Working in an academic environment means using the tools available to you responsibly, and generative AI is no different. Beyond the formal rules, a few principles will help you use it in a way that supports your learning rather than undermining it. 

Use AI to support essential academic skills, not to stand in for them. The same goes for foundational knowledge in your field. If you don't understand the basics of your topic, you have no way to judge whether an AI's output is reliable or even true. The goal is to come out of your degree with the skills, not just the deliverables. 

Every tool has limitations, and they differ from one to the next. Before you rely on a tool, ask: what are its weaknesses, do they matter for what I'm doing, and can I work around them? Some tasks are a poor fit by design. For example, don't ask a language model to search for literature or solve mathematical problems, and don't use a tool at all if you lack the knowledge to review its output critically!

Always evaluate the correctness of what a tool produces, and compare it against other sources. Check where the information comes from: an answer built on company marketing or a low-quality journal may not be trustworthy. Remember, too, that a tool's own training data carries biases, which can shape its output in ways worth questioning. 

Generative AI takes vast computing power to train and run, consuming large amounts of energy and water and emitting significant CO₂. When a less energy-intensive alternative exists or when you don't really need a tool at all consider going without.