Course: Using Artificial Intelligence (AI 101) · Silver Oak University (fictional sample) · Prof. Quinn
Module: Week 2 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 2 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 2 meeting Tue Sep 9 and Thu Sep 11, and end-of-week work due Sunday Sep 14, 11:59 p.m. Adjust the day-of-week and times to match your section.

(A) Module 2 Overview — Start Here

Welcome to Week 2: Opening the Hood (Conceptually)

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.

Last week you got the mental model: generative AI is software that predicts likely text, not a mind that knows things. This week we open the hood — just enough to understand why these tools behave the way they do. You'll learn what a token and a context window actually are, why a bigger window isn't the same as being "smarter," and — most importantly — why AI can be confidently wrong (a property called hallucination). You'll also see how AI and search engines are completely different tools built for completely different jobs, and you'll encounter the Turing test (Alan Turing, 1950) — one of the most debated ideas in computing history.

AI reminder: Use your approved assistant on the tutorial, practice, discussion, assignment, and Studio. No AI on the quiz — it checks that you understand.

The week's big question

"What is actually happening inside the model — and why does that explain the mistakes it makes?"

By Sunday you'll be able to explain tokens and the context window in plain language, describe why hallucination happens at a conceptual level, distinguish AI from search, and say what the Turing test does and does not prove.

By the end of this week, you can…

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

• [ ] Explain what a token is and roughly how language models process text one token at a time.
• [ ] Define the context window and explain what happens when a conversation exceeds it — and why that does NOT mean the model got "smarter" or "dumber."
• [ ] Explain hallucination in plain language: why LLMs generate confident, specific, sometimes wrong output — and what you should do about it.
• [ ] Distinguish AI from search — what each tool is actually doing and which is right for which job.
• [ ] Describe the Turing test (Alan Turing, 1950, "Computing Machinery and Intelligence") accurately — what it is, what it does and does not prove, and why it remains contested.

What's due this week, and when

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

Heads-up on the AI policy: you must use AI on the tutorial, discussion, assignment, practice, and Studio — that's the learning. AI is not allowed on the quiz, which checks that you understand this week's concepts. And in Studio 2 you'll deliberately catch the AI being confidently wrong — one of the most useful habits in the whole course.

Late policy reminder: 10% off per day late. If something comes up, reach out before the deadline.

How to succeed this week

• Link the mechanism to the behavior. Tokens + training = "predict the next likely piece of text." That's it. Once you see that, hallucination makes sense: the model generates plausible output, not verified output.
• Resist the "bigger = smarter" trap. A bigger context window holds more text — it doesn't make the model more truthful. Separate those two ideas now, before the quiz.
• Search vs. AI: different tools, different jobs. Search finds pages that exist. AI writes new text that may or may not be accurate. Both are useful; they just fail differently.
• Don't over-interpret the Turing test. Passing it means a human can't tell the difference — it's a behavioral test, not a test of consciousness, feelings, or true understanding. Keep that distinction sharp.
• Catch the AI being wrong this week. Studio 2 is designed to make that easy. The habit you're building — verify before trusting — is the course's central discipline.

You should come to Tuesday's class having skimmed at least one reading and ready to argue about whether "hallucination" is a misleading metaphor. See you there.

(B) Welcome Announcement — Module 2

Release setting: post on the module's start day (offset = 7 days), i.e., Tue Sep 9, 2026 — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Tue Sep 9."

Subject: Week 2 — why does the AI confidently make things up?

Hi everyone,

Short warm-up before we start: you've probably seen a chatbot answer a question smoothly, confidently, and wrong. Maybe it invented a statistic, named a book that doesn't exist, or "remembered" something from earlier in the conversation that it actually forgot. Why does that happen? Hold onto your guess — this week's answer will change the way you use these tools for good.

This week — How AI Actually Works (Conceptually) & Its Limits — we go under the hood, just enough to explain the behavior. You'll learn what a token is, what the context window really means, why a bigger window doesn't make the model more truthful, and why AI generates confident answers that aren't always right — a behavior the field calls hallucination. We also compare AI and search engines (different tools, different jobs, different failure modes) and look at the Turing test (Alan Turing, 1950) — still the most famous idea in the debate about what AI "is."

A reminder about this course's (backwards) AI policy:
1. You are required to use AI on the tutorials, discussions, assignments, practice, and the weekly AI Build Studio — this week especially, because you're going to deliberately probe its limits.
2. AI is not allowed on Quiz 2, which checks that you understand the concepts.
3. Studio 2 is "Probe the Limits" — you'll demonstrate a real context-window failure and catch a real hallucination. Start this early; hands-on time matters.

Three things not to miss this week:
1. Lecture Tutorial 2 — work through tokens, context window, hallucination, and the Turing test with one approved assistant. Due Sun Sep 14.
2. Studio 2 ("Probe the Limits") — the most hands-on component this week. 50 points, due Sun Sep 14.
3. Quiz 2 and Discussion 2 also close Sun Sep 14 — check the Start Here page for the full order.

One thing to notice this week: the AI you're using in the tutorial and Studio is itself an example of everything we're studying. Pay attention to what it does well and where it goes wrong. That's the class in miniature.

Bring your laptop and a genuine curiosity about why confident-sounding AI answers sometimes aren't true.

See you Tuesday,
Prof. Quinn