Data to AI in the classroom
Data to AI is 24 modules of five-minute lessons on how AI works, starting with the data on students’ own phones. There are no student accounts, and it collects nothing about the people using it. Here’s how it fits a class, and which standards each module lines up with.
How it fits a class
A five-minute opener
Each lesson is about five minutes: a scene, something to play with, a named idea and one sentence to say out loud. It fits the start of a period.
Nothing to set up
No accounts, no ads and no analytics. Progress stays on the phone, so there’s no student data for anyone to manage.
A question to take home
Every module ends with a Dinner Table Challenge, one question to ask someone at home. Explaining it out loud is how students find out whether they got it.
Try it before anyone installs anything
The first lesson runs in the browser, so you can put it on the projector and play it with the class.
Standards, module by module
Codes come from the Common Core State Standards for Mathematics and the CSTA K–12 Computer Science Standards (2017 revised). They mark where a module’s topic lines up with a standard, not a full unit that covers it. CSTA published a 2026 revision; these codes are the 2017 ones and will be updated. Where nothing fits honestly, it says so.
1. You’re a Data Source
Your digital footprint
- CSTA 2-IC-23Describe tradeoffs between allowing information to be public and keeping information private and secure.
- CSTA 3A-IC-29Explain the privacy concerns related to the collection and generation of data through automated processes that may not be evident to users.
2. What Counts as Data
Structured vs. unstructured data
No close match in either set. This one goes past what the standards cover.
3. Where Data Lives
What a database is
- CSTA 2-DA-07Represent data using multiple encoding schemes.
4. Spreadsheet Superpowers
Excel and Google Sheets basics
- CSTA 2-DA-08Collect data using computational tools and transform the data to make it more useful and reliable.
5. Clean It or It Lies
Data cleaning
- CSTA 2-DA-08Collect data using computational tools and transform the data to make it more useful and reliable.
6. Good Questions, Fair Data
Sampling bias
- CCSS 6.SP.A.1Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. For example, 'How old am I?' is not a statistical question, but 'How old are the students in my school?' is a statistical question because one anticipates variability in students' ages.
- CCSS 7.SP.A.1Understand that statistics can be used to gain information about a population by examining a sample of the population; generalizations about a population from a sample are valid only if the sample is representative of that population. Understand that random sampling tends to produce representative samples and support valid inferences.
7. Averages That Lie
Mean vs. median
- CCSS 6.SP.A.3Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.
- CCSS HSS-ID.A.2Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
8. Spread, Shape and Percentiles
Standard deviation and percentiles
- CCSS HSS-ID.A.4Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
- CCSS 6.SP.B.5cGiving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.
9. Chance, Samples and Star Ratings
Probability and sample size
- CCSS 7.SP.C.5Understand that the probability of a chance event is a number between 0 and 1 that expresses the likelihood of the event occurring. Larger numbers indicate greater likelihood. A probability near 0 indicates an unlikely event, a probability around 1/2 indicates an event that is neither unlikely nor likely, and a probability near 1 indicates a likely event.
- CCSS 7.SP.A.2Use data from a random sample to draw inferences about a population with an unknown characteristic of interest. Generate multiple samples (or simulated samples) of the same size to gauge the variation in estimates or predictions…
10. Correlation Isn’t Causation
Correlation vs. causation
- CCSS HSS-ID.C.9Distinguish between correlation and causation.
- CCSS HSS-ID.B.6Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
11. Charts That Tell the Truth (and Ones That Don’t)
Misleading graphs
No close match in either set. This one goes past what the standards cover.
12. Running a Fair Test
A/B testing
- CCSS HSS-IC.B.3Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.
- CCSS HSS-IC.B.5Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
13. Telling a Data Story
Data storytelling
- CCSS HSS-IC.B.6Evaluate reports based on data.
- CSTA 3A-DA-11Create interactive data visualizations using software tools to help others better understand real-world phenomena.
14. Teaching With Examples
What machine learning is
No close match in either set. This one goes past what the standards cover.
15. Predicting Numbers
Linear regression
- CCSS HSS-ID.B.6cFit a linear function for a scatter plot that suggests a linear association.
- CCSS HSS-ID.C.7Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
16. Sorting Into Categories
Classification
No close match in either set. This one goes past what the standards cover.
17. Finding Hidden Groups
Clustering
No close match in either set. This one goes past what the standards cover.
18. Learned It or Memorized It?
Overfitting
No close match in either set. This one goes past what the standards cover.
19. Bias In, Bias Out
AI bias
- CSTA 2-IC-21Discuss issues of bias and accessibility in the design of existing technologies.
- CSTA 3A-IC-25Test and refine computational artifacts to reduce bias and equity deficits.
20. Inside a Neural Network
Neural networks and deep learning
No close match in either set. This one goes past what the standards cover.
21. How Chatbots Work
How ChatGPT works
No close match in either set. This one goes past what the standards cover.
22. How Your Feed Works
Recommendation algorithms
- CSTA 3A-IC-24Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices.
23. AI in the Real World
What data scientists do
- CSTA 3B-DA-05Use data analysis tools and techniques to identify patterns in data representing complex systems.
- CSTA 3B-DA-06Select data collection tools and techniques to generate data sets that support a claim or communicate information.
24. Your Turn
Your first data project with Python
- CSTA 3A-DA-11Create interactive data visualizations using software tools to help others better understand real-world phenomena.
- CSTA 3A-DA-12Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
Questions teachers ask
What grades is Data to AI for?
It’s written for curious teens and adults, so it fits middle and high school. The statistics modules line up with grades 6 through 12 math standards, and the computing modules with CSTA levels 2, 3A and 3B.
Do students need accounts?
No. There’s no sign-up, no ads and no analytics, and progress stays on the phone.
Does it cover the statistics in the math standards?
Parts of it. Mean and median, spread, sampling, correlation versus causation, randomized experiments and fitting a line all line up with Common Core statistics standards, as the map shows. It’s a companion to a math class, not a replacement for one.