Course: Using Artificial Intelligence (AI 101) · Silver Oak University (fictional sample) · Prof. Quinn
Module: Week 1 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objective covered: Objective 1 — Explain what generative AI and large language models are, how they work conceptually, and their core capabilities and limits.

This file holds two pieces: (A) the Module 1 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. Dates below assume a Tuesday/Thursday lecture pattern with Week 1 meeting Tue Sep 1 and Thu Sep 3, and end-of-week work due Sunday Sep 6, 11:59 p.m. Adjust the day-of-week and times to match your section.

(A) Module 1 Overview — Start Here

Welcome to Week 1: The Right Mindset for the AI Age

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module. Bring your laptop to class — we use these tools live.

Before you write a single prompt, you need the right mental model of what these tools are. Most people's frustration with AI comes from a wrong one: they treat a chatbot like a search engine, expect one perfect answer, and conclude "AI is dumb" when it isn't. This week we replace that with a working picture — what generative AI actually is, the words to describe it (AI vs. generative AI vs. LLM vs. AGI), and the two habits that separate people who get great results from people who give up: start general and get specific, and treat it as an iterative conversation, not a vending machine. And one idea we'll repeat all term: the machine has no brain — so you bring yours.

The week's big question

"What is this thing I'm talking to — and how do I have to think to get the most out of it?"

By Friday you'll be able to define generative AI in plain language, use the key terms correctly, set up and run your first real AI conversation, and explain the mindset that makes AI useful instead of frustrating.

By the end of this week, you can…

Use this as a checklist. If you can do all four out loud, you're ready for the quiz.

• [ ] Define generative AI and correctly distinguish AI, generative AI, LLM ("the model"), and AGI.
• [ ] Adopt the working mindset — start general → specific, iterate, and remember the machine predicts text rather than "thinking."
• [ ] Run a real first conversation — set up an approved assistant and get genuinely useful output by refining your prompt.
• [ ] Explain the "what if…" paradigm — why AI lowers the cost of trying ideas, and why you stay the judge of what comes back.

What's due this week, and when

Work these in order — each one gets you ready for the next.

Heads-up on how AI works in this course (it's backwards from most classes): you are required to use AI on the tutorial, discussion, assignment, practice, and Studio — that's the whole point. AI is not allowed on the quizzes, midterm, or final, which check that you understand. And every week you'll deliberately catch the AI's mistakes — starting in this week's Studio.

Late policy reminder: 10% off per day late. If life happens, reach out before the deadline — I'd much rather hear from you early.

How to succeed this week

• Lead with the idea, not the jargon. Every term this week is a plain-English idea first (an LLM is just the prediction engine behind the chatbot; AGI is the hypothetical "can-do-anything" AI that doesn't exist yet). The vocabulary comes after the idea clicks.
• Memorize two tiny hooks. "The machine has no brain — use your own." And "Start general, then get specific."
• Actually run a conversation. You can't learn to ride a bike by reading about it. Open an assistant tonight and ask it to help with something real from your own life or major — then refine your request twice.
• Expect to be wrong about what it is. Most people start thinking AI is either a magic genius or a useless toy. It's neither: it's a very capable, very confident text-prediction tool that needs a skilled human director. That's you.
• Treat the chatbot as a smart intern, not an oracle. It drafts; you check. That habit is the whole semester in miniature.

You don't need any background for this week — no coding, no math, no prior AI experience. Just curiosity and a willingness to question your assumptions about what this technology is. Come to class ready to argue about whether AI "understands" you. See you Tuesday.

(B) Welcome Announcement — Module 1

Release setting: post on the module's start day (offset = 0 days), i.e., Tue Sep 1, 2026 — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Tue Sep 1."

Subject: Welcome to AI 101 — does this thing actually understand you? 🤖

Hi everyone, and welcome to Using Artificial Intelligence!

Quick warm-up before we start: when a chatbot gives you a smooth, confident answer, is it thinking? Does it understand your question — or is it doing something else entirely that just looks like understanding? Hold onto your gut answer. By Friday you'll have a much sharper one, and it will change how you use these tools for good.

This week — Welcome to the AI Revolution — we tackle the big question: What is this thing I'm talking to, and how do I have to think to get the most out of it? We'll define generative AI in plain language, get the vocabulary straight (AI vs. generative AI vs. LLM vs. AGI), set up your tools, and run your first real AI conversation — the kind that actually saves you time.

A heads-up about this course — the AI policy is backwards from most classes:
1. You are required to use AI on the tutorials, discussions, assignments, practice, and the weekly AI Build Studio — using AI well is literally the subject. But AI is not allowed on the quizzes, midterm, and final, which confirm that you understand.
2. Every week we catch the AI's mistakes. These tools are confidently wrong often enough that verifying them is a core skill. Your first catch is in Studio 1.
3. Bring your laptop to every class — we build and test things live.

Three things not to miss this week:
1. Lecture Tutorial 1 — work through the mindset with one approved assistant (ChatGPT, Claude, Gemini, or Copilot) and submit the share link. Due Sun Sep 6.
2. Studio 1, Quiz 1, Discussion 1, and Assignment 1 also close Sun Sep 6 — start the Studio early; it's hands-on.
3. Open the Start Here page first — it lays out everything in order with due dates.

One promise: this is a course about doing, not memorizing definitions of "neural network." By the end of the term you'll have built real prompts, real automations, and a real capstone — and you'll know exactly when to trust AI and when to double-check it.

Bring your laptop and a strong opinion about whether AI understands you to class on Tuesday.

See you soon,
Prof. Quinn