Maria J. Molina, Amy McGovern, Jhayron S. Perez-Carrasquilla, Robin L. Tanamachi
{"title":"Using Generative Artificial Intelligence Creatively in the Classroom: Examples and Lessons Learned","authors":"Maria J. Molina, Amy McGovern, Jhayron S. Perez-Carrasquilla, Robin L. Tanamachi","doi":"arxiv-2409.05176","DOIUrl":null,"url":null,"abstract":"Although generative artificial intelligence (AI) is not new, recent\ntechnological breakthroughs have transformed its capabilities across many\ndomains. These changes necessitate new attention from educators and specialized\ntraining within the atmospheric sciences and related fields. Enabling students\nto use generative AI effectively, responsibly, and ethically is critically\nimportant for their academic and professional preparation. Educators can also\nuse generative AI to create engaging classroom activities, such as active\nlearning modules and games, but must be aware of potential pitfalls and biases.\nThere are also ethical implications in using tools that lack transparency, as\nwell as equity concerns for students who lack access to more sophisticated paid\nversions of generative AI tools. This article is written for students and\neducators alike, particularly those who want to learn more about generative AI\nin education, including use cases, ethical concerns, and a brief history of its\nemergence. Sample user prompts are also provided across numerous applications\nin education and the atmospheric and related sciences. While we don't have\nsolutions for some broader ethical concerns surrounding the use of generative\nAI in education, our goal is to start a conversation that could galvanize the\neducation community around shared goals and values.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"59 4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Atmospheric and Oceanic Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.05176","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Although generative artificial intelligence (AI) is not new, recent
technological breakthroughs have transformed its capabilities across many
domains. These changes necessitate new attention from educators and specialized
training within the atmospheric sciences and related fields. Enabling students
to use generative AI effectively, responsibly, and ethically is critically
important for their academic and professional preparation. Educators can also
use generative AI to create engaging classroom activities, such as active
learning modules and games, but must be aware of potential pitfalls and biases.
There are also ethical implications in using tools that lack transparency, as
well as equity concerns for students who lack access to more sophisticated paid
versions of generative AI tools. This article is written for students and
educators alike, particularly those who want to learn more about generative AI
in education, including 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. While we don't have
solutions for some broader ethical concerns surrounding the use of generative
AI in education, our goal is to start a conversation that could galvanize the
education community around shared goals and values.