $19.6M
- NY 9 awards $17.0M
- IL 4 awards $16.4M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 47.075). How funding has trended year over year.
| 2024 | $292,390,000 | |
| 2025 | $219,410,000 | |
| 2026 est. | $92,200,000 |
FAQ
Who can apply for this NSF Science of Learning and Augmented Intelligence grant?
Universities, research institutes, and nonprofit research organizations are typically eligible. Principal Investigators must have appropriate institutional affiliation and meet NSF eligibility requirements.
What is the deadline?
The deadline is August 5, 2026. This is a fixed deadline, not rolling.
What types of research are supported?
The program funds basic research on learning principles, memory consolidation, transfer of learning, and how humans can augment cognition through technology or collaboration. Both disciplinary and interdisciplinary approaches are welcome.
Is cost-sharing required?
No, cost-sharing is not required for this program.
What is the typical award amount?
Awards typically begin around $550,000, though the upper limit is not specified in this program announcement.
💡 Tips for applicants
- Emphasize the fundamental, basic research nature of your work rather than immediate applications. The program values theoretical insights.
- Show how your project connects learning mechanisms across multiple levels of analysis (molecular, cognitive, social, etc.) if possible.
- Demonstrate how your research addresses collaborative or collective aspects of learning, especially those enabled by technology or AI.
- Clearly articulate the intellectual merit of your proposed research separate from broader impacts.
- Use the specific research questions in the solicitation as a guide to align your proposal with program priorities.
⚠️ Common mistakes
Framing proposals primarily as application-focused or technology-development projects rather than basic research on learning mechanisms. Proposals that fail to specify research questions within the program's scope or that ignore the emphasis on learning transfer, memory consolidation, and augmented intelligence through collaboration.
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