Using Generative Artificial Intelligence Creatively in the Classroom and Research: Examples and Lessons Learned

Although generative artificial intelligence (AI) is not new, recent technological break-throughs have transformed its capabilities across many domains. These changes necessitate new attention from educators and specialized training within the atmospheric and related sciences. Enabling students to use generative AI effectively, responsibly, and ethically is crucial for their academic and professional development. Educators can also use generative AI to develop engaging classroom activities, such as active learning modules and games; however, they must be aware of potential pitfalls and biases. There are also ethical implications in using tools that lack transparency and have a considerable carbon footprint, as well as equity concerns for students who lack access to more sophisticated paid versions of generative AI tools and who have deficiencies in prior educational training. This article is written for students and educators alike, particularly those interested in learning more about generative AI in education and research, including its use cases, ethical concerns, and a brief history of its emergence. Sample user prompts are also provided across numerous applications in education and the atmospheric and related sciences. Current solutions addressing broader ethical concerns regarding the use of generative AI in education remain limited; however, this work aims to foster a discussion that could galvanize the education community around shared goals and values. SIGNIFICANCE STATEMENT: Recent technological advances have transformed intelligent machines into much more useful tools across a wide range of applications, leading to rapid career changes. However, few educators and students are familiar with the history of these technologies, nor do they have concrete examples of how to apply them in the atmospheric and related sci-ences. We aim to fill this gap by providing a brief history of their emergence and examples of the uses of intelligent machines, including lesson planning, literacy training, concept understanding, and software development. Examples of subtle errors produced by intelligent machines are also included, illustrating that domain expertise is necessary when using these technologies, given the far-reaching societal impacts of the atmospheric and related sciences.