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Deadline was March 31, 2024. Program is now closed/archived.
Advancing Education for the Future AI Workforce (EducateAI) is sponsored by National Science Foundation (NSF). This Dear Colleague Letter (DCL) encourages proposals that advance inclusive computing education to prepare preK-12 and undergraduate students for the AI workforce. It supports professional learning, inclusive AI educational resources (curricula, tools, data sources, assessments), and educational infrastructure.
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Advancing education for the future AI workforce (EducateAI) | NSF - U.S. National Science Foundation NSF's implementation of the revised 2 CFR NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website .
These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.
Important information for proposers All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements.
Submitting a proposal prior to a specified deadline does not negate this requirement.
Updates to NSF Research Security Policies On July 10, 2025, NSF issued an Important Notice providing updates to the agency's research security policies, including a research security training requirement, Malign Foreign Talent Recruitment Program annual certification requirement, prohibition on Confucius institutes and an updated FFDR reporting and submission timeline. This document has been archived.
Advancing education for the future AI workforce (EducateAI) Encourages the submission of novel and high impact proposals that advance inclusive computing education that prepares preK-12 and undergraduate students for the AI workforce. Encourages the submission of novel and high impact proposals that advance inclusive computing education that prepares preK-12 and undergraduate students for the AI workforce.
Artificial Intelligence (AI) is transforming the ways in which citizens navigate their daily lives, researchers make discoveries, educators teach their students, students learn both individually and collaboratively, and manufacturers build products.
As new AI-driven discoveries and capabilities emerge, it is critical to promote responsible innovation, competition, and collaboration to unlock the technology’s potential to solve some of society’s most difficult challenges and maintain U.S. leadership in applying AI to address critical global challenges.
To develop the next generation of talent for a diverse, well-trained AI workforce, the computing community must create new pathways and educational experiences that provide the knowledge, skills, and dispositions necessary for current and future AI careers.
To address this growing need, the Directorates for Computer and Information Science and Engineering (CISE) and STEM Education (EDU) are launching the EducateAI initiative to support educators to make state-of-the-art, inclusive AI educational experiences available nationwide.
With this Dear Colleague Letter (DCL), EDU and CISE encourage the submission of novel and high impact proposals that advance inclusive computing education that prepares preK-12 and undergraduate students for the AI workforce .
This EducateAI DCL seeks to support: professional learning opportunities & communities; inclusive AI educational resources (curricula, tools, data sources, assessments, etc.); and educational infrastructure needed to support AI education across institutions; and build upon/contribute to research on broadening participation to better understand and inform efforts to support students from groups historically underrepresented in computing; and research and development needed to make state-of-the-art, inclusive AI education available to all students in preK-12 schools, 2-year colleges, and 4-year colleges and universities.
This DCL does not constitute a new competition or a new program. Rather, interested proposers should consult the program pages for Computer Science for All: Research Practice Partnership (CSforAll:RPP) and Improving Undergraduate STEM Education: Computing in Undergraduate Education (IUSE: CUE) as those programs are revised.
Proposals submitted to either CSforAll or CUE will need to be responsive to requirements of the corresponding solicitation. Principal investigators (PIs) are encouraged to review the CSforAll and CUE programs that develop educational approaches or pathways to support learners' preparation toward skills relevant for AI careers.
The CSforAll:RPP program aims to provide all U.S. preK-12 students with opportunities to participate in rigorous computer science and computational thinking education in their schools through funding both research and research-practitioner partnerships.
The IUSE: CUE program supports evidence-based transformative efforts to modernize computing courses and accelerate student success in the knowledge, skills, and dispositions of industries such as AI, and/or explore effective pathways to computing degrees and careers that involve two-year colleges and industry partnerships.
Both CSforAll:RPP and IUSE: CUE have a focus on broadening participation of groups who are historically underrepresented and underserved by existing computing courses and careers. Proposals in alignment with EducateAI should support the goal of providing opportunities for all students taking computing-related courses to participate in inclusive AI educational activities at the preK-12, 2-year college, or 4-year college levels.
As appropriate, proposals may also address data science skills and practices relevant to AI careers. Proposals may incorporate perspectives of stakeholders such as AI subject matter experts, industry professionals, and educators in formal and or informal settings, including connecting to existing National AI Research Institutes ( https://aiinstitutes. org ) and other NSF-funded AI research projects.
CISE and EDU especially encourage proposals that develop and/or evaluate strategies that prepare teachers to incorporate AI material in computing courses in ways that a) enable students to progress in AI education pathways, b) are responsive to the quickly changing needs of the AI workforce, c) and aim to broaden participation in the AI workforce.
Additionally, CISE and EDU encourage proposals from institutions in EPSCoR jurisdictions, minority-serving institutions, and emerging research institutions. EducateAI supports the goals of the National AI Research Resource pilot initiative , which aims to democratize access to the computational, data and related educational resources needed for AI research and education.
In addition to learning and curricular specific activities, proposers can indicate needs for AI-related computational, data, model or other resources, and associated workforce training to accomplish the project's goals.
EAGER and Conference Proposals In addition, NSF is interested in supporting proposals for capacity-building projects related to the goals of the EducateAI Initiative through EArly-concept Grants for Exploratory Research (EAGER) and conferences and workshops. EAGER proposals seek support for highly innovative, potentially transformative, high-risk/high-reward projects.
These proposals should describe radically different approaches, apply new expertise, or engage novel disciplinary or interdisciplinary perspectives to advance the EducateAI initiative. PIs must contact the NSF program officer(s) listed below whose expertise is most germane to the proposal topic prior to submission of an EAGER proposal. Guidance on preparation of EAGER proposals is contained in Chapter II.
F. 3 of the NSF Proposal and Award Policies and Procedures Guide (PAPPG). Conference or Workshop proposals should address bringing people together consistent with the goals of the EducateAI initiative and aligned with the program from which the proposer is seeking funding.
Proposed activities should be innovative, focused on meeting clearly defined objectives with potentially broad implications for advancing AI education. They should go beyond what would be obtained through existing meetings such as professional societies or state or local education agency efforts. All conference proposals must comply with the guidance outlined in PAPPG Chapter II.
F. 9. For EAGER and Conference proposals, proposals should select the current PAPPG as the funding opportunity and direct proposals to the Education and Workforce (EWF) program in the Division of Computer and Network Systems (CNS) in the Directorate of Computer and Information Science and Engineering (CISE).
Titles of EAGER and Conference proposals submitted in response to this DCL should include "EducateAI". The deadline for submission of EAGER and Conference proposals is March 31, 2024. Principal investigators with questions pertaining to this DCL may contact: Jeffrey Forbes, Program Director, CISE/CNS, jforbes@nsf.
gov Allyson Kennedy, Program Director, CISE/CNS, aykenned@nsf. gov Fengfeng Ke, Program Director, EDU/DRL, fke@nsf. gov Paul Tymann, Program Director, EDU/DUE, ptymann@nsf.
gov Directorate for STEM Education (EDU) Division of Computer and Network Systems (CISE/CNS) Directorate for Computer and Information Science and Engineering (CISE)
Based on current listing details, eligibility includes: Organizations involved in education and research, including preK-12 schools, 2-year colleges, and 4-year colleges and universities. Projects should focus on broadening participation in computing. Applicants should confirm final requirements in the official notice before submission.
Current published award information indicates Not specified (DCL) Always verify allowable costs, matching requirements, and funding caps directly in the sponsor documentation.
The current target date is rolling deadlines or periodic funding windows. Build your timeline backwards from this date to cover registrations, approvals, attachments, and final submission checks.
Federal grant success rates typically range from 10-30%, varying by agency and program. Build a strong proposal with clear objectives, measurable outcomes, and a well-justified budget to improve your chances.
Requirements vary by sponsor, but typically include a project narrative, budget justification, organizational capability statement, and key personnel CVs. Check the official notice for the complete list of required attachments.
Yes — AI tools like Granted can help research funders, draft proposal sections, and check compliance. However, always review and customize AI-generated content to reflect your organization's unique strengths and the specific requirements of the solicitation.
Review timelines vary by funder. Federal agencies typically take 3-6 months from submission to award notification. Foundation grants may be faster, often 1-3 months. Check the program's timeline in the official solicitation for specific dates.
Many federal programs offer multi-year funding or allow competitive renewals. Check the official solicitation for continuation and renewal policies. Non-competing continuation applications are common for multi-year awards.
Past winners and funding trends for this program
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The California Department of Education (CDE) Early Education Division is making approximately .7 million available to expand California State Preschool Program (CSPP) services statewide, appropriated under the 2021 Budget Act. Eligible applicants are local educational agencies (LEAs), including school districts, county offices of education, community college districts, and direct-funded charter schools—both current CSPP contractors and new applicants. Funding supports full-day/full-year or part-day/part-year preschool services for income-eligible children beginning in FY 2024–25. Awards are allocated by county based on Local Planning Council priority areas and application scores, with redistribution provisions if county allocations are underutilized.
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