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As artificial intelligence (AI) applications like ChatGPT become more commonplace in K-12 classrooms, school district leaders and policymakers will face the challenge of ensuring these tools are used in an ethical, responsible, and environmentally sustainable way. Seth B. Hunter, associate professor of education leadership and senior fellow in the EdPolicyForward center at 911����, has developed an to help. 

The tool enables school district policymakers to make better informed decisions on selecting AI software that meets the learning needs of their students and promotes environmental sustainability.  

“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,” said Hunter. “Almost no one is talking about the costs beyond the purchase price. This tool is one way to start filling that gap—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 increases in kilowatt hours and the 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. 

“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.” 

School district leaders will need to consider several factors, such as the complexity of the task the AI tool will be used to support. For example, if an AI chatbot is used to help students with multi-step math problem-solving, it will use more energy than simpler tasks such as quick factual lookups or summarizing an article. 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 like coal. 

“No one wants their students to fall behind in what can feel like an AI arms race,” said Hunter. “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.”