Day 35: Intro to AI (BrainPOP)
Friday, May 8th, 2026
Objectives
- I can define artificial intelligence and machine learning in my own words.
- I can explain at least one real-world example of AI being used today.
- I can describe one risk or limitation of AI, such as bias or errors in training data.
- I can complete a graded BrainPOP quiz and reading activity using the vocabulary I built.
Warmup: Vocab Builder First
Today’s entire period is in BrainPOP. Before you watch anything, start with the Vocab Builder. This is not optional — it takes about five minutes and it makes the movie dramatically easier to follow. If you watch the movie without knowing what algorithm, training data, or neural network mean, those words will fly past you.
The Artificial Intelligence topic has been assigned to you.
- Open the Vocab Builder activity.
- Work through every term. Read the definition, look at the example, and answer the practice question before moving on.
- Move on to the rest of the activities only after you have completed the entire Vocab Builder.
Work Session: BrainPOP AI Lesson
Work through the remaining three activities.
Activity 2: Movie with Pause Points
Activity 3: Quiz (Graded Mode)
Activity 4: Connected Texts (Graded Mode)
Key Vocabulary
Keep these definitions handy. We will use them again next week when we write our first machine learning code.
- Artificial Intelligence (AI)
- Software designed to do tasks that normally require human thinking — like recognizing faces, understanding speech, or recommending videos.
- Machine Learning
- A way of building AI where the computer learns from examples instead of following rules a programmer wrote line by line.
- Training Data
- The collection of examples used to teach a machine learning model. The quality and fairness of the training data directly affects how well — and how fairly — the model performs.
- Bias (in AI)
- When an AI system produces results that are systematically unfair, often because the training data over-represented or under-represented certain groups.
If You Finish All Four Activities Early
If you completed Vocab Builder, Movie, Quiz, and Connected Texts and still have time, open code.org through Clever and explore any course or lesson you want. There is no specific assignment — use the time to try something you are curious about.
Closing
Think about what you learned today. You can answer these questions out loud with a neighbor, in your notebook, or on a scrap of paper — your choice.
- In your own words, what is the difference between regular programming and machine learning? (Hint: think about who writes the rules.)
- Name one way AI is already being used in something you interact with every day. How do you know it is AI and not just a regular program?
- What is one question you still have about AI — something you hope we answer next week?
Next week we will move from the concept to the code: you will build and run basic machine learning examples in class using real data. Today gave you the vocabulary you will need to understand what the code is actually doing.
Standards
- MS-CS-FCP.3.2 — Develop a working vocabulary of computational thinking including data, data collection, data analysis, and automation — today students build AI-specific vocabulary (algorithm, training data, neural network, bias) that extends directly from the data and automation concepts covered all week.
- MS-CS-FCP.3.3 — Analyze the problem-solving process and how computers help humans solve problems — the BrainPOP movie explicitly addresses how AI systems use data and training to solve problems that traditional programming cannot.
- MS-CS-FCP.3.4 — Develop an algorithm to decompose a problem of a daily task — students articulate, in the closing reflection, how machine learning decomposes recognition and decision problems differently from a programmer-written algorithm.
- MS-CS-FCP.6.1 — Summarize ethical, privacy, and legal issues of a digital world using current case studies — the movie and Connected Texts cover AI bias, responsible use, and the social implications of automated decision-making.