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AI’s Environmental Footprint Awareness Tool for Educators

As artificial intelligence tools like ChatGPT, Google Gemini, and Microsoft Copilot become fixtures in K–12 classrooms, school and district leaders face a new and largely invisible policy question: what is the environmental cost of student AI use, and what can districts do about it? This interactive calculator — developed by Dr. Seth B. Hunter at EdPolicyForward: The Center for Education Policy at George Mason University — is designed for education policymakers, district administrators, curriculum directors, and sustainability officers who want to move beyond awareness and into action. 

With just a few clicks, users can easily use the tool to reflect their own district’s enrollment, state energy grid, AI adoption rate, and model complexity, then explore how much additional energy and carbon emissions student AI use adds on top of existing digital infrastructure. 

The tool also projects emissions trajectories through 2031 under business-as-usual versus policy-intervention scenarios, grounding those scenarios in peer-reviewed research and federal data. Potential uses include briefing school boards on AI’s environmental footprint, informing AI procurement and vendor contract language, supporting district sustainability planning, and anchoring professional development conversations about responsible AI use in schools.

Try the tool here – https://claude.ai/public/artifacts/31caa0e2-d1d1-4225-b6bf-2e9ff52fe675.

The U.S. Department of Labor Has Released an AI Literacy Framework — Here’s What You Need to Know

The U.S. Department of Labor (DOL) has released its official Artificial Intelligence (AI) Literacy Framework, a landmark resource designed to help workers, educators, employers, and workforce agencies prepare for an AI-driven economy. Issued in February 2026 through Training and Employment Notice No. 07-25, this framework provides a common foundation for building AI skills across the American workforce — from classrooms to shop floors to offices.

What Is AI Literacy?

The DOL defines AI literacy as a foundational set of competencies that enable individuals to use and evaluate AI technologies responsibly. The framework places particular emphasis on generative AI — tools like chatbots and content generators — which are increasingly central to the modern workplace. AI literacy is not about becoming a programmer or data scientist. It is about every worker having the baseline knowledge to confidently and responsibly work alongside AI tools.

Who Is This Framework For?

The framework is designed for a wide range of audiences:

  • Workers — to understand how AI affects their careers and build skills proactively
  • Employers — to upskill teams and integrate AI tools into daily operations
  • Education & Training Providers — to design AI literacy curricula for their programs
  • State & Local Agencies — to align workforce development programs with AI readiness goals

The 5 Foundational Content Areas

The framework identifies five core knowledge areas every AI-literate worker should develop:

1. Understand AI Principles Workers need a basic understanding of how AI systems work — including how they generate outputs, their limitations (such as “hallucinations” or incorrect responses), and the importance of human oversight. This is about building vocabulary and mental models, not technical expertise.

2. Explore AI Uses Workers should be exposed to real-world applications of AI across industries — from drafting documents and summarizing reports to decision-support tools and creative assistance. Familiarity builds confidence and judgment.

3. Direct AI Effectively Effective AI use requires knowing how to write clear prompts, provide context, supply relevant information, and iterate on outputs. This is often called “prompt engineering” and is a practical, learnable skill.

4. Evaluate AI Outputs AI does not always get it right. Workers must be able to verify accuracy, spot logical errors, assess completeness, and apply their own expertise and judgment before acting on AI-generated content.

5. Use AI Responsibly Workers need to understand how to protect sensitive data, follow workplace policies, avoid misuse, and maintain accountability for the decisions and outputs they produce with AI tools.

The 7 Delivery Principles

The DOL does not just tell us what to teach — it also offers guidance on how to teach it effectively:

  1. Enable Experiential Learning — Hands-on practice with real tasks is more effective than reading about AI in theory
  2. Embed Learning in Context — Training is most impactful when tied to workers’ actual jobs and industries
  3. Build Complementary Human Skills — AI amplifies human strengths like critical thinking, creativity, and communication
  4. Address Prerequisites to AI Literacy — Programs must account for digital literacy gaps and unequal access to devices and broadband
  5. Create Pathways for Continued Learning — Foundational literacy is just the beginning; clear progression routes should be built in
  6. Prepare Enabling Roles — Managers, trainers, and counselors need their own tailored AI literacy to support learners effectively
  7. Design for Agility — Because AI evolves rapidly, training programs must be built to update and adapt continuously

Why This Matters for Education

This framework is directly relevant to schools, colleges, and training programs. It complements the U.S. Department of Education’s own guidance on AI, and builds on Executive Order 14277 on advancing AI education for American youth. For educators, it offers a clear, flexible structure for integrating AI literacy into existing curricula — not as a standalone subject, but woven into the learning experiences students already have.

The message is clear: AI literacy is no longer optional. Whether a student is heading into healthcare, manufacturing, business, or the trades, foundational AI skills will be essential to their success.

Read the Full Framework

You can access the complete DOL AI Literacy Framework here: 👉 U.S. Department of Labor AI Literacy Framework (PDF)

Podcast: Navigating AI’s risks and rewards, Dr. Amarda Shehu

Artificial intelligence dominated conversations across the globe, from copyright lawsuits against AI art generators to developing legislation for artificial intelligence regulation. As we enter this new frontier of technological advancement and intelligence, George Mason is positioning itself to be a pioneer in the field.   

On this episode of Access to Excellence, President Gregory Washington and George Mason’s inaugural vice president and chief AI officer Amarda Shehu discuss the research possibilities of AI and the role of higher education in AI training and development.

Podcast: Durable Skills for 21st Century Learning, Dr. Padhu Seshaiyer

In today’s rapidly evolving world, the skills required for success are constantly shifting. The 21st century demands a new set of durable skills — critical thinking, communication, collaboration, creativity, grit, and curiosity. Padhu Seshaiyer, Professor and Director at George Mason University, discusses the challenges of teaching in the 21st century and the ways he is responding to this challenge in his work with educators.

Podcast: Using Data and AI to Transform Education, Dr. Seth Hunter

What does it really mean to make evidence-based decisions in education? In this episode, First State Education’s Executive Director Dr. Julia Keller sits down with Dr. Seth Hunter, Associate Professor at George Mason University and nationally recognized researcher in education policy and leadership. Drawing on his experience across classrooms, state agencies, and research institutions, Dr. Hunter discusses the challenges school boards face in using data well, the power and risk of story-driven decision-making, and how thoughtful use of evidence, pilots, and emerging technologies like AI can support better outcomes for students.