World 1: What Is AI? · Lesson 1.2.2

Step-by-Step Instructions

Everything a teacher needs to deliver this lesson — pick your grade's script below once you've read the background.

Learning Objectives

By the end of this lesson, students should be able to:

Define an algorithm as an ordered set of clear, step-by-step instructions for completing a task or solving a problem — whether a person follows it or a computer runs it.
Explain why computers need extra-precise instructions. A person fills in small gaps with common sense; a computer cannot. It does exactly what it is told, even when that produces a useless or broken result.
Recognize that order matters. The exact same steps, run in a different sequence, can produce a completely different — and often broken — outcome. The lesson's running example is a sandwich-making robot that smears peanut butter on a closed jar because no one told it to open the jar first.
Spot algorithms in everyday, non-digital life — a recipe, walking directions, tying a shoelace, putting numbers in order — as well as in the digital systems students already use: a search engine, a game character's behavior, a social media feed, a traffic light.
(Ages 11 and up) Distinguish "algorithm" from "AI." Every AI system runs on algorithms, but not every algorithm is AI. A traffic light that counts green-yellow-red on a fixed timer is an algorithm with no learning involved — a different thing from a social feed that ranks posts using patterns learned from data.

Teacher Background

You do not need a programming background to teach this lesson well — the core idea is one most adults already use every day without naming it. An algorithm is simply an ordered set of steps for getting something done. A recipe is an algorithm. Directions to a friend's house are an algorithm. The sequence you use to tie a shoelace is an algorithm you learned so long ago that it now runs on autopilot. None of these require a computer — "algorithm" describes the instructions, not the machine that might carry them out.

What makes algorithms worth a whole lesson is what happens when the instructions have to be followed by something with zero common sense: a computer, or in this lesson's central example, a robot being taught to make a sandwich. A person told "put peanut butter on the bread" will automatically open the jar first, because that step is obvious to a human. A computer will not fill in that gap. It executes exactly the steps it was given, in exactly the order it was given them — nothing more, nothing assumed. If "open the jar" is missing from the instructions, the honest result is a robot arm pressing a knife against a closed lid, not an error message saying "did you forget something?" This is the single idea the whole lesson is built to make visible: computers need instructions that are complete (no missing steps), specific (no ambiguous verbs like "handle" or "prepare"), and correctly ordered (the same steps in the wrong sequence produce a different, usually broken, result).

If a student says "well, obviously you'd open the jar first" — that's the teachable moment, not a wrong answer. Their brain filled the gap automatically because they're not a computer. Ask them to imagine explaining "obviously" to something that has never opened a jar and has no idea what a jar even is. That's the gap an algorithm has to close in words.

Once the sandwich example lands, the lesson widens out to show that algorithms are not a niche computer-science topic — they're the invisible machinery behind things students already use daily. A search engine is an algorithm for deciding which of billions of web pages to show first. A game's enemy character follows an algorithm like "if the player is near, chase; if health is low, retreat." A social media feed uses an algorithm to decide which post appears at the top of your screen. A traffic light runs a much simpler algorithm: stay green for 30 seconds, yellow for 5, red for 30, then repeat forever. All four are algorithms. Only some of them are AI.

The algorithm/AI distinction is worth being precise about, especially for older students: every AI system is built out of algorithms, but not every algorithm is AI. The traffic light's fixed count-up is an algorithm with no data, no learning, and no adaptation — the same three numbers forever. A feed-ranking or search-ranking system is also an algorithm, but one that was shaped by patterns learned from huge amounts of data, and that can change its own behavior as new data comes in. That "learns from data and adapts" piece is what later AIQ lessons build toward; this lesson is deliberately about the more basic idea of "instructions a machine can follow," which is the foundation everything else in the course sits on.

For older students, this lesson is also a natural entry point into computer science vocabulary they may encounter later: a program is one way of writing an algorithm down in a language a computer can execute; different algorithms that solve the same problem can be compared by how much time or memory they use as the amount of data grows (this is what "efficiency" means to a computer scientist); and some famous named algorithms — Google's PageRank for ranking search results, or Dijkstra's algorithm for finding the shortest route on a map — are things your Hacker- and Architect-mode students will meet explicitly in their version of this lesson. You do not need to know these in depth to teach the 5–14 age bands; the app's own delivery script carries the technical weight for the 15–18 band.

One thing worth knowing before you teach it: this is lesson 1.2.2, the sixth lesson in World 1 ("What Is AI?"), so students arrive already knowing that computers process input into output and that they store information as 1s and 0s. Algorithms is the natural next step in that sequence — it answers "okay, but how does a computer know what to do with the input?" You don't need to re-teach the earlier lessons, but a one-line callback ("remember, computers take input, do something to it, and produce output — today we're looking at the 'do something' part") helps students place this lesson inside the bigger picture rather than as a standalone topic.

Materials & Prep

This lesson needs almost nothing beyond what students already bring to class:

One device per student or pair (phone, tablet, or computer) with a browser, loaded to AIQ and this lesson (1.2.2, World 1) in the age mode you're teaching.
A whiteboard, chalkboard, or large sheet of paper to write down the "robot instructions" students dictate during the activity — seeing their own words written exactly as spoken makes the missing-step problem obvious.
Optional, not required: actual sandwich-making items (bread, a jar with a lid, a butter knife) for a live volunteer demonstration in Explorer or Builder mode. A mimed version works just as well if you'd rather not bring food into the classroom.

No printing, no accounts, and no setup beyond opening the lesson — the whole activity runs from the hook and learn screens already built into the app.

Common Misconceptions

"An algorithm is a computer thing — it only applies to programming."

An algorithm is just an ordered set of steps for getting something done; a computer program is only one way of writing an algorithm down so a machine can run it. Recipes, walking directions, and the sequence for tying a shoelace are all algorithms, and none of them involve a computer. This lesson deliberately opens with non-digital examples before it ever gets to a robot, precisely so students don't file the word away as "coding stuff I'll learn later."

"Algorithm and AI mean the same thing, so anything with an algorithm is 'AI.'"

Every AI system is built on algorithms, but plenty of algorithms have nothing to do with AI. The lesson's own traffic-light example — green 30 seconds, yellow 5, red 30, repeat — is a complete algorithm that never looks at data and never adapts. What typically makes something "AI" is that its algorithm was shaped by patterns learned from data (and can keep adjusting), rather than being a fixed sequence a person wrote out in full. Older students get this distinction explicitly in their quiz; younger students just need to see that a simple, non-learning "recipe" (the traffic light) and a "smart," learning system (a social feed) are both, underneath, algorithms.

"If the instructions are 'basically right,' a computer will figure out what you meant."

A computer does not infer intent — it executes literally. Skipping "open the jar" doesn't produce an error asking for clarification; it produces a robot pressing a knife against a closed lid, because that is what the literal instructions describe. This is the exact failure the sandwich-robot example is built to demonstrate, and it's worth returning to whenever a student's own "robot instructions" during the activity have a similar gap.

"Being good at following instructions is the same skill as being good at writing them."

Following someone else's clear instructions and writing instructions precise enough for something with no common sense are different skills — the second requires anticipating everything that could go wrong or be misread, before anyone follows a single step. That's the actual thinking task this lesson is training, and it's why the activity has students write the instructions themselves rather than just reading about algorithms.

Pick your grade's script

🌈 Explorer — Ages 5–7 Full delivery script for this age group. 🔧 Builder — Ages 8–10 Full delivery script for this age group. 💻 Hacker — Ages 11–14 Full delivery script for this age group. ⚡ Architect — Ages 15–18 Full delivery script for this age group.

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