Learn AI at your own pace

Each lesson answers three questions: what is it, why does it matter to me, and how do I use it. Follow a path, or pick any lesson on its own.

Learning paths

• Student starter (New to AI): Eight guided lessons from “what is this?” to using AI confidently and responsibly in your coursework.
• Faculty path (Teaching and research staff): Fundamentals, then teaching, assessment and research, with integrity throughout.
• Research path (Grad students and researchers): Literature, data and grounded answers, verified at every step.
• Builder path (Want to build with AI): Code, agents and automation, for people who want to make things.

All lessons

• What generative AI actually is: The mental model that makes everything else make sense.
• How large language models work: Tokens, transformers, training and why context windows matter.
• Reasoning models: Why some models “think” before answering, and when that's worth the wait.
• Images, video and voice: What AI can see, hear and generate beyond text.
• AI agents: Models that take actions: browsing, running code and using your apps.
• RAG: AI over your own documents: How tools answer from your files instead of from memory.
• What AI gets wrong: Hallucinations, bias, sycophancy, and how to catch them.
• Your first week with an AI assistant: ChatGPT, Claude and Gemini side by side, and how to pick.
• Prompting that works: Context, examples, constraints and iteration.
• Research and verification: Use AI to find and read faster without losing rigor.
• Writing with AI, not by AI: Brainstorm, outline, get feedback and edit, and keep your voice.
• Analyzing data with AI: Upload a spreadsheet, ask questions, check the code.
• Coding with AI: From autocomplete to agents, and how to stay the engineer.
• Presentations: Get to a first draft fast, then make it yours.
• Automating routine work: Connect apps and add AI steps to workflows you repeat every week.
• AI for your major: How professionals in your field are using AI right now.
• Teaching with AI: Course design, materials and feedback, for faculty and TAs.
• Assessment in the AI era: Designing assignments where AI use is clear, fair and visible.
• Ethics and academic integrity: Privacy, copyright, disclosure and USC policy.