FAQ

Generative AI (GenAI) is a subset of artificial intelligence that can create new content, such as text, images, or other forms of media, by learning patterns from existing data.

GenAI uses advanced machine learning models, often neural networks, to recognize patterns within large datasets. These patterns enable the AI to generate new, original outputs based on prompts or inputs provided by the user. Where can I find training and other useful AI materials?

Yes, GenAI can be used in academic work and research; however, it's essential to follow university guidelines regarding academic integrity and ethical usage. Always verify acceptable use with your instructor or advisor.

To start using GenAI, first take advantage of the new AI services such as Gemini or Notebook LM, which will be available to all Weber State students, faculty, and staff starting May 5th. These tools offer powerful capabilities to enhance your learning, research, or workflow. Additionally, our AI Training & Resources page provides comprehensive introductory guides, step-by-step tutorials, and helpful videos specifically designed for new users. We also recommend participating in our interactive workshops and webinars, which offer hands-on experiences to help you become proficient with GenAI quickly and confidently. If you're unsure where to start, the resources provided are structured to accommodate varying levels of experience, ensuring you can comfortably and effectively integrate AI into your academic or professional activities.

Prompt engineering involves carefully crafting instructions or queries (known as prompts) designed to clearly communicate the desired outcome to an AI model. Effective prompts are structured to guide AI systems in generating accurate, relevant, and contextually appropriate responses. This process often requires experimentation, refinement, and a solid understanding of the AI’s capabilities and limitations to optimize results. Skilled prompt engineering enhances the reliability and utility of AI-generated content by precisely specifying the context, format, tone, and details needed to produce meaningful and targeted outputs.

Generative AI models, while powerful, have several important limitations. They can sometimes produce inaccurate or outdated information, particularly if the training data they rely on is incomplete, biased, or incorrect. GenAI lacks a genuine understanding of context, nuance, and common sense that humans naturally possess, often resulting in outputs that may be plausible yet incorrect or inappropriate. Additionally, these models might unintentionally reinforce biases present in their training data, leading to outputs that perpetuate stereotypes or misinformation. Users must approach AI-generated content with a critical mindset, always verifying its accuracy, relevance, and appropriateness for the intended purpose.

Generative AI raises several ethical concerns that affect users and communities. One major issue is bias, where AI systems might produce results that reflect unfair or discriminatory attitudes because they're trained on datasets containing biased information. Privacy is another concern, as AI often requires large amounts of personal data, making it important to address issues around data protection, user consent, and secure data handling. Additionally, GenAI can inadvertently generate misleading or false information, posing a challenge to users who rely on accurate and trustworthy sources. Issues related to intellectual property can arise if AI-generated content closely matches existing copyrighted materials, leading to legal questions about ownership and originality. 

Microsoft Copilot has specific compliance certifications and security measures in place when logged in using WSU credentials; however, users should always confirm its suitability for handling sensitive or protected information under FERPA regulations with their department or IT support.