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Generative AI is a branch of artificial intelligence that enables machines to create new content — such as text, images, audio, or code — based on patterns learned from existing data. Unlike traditional AI systems that primarily classify or predict based on input data, generative AI models can produce original outputs that resemble the data they were trained on.
As these tools become more common in academic, professional, and creative contexts, UW-Whitewater is committed to guiding their responsible and ethical use. These guidelines, informed by campus-wide input and best practices, are designed to promote transparency, integrity, inclusivity, and respect for privacy in all AI-supported work.
In what follows, we use the terms “Generative AI”, “Gen AI” and “AI” interchangeably to refer to the generative AI branch of artificial intelligence, while recognizing that AI contains several other areas.
Understanding how generative AI works helps our community engage more critically with its strengths and limitations in academic, creative, and professional contexts.
Training a generative AI model involves several key steps:
Training and refining these models requires substantial computational resources and human expertise to ensure ethical, effective performance.
For a visual explanation of how Large Language Models (LLMs) work, you might find the following video helpful:
Source: 3blue1brown.com
If you're interested in exploring generative AI tools or have specific questions about their applications, feel free to reach out to CATLST.
Refer to Institutional Research Assessment and Planning and UW System Administrative Policy 1031 to understand more about institutional data risk levels, specific examples of data classification (SYS 1031 Guidance: Data Classification Examples) and refer to Allowable or Prohibitive Uses uses for more details.