NSF AI Education Act of 2026
- Sponsor
- Sen. Moran, Jerry [R-KS]
- Committees
- Commerce, Science, and Transportation Committee (primary)
- Last action
- Mar 2, 2026
Bottom line
The bill aims to bolster the U.S. AI and quantum workforce pipeline from K-12 through graduate studies and professional development, emphasizing equitable access and national security, while requiring future appropriations for its implementation.
What it actually does
This bill establishes and expands various National Science Foundation (NSF) programs to enhance artificial intelligence (AI) and quantum information science education and workforce development across the United States. It creates scholarships for undergraduate and graduate students, professional development fellowships, grants for land-grant colleges, and awards for AI resources, with a focus on underserved populations and specific sectors like agriculture and manufacturing. The bill also directs the National Institute of Standards and Technology (NIST) to develop workforce frameworks for critical and emerging technologies, including AI, and mandates federal guidance for AI in K-12 education.
Proponents argue
Proponents argue that the bill is crucial for maintaining U.S. leadership in artificial intelligence and quantum information science, addressing critical workforce shortages, and ensuring that diverse populations and regions benefit from advancements in these key emerging technologies. They contend that a comprehensive approach to education and training, coupled with robust research security, is essential for national competitiveness and economic growth.
Opponents contend
While direct opposition to AI education is minimal, potential critics might raise concerns about the overall cost of the authorized programs if fully funded, questioning the efficiency of federal intervention in education markets, or the administrative burden on agencies like NSF and NIST. Some might also argue that the rapid pace of AI development could render specific educational frameworks or programs quickly outdated, or that the bill's focus areas could be too broad or too narrow.
The bill is moderately complex due to numerous definitions and cross-references to existing law, requiring careful reading to understand the full scope of its provisions. An informed reader would need adequate time to fully grasp its implications.
Sec. 3
Undergraduate Scholarships for Artificial Intelligence Education
This section directs the NSF Director to award merit- or need-based scholarships to undergraduate students pursuing studies in AI development, deployment, integration, or application, or quantum hybrid computing. It also creates specific scholarship tracks for AI in agriculture, education, and advanced manufacturing, prioritizing students at rural-located, rural-serving, Tribal Colleges or Universities, and minority-serving institutions for agriculture-related scholarships. Scholarships cover tuition, fees, and a stipend for up to four years.
Supporters argue
Supporters argue these scholarships are vital for building a diverse and skilled AI workforce, addressing talent gaps, and ensuring equitable access to education in critical emerging technologies, particularly in underserved communities and key economic sectors.
Critics contend
Critics, if any, might contend that while scholarships are beneficial, direct federal intervention could distort educational market incentives or that the specific focus areas might not adapt quickly enough to evolving industry needs.
Tradeoffs
The bill balances broad access to AI education with targeted support for specific applications (agriculture, manufacturing) and underserved populations, navigating the tension between general skill development and specialized workforce needs.
Sec. 4
Graduate Scholarships for Artificial Intelligence Education
Similar to the undergraduate program, this section mandates merit- or need-based scholarships for graduate students studying AI development, deployment, integration, application, or quantum hybrid computing. It includes dedicated scholarships for AI in agriculture, education, and advanced manufacturing, with a preference for rural/minority-serving institutions in agriculture. These scholarships cover tuition, fees, and a stipend for up to three years.
Sec. 5
NSF Artificial Intelligence Professional Development Fellowships
This section establishes a program for merit-based fellowships to promote professional development in AI and key emerging technologies. These fellowships are for students, teachers, faculty, and industry professionals to acquire skills in AI development, deployment, prompt engineering, or quantum hybrid computing through programs administered by or affiliated with institutions of higher education. Each fellowship provides an annual award covering tuition, fees, and a stipend.
Sec. 9
Community College and Vocational School Centers of AI Excellence
This section directs the NSF Director, in coordination with the Departments of Commerce and NSF's Regional Innovation Engines program, to designate at least five regionally and geographically diverse Community College and Vocational School Centers of AI Excellence. These centers, formed through partnerships, will focus on AI education and training in areas like agriculture, manufacturing, or AI literacy, with a minimum of 20% located in EPSCoR states. The centers will develop and disseminate best practices for AI research, education, hands-on opportunities, and employment pathways.
Sec. 10
Award Program for Research on AI in Education
This section authorizes the NSF Director to make competitive, merit-reviewed awards to eligible entities (institutions of higher education, nonprofits, or consortia) for research on teaching models, tools, and materials for AI and other key emerging technologies (like quantum information science and photonics). The program specifically focuses on elementary and secondary school students from low-income, rural, or Tribal populations, emphasizing teacher professional development and ethical considerations in AI interactions.
Sec. 17
NIST Workforce Frameworks for Critical and Emerging Technologies
This section amends the National Institute of Standards and Technology (NIST) Act to expand NIST's functions to include developing, maintaining, and providing workforce frameworks for critical and emerging technologies (including a required AI framework within 540 days). These frameworks will offer common taxonomies and lexicons for tasks, knowledge, and skills, aiming to bolster scientific and technical education and workforce development. NIST is required to regularly update these frameworks, include professional skills, support roles, and information promoting diverse career pathways, and consult with various stakeholders.
This section provides extensive definitions for key terms used throughout the Act, including 'artificial intelligence,' 'community college,' 'emerging research institution,' 'EPSCoR institution,' 'foreign country of concern,' 'foreign entity of concern,' 'key emerging technologies,' 'minority-serving institution,' 'quantum hybrid computing,' 'rural-located institution of higher education,' 'rural-serving institution of higher education,' and 'STEM.'
Sec. 2
Why it matters:It is standard legislative practice to define terms upfront to ensure clarity, consistency, and precise application of the bill's provisions, preventing ambiguity in implementation and interpretation.
Case for: Defining terms precisely ensures that the scope and eligibility criteria for all programs and activities authorized by the Act are clear, preventing misinterpretation and facilitating effective implementation.
Case against: The sheer volume and technical nature of the definitions can make the initial reading cumbersome and potentially obscure the immediate operative effects of the bill for a casual reader.
Estimated impact: High, as these definitions govern the entire scope, eligibility, and operational parameters of all programs and restrictions outlined in the Act.
This section explicitly states that all activities authorized under this Act are only required if sufficient funds are either appropriated by Congress or otherwise made available to carry out those respective requirements.
Sec. 15
Why it matters:This is a common legislative practice to clarify that an authorization bill does not automatically provide funding, thereby managing expectations and providing fiscal flexibility for future appropriations decisions.
Case for: It provides Congress with the necessary fiscal flexibility, ensuring that programs are only initiated when the financial resources are secured, preventing unfunded mandates on federal agencies.
Case against: This provision can be seen as a 'poison pill' or a mechanism to authorize ambitious programs without a firm commitment to fund them, potentially leading to delayed implementation or programs that never fully materialize.
Estimated impact: Extremely high, as this section directly determines whether any of the bill's authorized programs, scholarships, or initiatives will actually be implemented and at what scale.
This section mandates that all activities authorized under this Act must be carried out in accordance with existing research security provisions from Subtitle D of Title VI of the Research and Development, Competition, and Innovation Act (42 U.S.C. 19231 et seq.) and Section 223 of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 (42 U.S.C. 6605).
Sec. 16
Why it matters:This is standard legislative practice to incorporate existing legal frameworks by reference, avoiding redundancy and ensuring consistency with established national security protocols.
Case for: It ensures that new AI education and research programs adhere to established national security protocols, protecting against foreign espionage, intellectual property theft, and undue influence in critical technology sectors.
Case against: The reliance on external, complex legal texts makes it difficult for a casual reader or even some stakeholders to fully grasp the stringent security requirements without extensive legal research.
Estimated impact: Significant, as it imposes strict research security and disclosure requirements on all funded activities, potentially affecting international collaborations, data sharing, and the administrative burden on recipient institutions.
This section prohibits the Director from awarding any funds or initiating any programs authorized by this Act to elementary or secondary education institutions, institutions of higher education, or related nonprofit entities if such recipient has been found in violation of Title VI of the Civil Rights Act of 1964 (42 U.S.C. 2000d et seq.) due to discrimination on the basis of shared ancestry or ethnic characteristics on or after January 1, 2020.
Sec. 18
Why it matters:Its placement at the end could indicate it was a late addition or intended as a strong, final statement on a sensitive issue, potentially to address recent concerns regarding discrimination on college campuses.
Case for: Proponents argue this provision ensures that federal funds from this Act do not inadvertently support institutions that have been found to engage in discrimination, thereby upholding civil rights principles and promoting an inclusive educational environment.
Case against: Critics might view this as a politically charged provision that could be used to target specific institutions based on interpretations of discrimination findings, potentially impacting academic freedom or due process, and could be seen as overbroad.
Estimated impact: Potentially significant for any educational institution found in violation, as it would cut off access to all funding and programs authorized by this Act, impacting their ability to participate in crucial AI and quantum education initiatives.