Introduced 6-8 · Unit 1
act
To act is to execute an output — show a label, unlock a door, call a tool, send a message. Smart systems close the loop: sense, reason, act.
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Age-specific explanations
K-2To act is to execute an output — show a label, unlock a door, call a tool, send a message. Smart systems close the loop: sense, reason, act.
6-8To act is to execute an output — show a label, unlock a door, call a tool, send a message. Smart systems close the loop: sense, reason, act.
9-12To act is to execute an output — show a label, unlock a door, call a tool, send a message. Smart systems close the loop: sense, reason, act.
Introduced 6-8 · Unit 1
AND
AND means both conditions must be true for the result to be true. Valid badge AND door closed — both required to unlock. If either is false, the output is false.
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Age-specific explanations
6-8AND means both conditions must be true for the result to be true. Valid badge AND door closed — both required to unlock. If either is false, the output is false.
9-12AND means both conditions must be true for the result to be true. Valid badge AND door closed — both required to unlock. If either is false, the output is false.
Introduced 6-8 · Unit 1
Boolean operator
Boolean operators — AND, OR, NOT — combine true and false values to make a decision. AND needs both true. OR needs at least one. NOT flips the value. They appear in hardware gates and in code.
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Age-specific explanations
6-8Boolean operators — AND, OR, NOT — combine true and false values to make a decision. AND needs both true. OR needs at least one. NOT flips the value. They appear in hardware gates and in code.
9-12Boolean operators — AND, OR, NOT — combine true and false values to make a decision. AND needs both true. OR needs at least one. NOT flips the value. They appear in hardware gates and in code.
Introduced 6-8 · Unit 1
chip
A chip is a small piece of silicon packed with transistors and circuits. Your phone, laptop, and the servers running large AI models all depend on chips doing math with switches.
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Age-specific explanations
6-8A chip is a small piece of silicon packed with transistors and circuits. Your phone, laptop, and the servers running large AI models all depend on chips doing math with switches.
9-12A chip is a small piece of silicon packed with transistors and circuits. Your phone, laptop, and the servers running large AI models all depend on chips doing math with switches.
Introduced 6-8 · Unit 1
hardware
Hardware is the physical parts — chips, memory, screens, sensors. If you can touch it or it draws power, it is hardware.
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Age-specific explanations
6-8Hardware is the physical parts — chips, memory, screens, sensors. If you can touch it or it draws power, it is hardware.
9-12Hardware is the physical parts — chips, memory, screens, sensors. If you can touch it or it draws power, it is hardware.
Introduced 6-8 · Unit 1
inclusive OR
Inclusive OR is true when either input is true, including when both are true. Do not treat OR as exclusive — both-on is still a yes.
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Age-specific explanations
6-8Inclusive OR is true when either input is true, including when both are true. Do not treat OR as exclusive — both-on is still a yes.
9-12Inclusive OR is true when either input is true, including when both are true. Do not treat OR as exclusive — both-on is still a yes.
Introduced 6-8 · Unit 1
logic gate
A logic gate is a small circuit that combines on-off signals with a rule — like AND, OR, or NOT. Gates are how hardware implements the same true-false decisions you write in code.
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Age-specific explanations
6-8A logic gate is a small circuit that combines on-off signals with a rule — like AND, OR, or NOT. Gates are how hardware implements the same true-false decisions you write in code.
9-12A logic gate is a small circuit that combines on-off signals with a rule — like AND, OR, or NOT. Gates are how hardware implements the same true-false decisions you write in code.
Introduced 6-8 · Unit 1
NOT
NOT flips a value. True becomes false. False becomes true. It is the simplest gate — one input, one inverted output.
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Age-specific explanations
6-8NOT flips a value. True becomes false. False becomes true. It is the simplest gate — one input, one inverted output.
9-12NOT flips a value. True becomes false. False becomes true. It is the simplest gate — one input, one inverted output.
Introduced 6-8 · Unit 1
OR
OR means at least one condition is true. Inclusive OR is true if one is true or if both are true. Fire alarm OR motion sensor — either can trigger the alert.
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Age-specific explanations
6-8OR means at least one condition is true. Inclusive OR is true if one is true or if both are true. Fire alarm OR motion sensor — either can trigger the alert.
9-12OR means at least one condition is true. Inclusive OR is true if one is true or if both are true. Fire alarm OR motion sensor — either can trigger the alert.
Introduced 6-8 · Unit 1
reason
To reason, in AI terms, is to process input with rules or a learned model to reach a conclusion. It is not human reasoning — it is computation on representations — but it fills the middle step between sense and act.
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Age-specific explanations
6-8To reason, in AI terms, is to process input with rules or a learned model to reach a conclusion. It is not human reasoning — it is computation on representations — but it fills the middle step between sense and act.
9-12To reason, in AI terms, is to process input with rules or a learned model to reach a conclusion. It is not human reasoning — it is computation on representations — but it fills the middle step between sense and act.
Introduced 6-8 · Unit 1
sense
To sense is to gather input from the environment — the machine equivalent of noticing. Cameras, microphones, and sensors sense. No sensing, no smart behavior.
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Age-specific explanations
K-2To sense is to gather input from the environment — the machine equivalent of noticing. Cameras, microphones, and sensors sense. No sensing, no smart behavior.
6-8To sense is to gather input from the environment — the machine equivalent of noticing. Cameras, microphones, and sensors sense. No sensing, no smart behavior.
9-12To sense is to gather input from the environment — the machine equivalent of noticing. Cameras, microphones, and sensors sense. No sensing, no smart behavior.
Introduced 6-8 · Unit 1
silicon
Silicon is the material microchips are made from. It can act as a semiconductor — sometimes conducting electricity, sometimes blocking it. That on-off behavior is the physical basis of computing.
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Age-specific explanations
6-8Silicon is the material microchips are made from. It can act as a semiconductor — sometimes conducting electricity, sometimes blocking it. That on-off behavior is the physical basis of computing.
9-12Silicon is the material microchips are made from. It can act as a semiconductor — sometimes conducting electricity, sometimes blocking it. That on-off behavior is the physical basis of computing.
Introduced 6-8 · Unit 1
software
Software is the instructions that tell hardware what to do — apps, operating systems, AI models loaded into memory. A frozen app is usually software, not a broken chip.
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Age-specific explanations
6-8Software is the instructions that tell hardware what to do — apps, operating systems, AI models loaded into memory. A frozen app is usually software, not a broken chip.
9-12Software is the instructions that tell hardware what to do — apps, operating systems, AI models loaded into memory. A frozen app is usually software, not a broken chip.
Introduced 6-8 · Unit 1
transistor
A transistor is a tiny electronic switch on a chip. Billions of them turn on and off to run programs. Modern AI runs on hardware built from transistors switching at extreme speed.
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Age-specific explanations
6-8A transistor is a tiny electronic switch on a chip. Billions of them turn on and off to run programs. Modern AI runs on hardware built from transistors switching at extreme speed.
9-12A transistor is a tiny electronic switch on a chip. Billions of them turn on and off to run programs. Modern AI runs on hardware built from transistors switching at extreme speed.
Introduced 6-8 · Unit 1
troubleshoot
Troubleshoot means narrow down where a problem lives — connection, software, settings, hardware — and test one layer at a time. Random guessing wastes time; systematic checks fix things faster.
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Age-specific explanations
6-8Troubleshoot means narrow down where a problem lives — connection, software, settings, hardware — and test one layer at a time. Random guessing wastes time; systematic checks fix things faster.
9-12Troubleshoot means narrow down where a problem lives — connection, software, settings, hardware — and test one layer at a time. Random guessing wastes time; systematic checks fix things faster.
Introduced 6-8 · Unit 1
truth table
A truth table lists every combination of inputs and the output a gate or condition produces. It is the cheat sheet for AND, OR, and NOT — every row is a test case.
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Age-specific explanations
6-8A truth table lists every combination of inputs and the output a gate or condition produces. It is the cheat sheet for AND, OR, and NOT — every row is a test case.
9-12A truth table lists every combination of inputs and the output a gate or condition produces. It is the cheat sheet for AND, OR, and NOT — every row is a test case.
Introduced 6-8 · Unit 2
algorithmic bias
Algorithmic bias is when a model is reliably more wrong for some group than others. Nobody typed in prejudice — the model learned skew from skewed data. The fix starts with data, labels, and evaluation across groups.
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Age-specific explanations
6-8Algorithmic bias is when a model is reliably more wrong for some group than others. Nobody typed in prejudice — the model learned skew from skewed data. The fix starts with data, labels, and evaluation across groups.
9-12Algorithmic bias is when a model is reliably more wrong for some group than others. Nobody typed in prejudice — the model learned skew from skewed data. The fix starts with data, labels, and evaluation across groups.
Introduced 6-8 · Unit 2
artificial intelligence
Artificial intelligence is software that senses input, finds patterns or plans actions, and outputs decisions — from photo sorting to chatbots to agents. Today's AI is mostly pattern learning at scale, not human-like understanding.
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Age-specific explanations
6-8Artificial intelligence is software that senses input, finds patterns or plans actions, and outputs decisions — from photo sorting to chatbots to agents. Today's AI is mostly pattern learning at scale, not human-like understanding.
9-12Artificial intelligence is software that senses input, finds patterns or plans actions, and outputs decisions — from photo sorting to chatbots to agents. Today's AI is mostly pattern learning at scale, not human-like understanding.
Introduced 6-8 · Unit 2
feature
A feature is a measurable property of an example the model can use — sweetness, color, word count, pixel brightness. Models do not see objects the way you do; they see lists of features.
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Age-specific explanations
6-8A feature is a measurable property of an example the model can use — sweetness, color, word count, pixel brightness. Models do not see objects the way you do; they see lists of features.
9-12A feature is a measurable property of an example the model can use — sweetness, color, word count, pixel brightness. Models do not see objects the way you do; they see lists of features.
Introduced 6-8 · Unit 2
generalization
Generalization is the ability to perform well beyond the training set. It is the main goal of machine learning. Without generalization, you just have a lookup table.
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Age-specific explanations
6-8Generalization is the ability to perform well beyond the training set. It is the main goal of machine learning. Without generalization, you just have a lookup table.
9-12Generalization is the ability to perform well beyond the training set. It is the main goal of machine learning. Without generalization, you just have a lookup table.
Introduced 6-8 · Unit 2
generalize
To generalize means the model works on new data it never trained on — not just memorizing old examples. A useful model generalizes. One that only succeeds on training data failed to learn the real pattern.
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Age-specific explanations
6-8To generalize means the model works on new data it never trained on — not just memorizing old examples. A useful model generalizes. One that only succeeds on training data failed to learn the real pattern.
9-12To generalize means the model works on new data it never trained on — not just memorizing old examples. A useful model generalizes. One that only succeeds on training data failed to learn the real pattern.
Introduced 6-8 · Unit 2
label
A label is the correct answer attached to an example — cat or dog, spam or not spam. Labeled data tells the model what to predict. Bad labels teach bad habits.
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Age-specific explanations
6-8A label is the correct answer attached to an example — cat or dog, spam or not spam. Labeled data tells the model what to predict. Bad labels teach bad habits.
9-12A label is the correct answer attached to an example — cat or dog, spam or not spam. Labeled data tells the model what to predict. Bad labels teach bad habits.
Introduced 6-8 · Unit 2
labeled dataset
A labeled dataset is a collection of examples where each item has its answer attached. Supervised learning runs on labeled datasets. The quality of labels is as important as the quantity.
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Age-specific explanations
6-8A labeled dataset is a collection of examples where each item has its answer attached. Supervised learning runs on labeled datasets. The quality of labels is as important as the quantity.
9-12A labeled dataset is a collection of examples where each item has its answer attached. Supervised learning runs on labeled datasets. The quality of labels is as important as the quantity.
Introduced 6-8 · Unit 2
model
A model is the pattern a system learned from data and saved for reuse. It takes input, applies what it learned, and outputs a guess. Different data builds a different model.
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Age-specific explanations
6-8A model is the pattern a system learned from data and saved for reuse. It takes input, applies what it learned, and outputs a guess. Different data builds a different model.
9-12A model is the pattern a system learned from data and saved for reuse. It takes input, applies what it learned, and outputs a guess. Different data builds a different model.
Introduced 6-8 · Unit 2
overfitting
Overfitting is when a model memorizes training examples instead of learning the underlying pattern. It scores high on old data and fails on new data. Too little diversity or too much complexity often causes it.
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Age-specific explanations
6-8Overfitting is when a model memorizes training examples instead of learning the underlying pattern. It scores high on old data and fails on new data. Too little diversity or too much complexity often causes it.
9-12Overfitting is when a model memorizes training examples instead of learning the underlying pattern. It scores high on old data and fails on new data. Too little diversity or too much complexity often causes it.
Introduced 6-8 · Unit 3
activation function
An activation function decides how a neuron responds after the weighted sum — a smooth curve instead of a hard on-off cutoff. It lets networks learn non-linear patterns. Without it, deep stacks would collapse to simple math.
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Age-specific explanations
6-8An activation function decides how a neuron responds after the weighted sum — a smooth curve instead of a hard on-off cutoff. It lets networks learn non-linear patterns. Without it, deep stacks would collapse to simple math.
9-12An activation function decides how a neuron responds after the weighted sum — a smooth curve instead of a hard on-off cutoff. It lets networks learn non-linear patterns. Without it, deep stacks would collapse to simple math.
Introduced 6-8 · Unit 3
backpropagation
Backpropagation sends error backward through the network to calculate how much each weight contributed. Then gradient descent updates those weights. It is the standard way deep networks learn from mistakes.
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Age-specific explanations
6-8Backpropagation sends error backward through the network to calculate how much each weight contributed. Then gradient descent updates those weights. It is the standard way deep networks learn from mistakes.
9-12Backpropagation sends error backward through the network to calculate how much each weight contributed. Then gradient descent updates those weights. It is the standard way deep networks learn from mistakes.
Introduced 6-8 · Unit 3
deep learning
Deep learning uses neural networks with many layers to learn complex patterns — images, speech, language. More layers can capture more detail, but they need more data and more compute.
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Age-specific explanations
6-8Deep learning uses neural networks with many layers to learn complex patterns — images, speech, language. More layers can capture more detail, but they need more data and more compute.
9-12Deep learning uses neural networks with many layers to learn complex patterns — images, speech, language. More layers can capture more detail, but they need more data and more compute.
Introduced 6-8 · Unit 3
gradient descent
Gradient descent is how training finds better weights automatically. It measures error, figures out which direction reduces error, and nudges weights that way — step after step. It is hill-climbing in reverse on a loss landscape.
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Age-specific explanations
6-8Gradient descent is how training finds better weights automatically. It measures error, figures out which direction reduces error, and nudges weights that way — step after step. It is hill-climbing in reverse on a loss landscape.
9-12Gradient descent is how training finds better weights automatically. It measures error, figures out which direction reduces error, and nudges weights that way — step after step. It is hill-climbing in reverse on a loss landscape.
Introduced 6-8 · Unit 3
layer
A layer is a row of neurons whose outputs feed the next row. Stacking layers lets a network build complex patterns from simple decisions. Deep learning means many layers.
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Age-specific explanations
6-8A layer is a row of neurons whose outputs feed the next row. Stacking layers lets a network build complex patterns from simple decisions. Deep learning means many layers.
9-12A layer is a row of neurons whose outputs feed the next row. Stacking layers lets a network build complex patterns from simple decisions. Deep learning means many layers.
Introduced 6-8 · Unit 3
neural network
A neural network is layers of connected neurons that learn by tuning weights. Input flows forward, the network produces an output, and training adjusts weights to reduce mistakes. It is pattern matching at scale.
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Age-specific explanations
6-8A neural network is layers of neurons that learn by tuning weights. Weighted sum, threshold, repeat — stacked thousands of times.
9-12A neural network is a differentiable stack of matrix operations trained via backpropagation and gradient descent.
Introduced 6-8 · Unit 3
neuron
In a neural network, a neuron is a tiny math unit — multiply inputs by weights, add them up, compare to a threshold, output on or off. It is arithmetic, not a brain cell. The name is historical.
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Age-specific explanations
6-8In a neural network, a neuron is a tiny math unit — multiply inputs by weights, add them up, compare to a threshold, output on or off. It is arithmetic, not a brain cell. The name is historical.
9-12In a neural network, a neuron is a tiny math unit — multiply inputs by weights, add them up, compare to a threshold, output on or off. It is arithmetic, not a brain cell. The name is historical.
Introduced 6-8 · Unit 3
sum
The sum is what you get after multiplying each input by its weight and adding everything together. That total gets compared to a threshold to decide if the neuron fires.
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Age-specific explanations
6-8The sum is what you get after multiplying each input by its weight and adding everything together. That total gets compared to a threshold to decide if the neuron fires.
9-12The sum is what you get after multiplying each input by its weight and adding everything together. That total gets compared to a threshold to decide if the neuron fires.
Introduced 6-8 · Unit 3
threshold
A threshold is the cutoff the weighted sum must beat for a neuron to fire. Below the threshold, output off. At or above, output on. Later models replace hard thresholds with smoother activation functions.
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Age-specific explanations
6-8A threshold is the cutoff the weighted sum must beat for a neuron to fire. Below the threshold, output off. At or above, output on. Later models replace hard thresholds with smoother activation functions.
9-12A threshold is the cutoff the weighted sum must beat for a neuron to fire. Below the threshold, output off. At or above, output on. Later models replace hard thresholds with smoother activation functions.
Introduced 6-8 · Unit 3
weight
A weight is a number saying how much an input matters to a neuron. Bigger weight, stronger pull on the output. Training a network mostly means adjusting weights until errors shrink.
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Age-specific explanations
6-8A weight is a number saying how much an input matters to a neuron. Bigger weight, stronger pull on the output. Training a network mostly means adjusting weights until errors shrink.
9-12A weight is a number saying how much an input matters to a neuron. Bigger weight, stronger pull on the output. Training a network mostly means adjusting weights until errors shrink.
Introduced 6-8 · Unit 4
accuracy
Accuracy is the fraction of predictions that were correct — right divided by total. Ten right out of sixteen is about sixty-three percent. Accuracy alone does not tell you who the errors hurt.
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Age-specific explanations
6-8Accuracy is the fraction of predictions that were correct — right divided by total. Ten right out of sixteen is about sixty-three percent. Accuracy alone does not tell you who the errors hurt.
9-12Accuracy is the fraction of predictions that were correct — right divided by total. Ten right out of sixteen is about sixty-three percent. Accuracy alone does not tell you who the errors hurt.
Introduced 6-8 · Unit 4
clean data
Clean data has duplicates removed, typos fixed, blanks handled, and labels corrected. Dirty data poisons training and makes every metric look untrustworthy. Clean before you trust a score.
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Age-specific explanations
6-8Clean data has duplicates removed, typos fixed, blanks handled, and labels corrected. Dirty data poisons training and makes every metric look untrustworthy. Clean before you trust a score.
9-12Clean data has duplicates removed, typos fixed, blanks handled, and labels corrected. Dirty data poisons training and makes every metric look untrustworthy. Clean before you trust a score.
Introduced 6-8 · Unit 4
curate
To curate data is to choose what goes in, label it, and remove junk. Curation is a human decision. What you include, exclude, and how you label shapes the model's behavior.
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Age-specific explanations
6-8To curate data is to choose what goes in, label it, and remove junk. Curation is a human decision. What you include, exclude, and how you label shapes the model's behavior.
9-12To curate data is to choose what goes in, label it, and remove junk. Curation is a human decision. What you include, exclude, and how you label shapes the model's behavior.
Introduced 6-8 · Unit 4
data quality
Data quality covers accuracy, completeness, consistency, and relevance. High-quality data is clean, labeled well, and covers the cases that matter. Models inherit data quality — they cannot fix bad source material alone.
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Age-specific explanations
6-8Data quality covers accuracy, completeness, consistency, and relevance. High-quality data is clean, labeled well, and covers the cases that matter. Models inherit data quality — they cannot fix bad source material alone.
9-12Data quality covers accuracy, completeness, consistency, and relevance. High-quality data is clean, labeled well, and covers the cases that matter. Models inherit data quality — they cannot fix bad source material alone.
Introduced 6-8 · Unit 4
error
An error is a case the model got wrong. Errors are not random noise — they cluster. Always ask which examples fail and whether failures fall harder on one group.
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Age-specific explanations
6-8An error is a case the model got wrong. Errors are not random noise — they cluster. Always ask which examples fail and whether failures fall harder on one group.
9-12An error is a case the model got wrong. Errors are not random noise — they cluster. Always ask which examples fail and whether failures fall harder on one group.
Introduced 6-8 · Unit 4
evaluation
Evaluation means measuring how well a model performs on data it was not trained on. You pick metrics, run tests, and inspect errors. Building a model without evaluation is guessing.
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Age-specific explanations
6-8Evaluation means measuring how well a model performs on data it was not trained on. You pick metrics, run tests, and inspect errors. Building a model without evaluation is guessing.
9-12Evaluation means measuring how well a model performs on data it was not trained on. You pick metrics, run tests, and inspect errors. Building a model without evaluation is guessing.
Introduced 6-8 · Unit 4
representativeness
Representativeness asks whether your data looks like the real situations the model will face. Training on one campus and deploying everywhere fails if the data is not representative. Measure gaps before you deploy.
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Age-specific explanations
6-8Representativeness asks whether your data looks like the real situations the model will face. Training on one campus and deploying everywhere fails if the data is not representative. Measure gaps before you deploy.
9-12Representativeness asks whether your data looks like the real situations the model will face. Training on one campus and deploying everywhere fails if the data is not representative. Measure gaps before you deploy.
Introduced 6-8 · Unit 5
digital citizenship
Digital citizenship is using technology responsibly — protecting privacy, spotting scams, giving credit, and thinking about how your actions affect others online. It is ethics plus security plus everyday habits.
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Age-specific explanations
6-8Digital citizenship is using technology responsibly — protecting privacy, spotting scams, giving credit, and thinking about how your actions affect others online. It is ethics plus security plus everyday habits.
9-12Digital citizenship is using technology responsibly — protecting privacy, spotting scams, giving credit, and thinking about how your actions affect others online. It is ethics plus security plus everyday habits.
Introduced 6-8 · Unit 5
encryption
Encryption scrambles data so only someone with the key can read it. Even if a message is intercepted, ciphertext looks like garbage without decryption. It protects privacy in transit and at rest.
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Age-specific explanations
6-8Encryption scrambles data so only someone with the key can read it. Even if a message is intercepted, ciphertext looks like garbage without decryption. It protects privacy in transit and at rest.
9-12Encryption scrambles data so only someone with the key can read it. Even if a message is intercepted, ciphertext looks like garbage without decryption. It protects privacy in transit and at rest.
Introduced 6-8 · Unit 5
malware
Malware is harmful software that steals data, spies, or locks your files. It often arrives through bad downloads or phishing links. Updates and antivirus add layers — no single tool is enough.
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Age-specific explanations
6-8Malware is harmful software that steals data, spies, or locks your files. It often arrives through bad downloads or phishing links. Updates and antivirus add layers — no single tool is enough.
9-12Malware is harmful software that steals data, spies, or locks your files. It often arrives through bad downloads or phishing links. Updates and antivirus add layers — no single tool is enough.
Introduced 6-8 · Unit 5
phishing
Phishing is a fake message designed to steal passwords or personal info — urgent emails, bogus login pages, scam texts. Real companies rarely ask you to verify a password by link. When in doubt, do not click.
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Age-specific explanations
6-8Phishing is a fake message designed to steal passwords or personal info — urgent emails, bogus login pages, scam texts. Real companies rarely ask you to verify a password by link. When in doubt, do not click.
9-12Phishing is a fake message designed to steal passwords or personal info — urgent emails, bogus login pages, scam texts. Real companies rarely ask you to verify a password by link. When in doubt, do not click.
Introduced 6-8 · Unit 5
privacy
Privacy is control over who sees and uses your data. Photos, location, messages, browsing history — all personal. Before you share, ask who gets access and whether you can take it back.
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Age-specific explanations
6-8Privacy is control over who sees and uses your data. Photos, location, messages, browsing history — all personal. Before you share, ask who gets access and whether you can take it back.
9-12Privacy is control over who sees and uses your data. Photos, location, messages, browsing history — all personal. Before you share, ask who gets access and whether you can take it back.
Introduced 6-8 · Unit 5
risk vs. benefit
Risk versus benefit means weighing what could go wrong against what you gain before you act. Posting a photo might get likes but expose location. Good digital citizens do this calculation on purpose.
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Age-specific explanations
6-8Risk versus benefit means weighing what could go wrong against what you gain before you act. Posting a photo might get likes but expose location. Good digital citizens do this calculation on purpose.
9-12Risk versus benefit means weighing what could go wrong against what you gain before you act. Posting a photo might get likes but expose location. Good digital citizens do this calculation on purpose.
Introduced 6-8 · Unit 5
security
Security is protecting systems and data from unauthorized access, theft, and damage. Strong passwords, updates, encryption, and careful sharing are layers. Security is never one switch — it is a stack.
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Age-specific explanations
6-8Security is protecting systems and data from unauthorized access, theft, and damage. Strong passwords, updates, encryption, and careful sharing are layers. Security is never one switch — it is a stack.
9-12Security is protecting systems and data from unauthorized access, theft, and damage. Strong passwords, updates, encryption, and careful sharing are layers. Security is never one switch — it is a stack.