Education Leadership Faculty Develops Interactive Tool to Measure Carbon Footprint of AI Use in K-12 Education
June 25, 2026
Seth Hunter
Photo by John Boal Photography
Seth B. Hunter, associate professor of Education Leadership and Senior Fellow in the EdPolicyForward center at George Mason University, has developed an interactive tool to help education policymakers estimate the additional amount of energy consumed and carbon emissions produced that are associated with student use of Artificial Intelligence (AI) in K-12 schools. These estimates show energy and emissions increases generated by AI queries that are on top of the existing digital footprint resulting from a student’s daily online activity in school such as Google searches, email use, learning management systems, educational videos, and collaborative documents.
As AI tools like ChatGPT become more commonplace in K-12 classrooms, school district leaders and policymakers will face the challenge of ensuring these AI tools are used in an ethical, responsible, and environmentally sustainable way. Doing so requires school district leaders to consider several key factors. One of these is the complexity of the task for which the AI tool will be used to support learning. For example, if an AI chatbot is to be used primarily to help students with multi-step math problem-solving, a greater level of processing power will be required. In turn, this will result in more energy consumption and higher carbon emissions compared with an AI model that is used to perform simpler tasks such as quick factual lookups, summarizing an article, brainstorming an idea for a paper, or drafting an email to a teacher.
Increases in energy requirements and carbon emissions will also be influenced by the number of students accessing AI and the average number of daily queries they conduct. The type of energy source used in the school district will also have an impact. Renewable energy sources like solar, wind, or hydropower produce less carbon compared to power sources such as coal.
The interactive tool developed by Hunter enables school district policymakers to incorporate these variables into their decisions on selecting AI software that meets the learning needs of their students and promotes environmental sustainability. Hunter explained, “The many conversations I’ve had with leaders across the Commonwealth and nation have focused almost exclusively on the potential benefits of AI, benefits that, I should point out, we don’t yet understand well. Almost no one is talking about the costs beyond the purchase price. This tool is one way to start filling that gap. It’s available at exactly the moment when many leaders are weighing the costs and benefits of scaling AI across students, by estimating a cost that’s seemingly invisible: the additional energy and emissions that come with putting these tools in students’ hands.”
The tool enables users to input data on the total number of students enrolled in a school district, the number of students expected to use AI (based on surveys from peer-reviewed research), the anticipated number of daily queries they are likely to conduct, and the level of AI processing power needed to handle those queries. It also allows users to select their state from a dropdown menu. Next to each state is a number indicating the amount of carbon dioxide in grams released per kilowatt hour of electricity consumed.
Using these inputs, the tool calculates the incremental increases in kilowatt hours, and carbon emissions generated by AI use. To help users better understand the significance of these estimates, the tool translates the data into every day, real-world equivalents—such as the number of miles driven with a car, the number of days a lightbulb could run continuously, or how many times a smartphone could be charged.
In addition, the tool shows what the trajectory of carbon emissions associated with the use of AI in schools will be through 2031 if no policies are put in place to reduce the associated carbon footprint. The trajectory calculations assume that teachers will incorporate AI into their lesson plans at an increased rate of 15 percent per year. The tool also provides a comparison of how the trajectory can be reduced by adopting “efficient policies” related to issues like software procurement and training of school instructional staff in the proper use of AI. “Even leaders with very little technical understanding of AI have many opportunities to influence its energy consumption, and it starts with a simple conversation with the vendor,” Hunter stated. “I’m not talking about choosing one brand over another, like Gemini versus ChatGPT. I'm talking about the type of model. Within a single company’s family of models, the ones built to ‘reason’ or ‘think’ through problems at length currently consume far more energy than lighter models designed for simpler tasks. The most useful question a procurement office can ask is how much a model relies on that extended ‘reasoning,’ and whether a lighter model would meet students’ needs.”
Hunter shared his thoughts on how the features that are part of the tool can help district administrators make better-informed decisions on selecting AI software for use by students and teachers in their schools—especially as it relates to the goal of minimizing any related increases in carbon emissions. “Regional decision-makers are caught between a rock and a hard place. No one wants their students to fall behind in what can feel like an AI arms race. But I argue that policymakers and system leaders should ask how much those advantages outweigh the financial, environmental, and social costs. This tool doesn’t make that decision for anyone, but it estimates one of the costs that usually gets left out, so leaders can weigh student learning and environmental sustainability in the same conversation.”
In other areas, the tool could assist school policymakers in making informed budgeting decisions, a benefit that would be especially important in districts which have experienced significant increases in electricity costs. By determining the amount of additional kilowatt hours of electricity that would be consumed because of AI usage, district administrators will be able to select AI options in support of student learning which are both cost-effective and environmentally sustainable. Hunter observed, “In many districts, you can’t scale up student AI use without board approval because of the line-item cost. When boards consider that cost, I hope they weigh it against the potential benefits, which, again, the rigorous evidence hasn’t yet pinned down, and the broader costs associated with procurement, including energy-related costs. An individual board member juggling a hundred constituent concerns may never get to this. But it would take a board liaison, a procurement officer, or an assistant superintendent about ten minutes with this tool to give leaders a fuller picture of what scaling AI actually costs.”
In his other comments, Hunter explained the reasons why he developed the tool and discussed how it relates to his extensive scholarship focusing on educator and organizational effectiveness and the use of research by policymakers. He stated, “In broad terms, my work has always been about evidence use for policy and leadership decisions, and here was a decision being made all over the country with an entire category of cost absent from the conversation. That gap isn’t unique to AI; we rarely consider costs beyond the budget line, but AI is different. The carbon footprint of some of these tools is substantial, far greater than, say, a digital textbook. And for those of us in Virginia and the broader DMV, the urgency is obvious. Drive down the road, and you’ll see the data centers producing emissions and deleterious health effects landing in our own backyards. Who better than us to lead the nation through these hard decisions?”
Hunter was asked whether an interactive tool like the one he developed could be adapted for use in classrooms as a way for teachers to instill a greater awareness in their students of how their everyday actions, including use of AI, impact the environment and why learning how to use AI responsibly can promote sustainability. “There’s value in helping students see how their everyday choices, including how they use AI, affect the environment. But I built this tool deliberately for the district and state level, because that’s where the scale is. The growth in energy use from scaling AI across a district’s or state’s students dwarfs what you’d see in a single classroom, or even across all of a district’s teachers. When it comes to the carbon footprint, it’s the size of the scale-up that matters most, and that decision sits with policymakers and senior leaders.”
The link to the tool may be accessed at: K-12 AI Carbon Footprint Calculator | Claude