Artificial Intelligence Education and Workforce Readiness Act of 2026
- Sponsor
- Rep. Bonamici, Suzanne [D-OR-1]
- Committees
- Ways and Means Committee (primary)
- Last action
- Sep 21, 2026
Bottom line
The bill aims to proactively prepare the U.S. education system and workforce for the widespread integration of AI, emphasizing ethical use, accessibility, and data privacy.
What it actually does
This bill establishes a comprehensive federal framework to address the impact of artificial intelligence (AI) on education and the workforce. It mandates the creation of AI-augmented occupation lists, interagency councils, and various grant programs for AI education, workforce reskilling, and equitable access. It also includes provisions for environmental impact assessment of AI infrastructure, student and worker data privacy, and a new tax credit for AI reskilling.
Proponents argue
Supporters argue that this bill is a crucial, forward-thinking response to the transformative potential of AI, ensuring that the U.S. workforce remains competitive and that educational systems adapt ethically and equitably. They contend it will mitigate job displacement, foster new opportunities, protect vulnerable populations, and address the environmental footprint of AI development.
Opponents contend
Critics might argue that the bill creates an overly complex and bureaucratic federal apparatus, potentially stifling innovation through excessive regulation and compliance burdens on AI developers and educational institutions. They might also contend that the proposed funding levels are insufficient for the scale of the challenge or that the tax credit could be inefficient. Some might also argue that the bill's focus on "human-centered AI" could hinder technological advancement.
The bill is substantial and covers many interconnected policy areas, requiring significant time to read and understand its full implications, which may exceed the time typically allotted for public review.
Section 4
Publication of AI-Augmented Occupations List
Directs the Secretary of Labor, in consultation with the Secretaries of Education and NSF, to publish and annually update a list of occupations significantly affected by AI, including those disrupted or eliminated. This list must disaggregate effects by geographic region and demographic group.
Supporters argue
Supporters argue this list is essential for understanding AI's labor market impact, enabling targeted workforce development and education programs, and ensuring equitable transitions for affected workers.
Critics contend
No specific opposition is typically raised against data collection and publication, though some might argue about the methodology or potential for misinterpretation of the data.
Tradeoffs
Balancing the need for comprehensive, granular data with the administrative burden of collection and the speed at which AI impacts evolve.
Section 5
Establishment of Interagency AI Council
Establishes an Interagency Council, chaired by the Secretary of Education, to coordinate federal AI activities in education and workforce, develop a national strategy, assist in adopting AI risk management frameworks, and facilitate open-access licensing of research and materials.
Section 6
Grant Recipient Compliance Requirements
Mandates that all entities receiving federal funds under this Act must make research and curricula open-access, undergo independent evaluations, comply with a designated AI risk management framework, provide transparency and opt-out options for AI tools affecting students, and implement robust data security. It also prohibits the commercial use of personally identifiable student data.
Section 101, Title I
AI-Disrupted Career Pathways Grant Program
Establishes a competitive grant program by the Secretary of Education for eligible consortia to develop career pathways preparing individuals for AI-augmented occupations. These pathways must include training in humanities, social sciences, AI literacy/governance, and work-based learning, leading to recognized postsecondary credentials.
Section 105, Title I
AI Training for Educators and School Leaders
Amends the Elementary and Secondary Education Act of 1965 to include high-quality, in-service training for teachers, principals, and school leaders on using AI to improve instruction and assessment ethically, ensuring human oversight, communicating AI limitations to families, and integrating humanities/social sciences into AI instruction.
Section 201, Title II
Development of National AI Workforce Strategy
Directs the Secretary of Labor, in consultation with other agencies, to develop and regularly update a National AI Workforce Strategy. This strategy will analyze AI's labor market impacts, identify data gaps, recommend mitigation plans, and set national objectives for workforce participation and reskilling.
Section 204, Title II
AI Apprenticeship and Pre-Apprenticeship Grants
Establishes a grant program by the Secretary of Labor to support apprenticeships and pre-apprenticeships in AI-impacted industries. Funds can be used for developing learning standards, instructional materials on AI ethics/bias/accessibility, providing supportive services (childcare, transportation), and training supervisors on inclusive mentorship and human oversight of AI.
Section 208, Title II
AI Reskilling Tax Credit for Employers
Amends the Internal Revenue Code of 1986 to establish a tax credit for employers equal to 40% of qualified expenses for employee participation in AI reskilling programs. The credit is capped at $250,000 for small businesses and $1,000,000 for others, with an option for qualified small businesses to apply it against payroll taxes.
Section 301, Title III
Equitable AI Education Access Grants
Establishes a grant program by the Secretary of Education for eligible consortia (including LEAs, SEAs, public libraries, museums) to promote equitable access to AI education in K-12 and higher education. Funds can be used for curriculum development, translation into multiple languages, public awareness campaigns, and teacher/librarian training.
Section 302, Title III
AI Capacity-Building Grants for MSIs
Establishes a competitive grant program for Minority-Serving Institutions (MSIs) to develop capacity for AI education and research. Funds can be used for interdisciplinary AI research centers, curriculum/certificate programs in AI/data science/automation, faculty training, incubator programs, and joint research/internship programs with employers.
Section 402, Title IV
Environmental Accountability Workforce Development Grants for AI Infrastructure
Establishes a grant program by the Secretary of Labor to build capacity for oversight and accountability regarding the environmental impacts of AI infrastructure. Eligible entities (state utility commissions, environmental agencies, local governments, nonprofits, higher education) can use funds for training staff on evaluating AI infrastructure impacts on utility rates, electric grid, water, land use, and for community education.
Section 404, Title IV
AI Infrastructure Environmental Disclosure Requirements
Requires AI infrastructure operators with a "Federal nexus" (receiving federal aid or permits) to disclose projected and actual annual energy/water consumption and land use impacts to affected communities. These disclosures must be verified by independent auditors. Federal agencies are also directed to consider energy/water consumption when procuring AI systems.
Section 501, Title V
Student Data Breach Reporting Portal and Requirements
Directs the Secretary of Education to establish a secure portal for entities receiving funds under this Act to report breaches of personally identifiable student data. It mandates specific reporting timelines (72 hours) to the Secretary, affected students/parents, and state educational agencies. The Secretary must also report to Congress on breaches and trends.
Section 504, Title V
Regulation of Automated Decision Systems in Workforce Programs
Limits the exclusive reliance on automated decision systems for "covered decisions" (admission, continuation, completion, placement) in workforce programs funded by the Act. It mandates pre-deployment testing, annual re-testing for efficacy and bias, disclosure to participants, human review/oversight, and training for personnel using such systems.
Defines an "artificial intelligence deployer" as responsible for "ensuring that a human retains oversight and decision-making authority over outputs generated by such model, system, application, or product when used in contexts related to education or workforce." Section 105 further specifies that training for school leaders must ensure "a human retains oversight and decision-making authority over outputs generated by artificial intelligence."
Section 3(12) and Section 105(P)(ii)
Why it matters:This is standard legislative practice to define terms and then apply them through specific program requirements. However, the broad implication of this definition for all AI deployers in education/workforce contexts funded by the Act is significant.
Case for: Supporters argue that embedding human oversight as a core responsibility in definitions and training ensures that AI remains a tool to augment, not replace, human judgment and accountability, especially in sensitive areas like education and employment.
Case against: Critics might argue that such broad requirements for "human oversight" could be vague or difficult to implement consistently across diverse AI applications, potentially slowing innovation or increasing operational costs for deployers.
Estimated impact: Significant, as it establishes a fundamental principle for AI use in education and workforce across all programs funded by the Act, potentially shaping future regulatory approaches.
Prohibits entities receiving funds under this Act from selling, licensing, or using personally identifiable student data (including metadata, behavioral, or biometric data) to train, retrain, or develop any AI model or system for commercial advertising, marketing, or product development unrelated to educational services.
Section 6(b)(2)
Why it matters:This is a common legislative technique to apply broad compliance rules to all funded activities. Its placement ensures it's a condition of *any* funding under the Act.
Case for: Proponents argue this is a vital safeguard against the exploitation of sensitive student data by commercial AI interests, protecting privacy and preventing potential harms from predictive analytics or targeted advertising.
Case against: Some AI developers or educational technology companies might argue that this prohibition is overly restrictive, potentially limiting their ability to improve AI tools through broader data sets (even if anonymized) or to develop new educational products.
Estimated impact: High, as it sets a strict privacy standard for student data across all federally funded AI education and workforce programs.
Explicitly states that an artificial intelligence developer, integrator, deployer, or infrastructure operator "may not receive, or serve as a partner or subgrantee under," a grant awarded under Section 402 (Workforce Development for Environmental Accountability).
Section 402(c)
Why it matters:This is a deliberate policy choice to prevent potential conflicts of interest, ensuring that the entities responsible for environmental oversight and accountability are independent of the AI industry they are meant to regulate.
Case for: Supporters argue this exclusion is crucial for maintaining the integrity and impartiality of environmental oversight programs, preventing industry influence over accountability efforts.
Case against: Critics might argue that excluding industry players from these grants could limit access to valuable technical expertise and practical insights that could inform more effective environmental accountability strategies.
Estimated impact: Moderate, as it ensures the independence of environmental accountability initiatives, potentially affecting the composition and approach of grant-funded projects.