Science of Learning and Augmented Intelligence (SL) | PD 19 127Y

Opportunity Information: Apply for PD 19 127Y

The National Science Foundation (NSF) Science of Learning and Augmented Intelligence (SL) program (Funding Opportunity Number PD 19-127Y) is a discretionary grant opportunity that funds potentially transformative basic research on two closely linked themes: the science of learning and augmented intelligence. On the learning side, the focus is on building fundamental, theory-driven understanding of how learning works, including the principles, processes, and mechanisms that let people (and, in some cases, other biological learners) acquire, stabilize, and use knowledge over time. On the augmented intelligence side, the program is interested in principled explanations of how human cognitive functioning can be improved through interaction with other people, with intelligent technologies (including AI-enabled tools), or through changes in context. The unifying idea is not just to build better applications, but to generate deeper scientific insights into learning and into human-technology teaming that can reshape how the field understands intelligence and learning in the first place.

A key feature of this program is its broad scope across levels of analysis. NSF explicitly welcomes research that examines learning in individuals and in groups, and it allows proposals to sit anywhere from molecular and cellular mechanisms, to brain circuits and systems, to cognitive, affective, and behavioral processes, and up through social and cultural influences. That means an SL project might study synaptic or cellular changes that support memory consolidation, or it might test how motivation and emotion shape learning strategies, or it might examine how norms, communication patterns, and culture influence what groups learn and how quickly they adapt. Projects that connect multiple levels of analysis are especially aligned with program priorities, such as work that links neural mechanisms to observed behavior, or that bridges individual cognition with team or network-level outcomes. At the same time, the solicitation makes clear that strong single-discipline or single-method projects are also appropriate as long as they advance basic understanding.

The augmented intelligence portion emphasizes improving human performance in learning and related activities such as complex decision-making, design, and problem-solving through well-justified models of interaction with other humans and with AI-enabled technologies. NSF is looking for research that goes beyond simply adding automation and instead explains how systems can be designed to complement human strengths and mitigate human limits. This includes research that uses knowledge about human cognition to guide the design of collaborative technologies that can learn and adapt to people, as well as research that clarifies when and why human-AI collaboration succeeds or fails. A recurring theme is that intelligence can be distributed across people and tools, and that the best outcomes may come from systems where humans and technology do different parts of the work in ways that neither could accomplish alone.

Another major emphasis is collective, collaborative, and networked intelligence at scale, enabled by modern connectivity. The program highlights special interest in models of learning and intelligence that emerge when people and technologies interact rapidly and broadly across platforms, organizations, or networks. This includes studying how group-level or organizational intelligence emerges, how it relates back to individual learning and cognition, and how knowledge is co-created through collaboration. In practical terms, SL is open to proposals that explain how teams coordinate, how shared representations form, how trust and communication affect collective reasoning, and how network structures shape what groups learn. It also supports work that explores how these group dynamics intersect with AI tools, such as collaborative platforms that recommend, summarize, or adapt in response to users over time.

Methodologically, SL is deliberately flexible. NSF indicates that experiments, field studies, surveys, computational modeling, and AI or machine learning approaches are all within scope. This creates room for tightly controlled lab studies of learning mechanisms, real-world classroom or workplace studies, large-scale online experiments on collaboration, and computational approaches that formalize theories and test them against data. Projects can also use big data and modern computational tools as a bridge across levels of analysis, for example by linking fine-grained behavioral traces to cognitive models, or by combining neuroscience measures with machine learning methods to characterize learning trajectories.

The solicitation provides a set of example research questions that signal what NSF considers central to the program. On learning, these include questions about transfer and generalization (how learning carries over across contexts or domains, and how broad rules are learned from a small number of experiences), robustness (why some learning persists despite interference from new experiences), and memory dynamics (how learning is consolidated and reconsolidated as memories stabilize or change). It also includes integrative questions like how to connect cellular and molecular learning mechanisms to circuit and systems-level computations and to cognitive and social processes, as well as developmental questions about how building new neural networks over time relates to learning in maturing or mature brains. On augmented intelligence, examples include how AI-imbued technologies can improve human task performance, what models capture the interplay between individual and collaborative processes that produce collective intelligence, how human cognitive constraints should shape human-AI collaboration, and how insights from biological learning might inform artificial intelligence, neuromorphic engineering, materials science, or nanotechnology. Additional examples point to biologically inspired efficiency (learning from few examples), causal reasoning in biological and artificial systems, and using knowledge of human learning to improve human-machine interfaces.

From a proposal positioning standpoint, the program draws a line between intellectual merit and broader impacts. Connections to specific technological, educational, or workforce applications are welcomed and can strengthen broader impacts, but they are not required to be the central driver of the science. In other words, a proposal can be strongly aligned if it primarily advances fundamental theory and mechanism, even if applications are only a secondary outcome. Conversely, applied work is most responsive when it is used as a vehicle to answer foundational questions about learning or about human-AI augmentation, rather than focusing mainly on product development or near-term deployment.

Administrative details from the opportunity listing indicate this is an NSF grant program in the Science and Technology and other Research and Development category (CFDA 47.075), with eligibility listed as unrestricted. The original closing date shown is 2024-08-07, and the opportunity was created on 2019-09-19. The award ceiling and expected number of awards are not specified in the provided listing, which typically means applicants should consult the current NSF program page or solicitation for up-to-date budget guidance, project size expectations, and submission windows.

• The National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Science of Learning and Augmented Intelligence (SL)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.075.
• This funding opportunity was created on 2019-09-19.
• Applicants must submit their applications by 2024-08-07. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: Unrestricted.

Apply for PD 19 127Y

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