About Generative AI and our Literacy Initiative

Welcome to the Generative AI Literacy Initiative, a program led by the Texas A&M Institute of Data Science. This initiative is designed to empower students, educators, researchers, and the broader Texas A&M community with the knowledge and tools needed to understand and responsibly engage with generative artificial intelligence (GenAI).

GenAI—What is Generative Artificial Intelligence?

Generative AI refers to a class of artificial intelligence capable of creating new content. Unlike traditional AI that might analyze data or make predictions, GenAI uses machine learning models that can generate original text, images, music, code, and more, often in response to a simple prompt. These models, like ChatGPT or DALL·E, are trained on vast datasets and can accomplish impressive things.

Why GenAI Literacy Matters

These technologies are rapidly transforming industries, education, and the everyday life of Aggies. As GenAI tools become more powerful and their influence grows, so does the need for accessible, accurate, and ethical AI literacy. All students, staff, and faculty need to be able to think critically about AI-generated content and participate in information conversations about its use, recognize biases and limitations in AI systems, and use AI tools ethically in academic, professional, and creative settings.

Get Started with GenAI

Our Initiative bridges the knowledge gap by offering educational and research resources, hands-on workshops, and collaborative opportunities that demystify GenAI. Whether you’re just beginning to explore AI or you’re looking to deepen your understanding, this site offers:

Clear explanations of generative AI concepts

Practical guides and tools for learning and teaching

Events and opportunities to engage with the AI community

Insights into the ethical and societal implications of AI


We invite you to explore, learn, and join us in shaping the future of AI literacy.

Mission & Leadership
Opportunities
Consultancy Hub
Learning Resources