Stanford: Artificial Intelligence Teaching Guide
We offer this guide to all instructors and teaching teams approaching the topic of generative AI tools in education, whether for the first time or as part of your ongoing engagement with the topic, in response to practical concerns that we heard from instructors like yourself. You don't need to be an expert or have prior experience with generative AI to use this resource, though you should have some understanding of or experience with teaching and learning in higher education contexts. We intend this guide to apply to any disciplinary area or teaching modality and to help you structure the work of integrating AI tools into your teaching practice.
Harvard Business Review: The Uneven Distribution of AI’s Environmental Impacts
The training process for a single AI model, such as an LLM, can consume thousands of megawatt hours of electricity and emit hundreds of tons of carbon. AI model training can also lead to the evaporation of an astonishing amount of freshwater into the atmosphere for data center heat rejection, potentially exacerbating stress on our already limited freshwater resources. These environmental impacts are expected to escalate considerably, and there remains a widening disparity in how different regions and communities are affected.
NEA: Environmental Impact of AI
As you are making decisions about the use of AI in your classroom, it is important to understand the environmental impact of AI's use.
PBS: The growing environmental impact of AI data centers’ energy demands
The EPA has reportedly drafted a plan to eliminate all limits on greenhouse gas emissions from power plants, according to documents obtained by The New York Times. Now, with the rise of artificial intelligence technology, demand on power plants is increasing, in large part due to AI’s reliance on data centers. Ali Rogin speaks with Kenza Bryan, climate reporter for The Financial Times, for more.
UN: AI has an environmental problem. Here’s what the world can do about that.
There are high hopes that artificial intelligence (AI) can help tackle some of the world’s biggest environmental emergencies. Among other things, the technology is already being used to map the destructive dredging of sand and chart emissions of methane, a potent greenhouse gas.
But when it comes to the environment, there is a negative side to the explosion of AI and its associated infrastructure, according to a growing body of research.
Yale: Can We Mitigate AI’s Environmental Impacts?
Associate Professor Yuan Yao is part of a National Science Foundation-led research initiative aimed at reducing the carbon footprint of computing. She recently spoke with YSE News about some of the environmental costs and benefits of AI.
UNESCO: Global Forum on the Ethics of AI 2025
With its unique mandate, UNESCO has led the international effort to ensure that science and technology develop with strong ethical guardrails for decades.
Stanford: Understanding AI Literacy
Here we offer a framework that identifies and organizes skills and knowledge to help you and your students independently and thoughtfully navigate the opportunities and challenges of generative AI.
World Economic Forum: Why AI literacy is now a core competency in education
Artificial intelligence is no longer just a frontier technology - it’s a pervasive force reshaping how we live, work and learn.
As the World Economic Forum’s Future of Jobs Report 2025 outlines, AI is expected to disrupt nearly every industry, shifting the skillsets required across global labour markets.
University of Cambridge: AI and Scholarship: A Manifesto
This manifesto and principles cut through the hype around generative AI to provide a framework that supports scholars and students in figuring out if, rather than how, generative AI contributes to their scholarship, writes Dr Ella McPherson and Prof Matei Candea.