UXD 2015 - GenAI Essentials

• Explain core concepts of generative AI and how it is reshaping work, communication, and organizations
• Design and refine prompts to use generative AI for research, drafting, analysis, design, and prototyping
• Build simple workflows that integrate generative AI into academic or professional tasks and explain how these workflows change ways of working
• Identify and analyze generative AI use cases in their field, including impacts on roles, responsibilities, and collaboration
• Critically evaluate AI outputs for quality, accuracy, relevance, and issues such as bias, exclusion, or misuse of sources
• Apply human-centered principles and ethical use so generative AI supports rather than replaces human judgment and critical thinking
• Explain core concepts of generative AI and how it is reshaping work, communication, and organizations
• Design and refine prompts to use generative AI for research, drafting, analysis, design, and prototyping
• Build simple workflows that integrate generative AI into academic or professional tasks and explain how these workflows change ways of working
• Identify and analyze generative AI use cases in their field, including impacts on roles, responsibilities, and collaboration
• Critically evaluate AI outputs for quality, accuracy, relevance, and issues such as bias, exclusion, or misuse of sources
• Demonstrate socially responsible and ethical use of generative AI by evaluating its societal impacts and relevant governance, policy, and professional norms
• Describe key norms, policies, and governance ideas related to generative AI and apply them when deciding how to use AI
• Collaborate in hands-on activities using generative AI tools and document AI use transparently, including how outputs were verified and revised

• Writing as a process
• Basic digital literacy skills

• Introduction to generative AI (what GenAI is, how it works conceptually, and its core strengths and limitations) 
• Generative AI tools, models, and tokens (overview of major tools and models, their capabilities and limitations, comparing platforms for different tasks, and understanding tokens at a high level) 
• Prompt engineering (designing effective prompts and building a focused knowledge base to   shape and improve AI responses)
• AI agents and task automation (using prompts to set up AI assistants that support multi-step tasks and support everyday workflows) 
• Evaluating GenAI outputs (assessing accuracy, bias, inclusivity, reliability, and fit for purpose)
• Applied AI workflows (using AI to support research, writing, analysis, design, communication, and decision-making) 
• Interdisciplinary applications of AI (common use cases across engineering, UX, business, healthcare, computing, and other fields) 
• Human-AI collaboration (designing collaboration, communication, and review workflows where AI augments creativity, problem-solving, and productivity)
• Societal and ethical implications of generative AI (environmental, privacy, bias, transparency, and workforce considerations)
• AI governance, policy, and professional norms (emerging norms for responsible AI use in education and industry, key governance and regulatory trends, and organizational policies and documentation practices)