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

2024-09-08

arXiv.org

AI Summary

This study addresses ethical risks, educational inequities, and environmental impacts arising from generative AI adoption in atmospheric science education. Methodologically, it pioneers an integrated framework combining discipline-specific prompt engineering, bias detection mechanisms, equity-of-access analysis, and pedagogically grounded scenario adaptation—implemented across leading LLMs (e.g., GPT, Claude). Key contributions include: (1) a reusable, multi-scenario prompt library; (2) an AI ethics self-assessment checklist for educators; and (3) a practitioner-oriented teacher training guide to support responsible AI integration. Empirically, the work fills a critical gap in evidence-based AI applications within atmospheric science education. Moreover, it catalyzes cross-institutional dialogue on the pedagogical value and ethical boundaries of AI in teaching, establishing a transferable methodology for AI-infused STEM education.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Enabling ethical and effective student use of generative AI
• Addressing educator challenges with AI in classroom activities
• Mitigating ethical and equity concerns in AI education tools

Innovation

• Generative AI enhances classroom engagement creatively
• Ethical AI use training for students emphasized
• Addressing equity in access to AI tools