Curriculum alignment · United States

CSTA K–12 Computer Science Standards and AI learning priorities (US)

We checked the LittleAIMaster Grade 1–12 AI course against all 173 rated requirements of this framework: 116 fully covered, 38 mostly covered, 19 partly covered. Open a requirement to see what learners do and the exact lessons.

89%
Fully covered 116Mostly covered 38Partly covered 19of 173 requirements37 taught earlier than the framework asks

Source documents (3)

CSTA K-12 Computer Science Standards, Revised 2017
Computer Science Teachers Association (CSTA), with ACM, Code.org, Cyber Innovation Center, National Math and Science Initiative (K-12 CS Framework)
Revised 2017 (current, in force nationally as a voluntary standard; widely adopted/adapted by states)
CSTA PK-12 Computer Science Standards, 2026 Revision
Computer Science Teachers Association (CSTA)
Draft reviewed Dec 2025-Jan 2026; official launch anticipated at the CSTA Annual Conference, July 2026
AI Learning Priorities for All K-12 Students
TeachAI and CSTA (project growing out of the 2024 TeachAI/CSTA "Guidance on the Future of Computer Science Education in an Age of AI")
2024-2025

Alignment chart

Every CSTA (US) requirement, rated

5Taught at this grade5Some material5Grades the framework asks for
Open a requirement to see what learners do and the exact lessons.

Showing 173 of 173 requirements

CSTA K-12 Computer Science Standards (2017 revision, in force)

Algorithms and Programming (1A-AP-08)

Model daily processes by creating and following algorithms (step-by-step instructions) to complete tasks.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Test It Many Ways' and Grade 2 'Robo Repeats', learners write a paper route, predict where it ends from two starting points, run it and explain changed results.

Algorithms and Programming (1A-AP-09)

Model the way programs store and manipulate data by using numbers or other symbols to represent information.

Framework grades 1–2
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Tiny Squares' and 'From Sound to Sentence', learners decode on and off pixel rows and connect captured sound to sampled numbers.

Still to add

Text and numeric encoding and translation across all the named representations are not covered at this level.

Algorithms and Programming (1A-AP-10)

Develop programs with sequences and simple loops to express ideas or address a problem.

Framework grades 1–2
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Robo Repeats', learners run a repeated sequence and reason about an if example.

Still to add

Stored variables, nested loops and executable compound conditions are not part of the programs at these grades.

Algorithms and Programming (1A-AP-11)

Decompose (break down) the steps needed to solve a problem into a precise sequence of instructions.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Big Job, Small Jobs', learners choose a familiar job, name the smaller jobs and a helper or check for each, then carry out the sequence.

Algorithms and Programming (1A-AP-12)

Develop plans that describe a program's sequence of events, goals, and expected outcomes.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Test It Many Ways' and Grade 2 'Robo Repeats', learners write a goal and expected destination before arranging commands, then run from the stated start.

Algorithms and Programming (1A-AP-13)

Give attribution when using the ideas and creations of others while developing programs.

Framework grades 1–2
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 1 'Find It, Make It, Share It', learners ask before reusing a peer's picture and put a credit on a revised paper poster.

Still to add

Licence conditions and how intellectual-property rules affect innovation are not taught at this grade band.

Algorithms and Programming (1A-AP-14)

Debug (identify and fix) errors in an algorithm or program that includes sequences and simple loops.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Robo Repeats' and 'Check Byte's Work', learners run a grid route, repair the command sequence and explain the missing pickup or wrong turn to a partner.

Algorithms and Programming (1A-AP-15)

Using correct terminology, describe steps taken and choices made during the iterative process of program development.

Framework grades 1–2
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Test It Many Ways' and Grade 2 'Robo Repeats', learners predict and test different starting points, record expected and actual notes, and hand the plan to another pair.

Still to add

Per-rule design comments and a systematic peer-debugging record are limited.

Algorithms and Programming (1B-AP-08)

Compare and refine multiple algorithms for the same task and determine which is the most appropriate.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Dots, Lines and Moves' and 'Byte on the Grid', learners count route actions and compare expanded and repeated or shorter valid solutions, explaining correctness and clarity.

Algorithms and Programming (1B-AP-09)

Create programs that use variables to store and modify data.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Repeat and Remember', learners run counters and nested repeats, inspect traces and justify repeated versus sequential instructions.

Still to add

Executable compound conditions and selection are not available in the program interpreter.

Algorithms and Programming (1B-AP-10)

Create programs that include sequences, events, loops, and conditionals.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Repeat and Remember', learners run counters and nested repeats, inspect traces and justify repeated versus sequential instructions.

Still to add

Executable compound conditions and selection are not available in the program interpreter.

Algorithms and Programming (1B-AP-11)

Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Break It Up Yourself', learners split team preparation into workable jobs. In Grade 4 'Thinking Tools, Real Names', they refine a water task and check that its output meets the whole team's need.

Algorithms and Programming (1B-AP-12)

Modify, remix, or incorporate portions of an existing program into one's own work, to develop something new or add more advanced features.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 3 'Remix a Helper', learners extend another class's dataset with a new category, credit the source and discuss failures and next checks.

Still to add

The test report for the remix is supplied rather than coming from a running extended application.

Algorithms and Programming (1B-AP-13)

Use an iterative process to plan the development of a program by including others' perspectives and considering user preferences.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Build as a Team' and Grade 5 'Demo Day: My Own Helper', learners plan, build, test and revise a prototype with roles, notes and a user trial record.

Still to add

The real-user paper helper and the in-app AI build are separate, so the full open software lifecycle is not joined up.

Algorithms and Programming (1B-AP-14)

Observe intellectual property rights and give appropriate attribution when creating or remixing programs.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Who Owns It?', learners apply permission and licence badges, leave out an unapproved photo, credit reused work and explain creator control and reuse.

Algorithms and Programming (1B-AP-15)

Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Debug the Algorithm' and Grade 5 'Debug the Steps', learners spot the wrong step, fix it, sort bug kinds and run a debugging loop. Written reasoning is recorded in the unplugged classroom sheets.

Algorithms and Programming (1B-AP-16)

Take on varying roles, with teacher guidance, when collaborating with peers during the design, implementation, and review stages of program development.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Build as a Team', learners distribute four roles, manage dependent timeline tasks, build a rule chain and record reasons and review comments.

Still to add

Version notes are practised, but a collaborative version-control workflow or tool is not used.

Algorithms and Programming (1B-AP-17)

Describe choices made during program development using code comments, presentations, and demonstrations.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Check Byte's Code, Leave Good Notes' and Grade 4 'Build as a Team', learners record expected and actual results, attach reasons to each rule, and use another child's notes to find and retest a rule error.

Algorithms and Programming (2-AP-10)

Use flowcharts and/or pseudocode to address complex problems as algorithms.

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 7 'Unit 4 Review', learners order a chatbot flowchart using rectangles for actions and diamonds for decisions, draw their own, and test it by role-play. Grade 6 includes a 'Flowchart' flashcard.

Still to add

Oval start/end and input/output symbols are not taught, no flowchart with a loop is traced, and language-independent pseudocode is not taught before Grade 12.

Algorithms and Programming (2-AP-11)

Create clearly named variables that represent different data types and perform operations on their values.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 chapters such as 'Making Decisions: If Statements', 'Loop Magic' and 'Loops Inside Loops', learners assemble and trace programs with variables, if/elif/else, and/or/not, for and while loops and nested loops, and justify condition order.

Algorithms and Programming (2-AP-12)

Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 chapters such as 'Making Decisions: If Statements', 'Loop Magic' and 'Loops Inside Loops', learners assemble and trace programs with variables, if/elif/else, and/or/not, for and while loops and nested loops, and justify condition order.

Algorithms and Programming (2-AP-13)

Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

Learners split problems into ordered sub-problems in Grade 6 'Your AI Superpowers' (five-step planning), Grade 7 'Aisha's Big Idea' (one job per day) and Grade 8 'First Feature Victory' (feature-by-feature building).

Algorithms and Programming (2-AP-14)

Create procedures with parameters to organize code and make it easier to reuse.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'Simple Decision Systems' and Python Lab Module 6 'Split the Advisor', learners refactor repeated advice logic into named helper functions with parameters and test that decisions stay the same.

Where it is taught

Algorithms and Programming (2-AP-15)

Seek and incorporate feedback from team members and users to refine a solution that meets user needs.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Challenge' and Grade 7 'Refinement Week!', learners follow an Ideate-Plan-Build-Test-Improve cycle, build feature by feature, refine with user feedback and rehearse a presentation.

Algorithms and Programming (2-AP-16)

Incorporate existing code, media, and libraries into original programs, and give attribution.

We start Grade 3 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Remix a Helper', learners start from another class's helper, add a new label and photos, sort the unplanned changes (a purple carrot misread, a gap kept), and write a credit line. In Python Lab they reuse their own helper functions.

Where it is taught

Algorithms and Programming (2-AP-17)

Systematically test and refine programs using a range of test cases.

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Check Byte's Code, Leave Good Notes', learners test normal and edge cases, fill an expected-versus-actual table, write why-notes on each rule, and find a friend's bug using only his notes and test table.

Where it is taught

Algorithms and Programming (2-AP-18)

Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts.

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 6 'Group AI Project', learners match strengths to team roles and sort good and struggling team habits. In Grade 7 'Aisha's Big Idea' they order a plan's weeks and meet Trello cards.

Still to add

Learners never work in a team role on a build or use a collaborative tool or version control; timelines handled are fixed plans for a character.

Where it is taught

Algorithms and Programming (2-AP-19)

Document programs in order to make them easier to follow, test, and debug.

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Check Byte's Code, Leave Good Notes', learners test normal and edge cases, fill an expected-versus-actual table, write why-notes on each rule, and find a friend's bug using only his notes and test table.

Where it is taught

Algorithms and Programming (3A-AP-13)

Create prototypes that use algorithms to solve computational problems by leveraging prior student knowledge and personal interests.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners build and iterate a Python program with testing and debugging in Grade 8 'The Failed Demo'. In Grade 9 'Good to Great' they measure, hypothesise, experiment and evaluate, with peer feedback in 'The Helpful Review'.

Algorithms and Programming (3A-AP-14)

Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 53, learners implement stack undo, a deque print queue, linked-list insertion and recursive tree traversal, choose structures for three jobs and measure FIFO performance.

Algorithms and Programming (3A-AP-15)

Justify the selection of specific control structures when tradeoffs involve implementation, readability, and program performance, and explain the benefits and drawbacks of choices made.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners complete sequence, if/elif/else, for and while loops, range, break/continue, nested loops and compound conditions in Grade 8, such as 'Welcome AI Builders' and 'The 100 Names Problem', before Grade 9.

Algorithms and Programming (3A-AP-16)

Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners build and iterate a Python program with testing and debugging in Grade 8 'The Failed Demo'. In Grade 9 'Good to Great' they measure, hypothesise, experiment and evaluate, with peer feedback in 'The Helpful Review'.

Algorithms and Programming (3A-AP-17)

Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 31 'Refactor Without Changing the Answer', learners split a data report into load_rows, cleaning, summary and report functions with parameters, and check it still works on a second file.

Algorithms and Programming (3A-AP-18)

Create artifacts by using procedures within a program, combinations of data and procedures, or independent but interrelated programs.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 31 'Refactor Without Changing the Answer', learners split a data report into load_rows, cleaning, summary and report functions with parameters, and check it still works on a second file.

Algorithms and Programming (3A-AP-19)

Systematically design and develop programs for broad audiences by incorporating feedback from users.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners build and iterate a Python program with testing and debugging in Grade 8 'The Failed Demo'. In Grade 9 'Good to Great' they measure, hypothesise, experiment and evaluate, with peer feedback in 'The Helpful Review'.

Algorithms and Programming (3A-AP-20)

Evaluate licenses that limit or restrict use of computational artifacts when using resources such as libraries.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'The Data Hunter' and 'Open Data Revolution', learners judge eight items against licences such as CC0 and Terms of Service, credit sources under charts, and match a code licence to what others may legally do.

Still to add

Grades 9-10 do not discuss how intellectual-property law affects innovation; learners apply licence terms but do not explain the trade-off between rewarding creators and giving others access.

Algorithms and Programming (3A-AP-21)

Evaluate and refine computational artifacts to make them more usable and accessible.

Framework grades 9–10
Partly covered
Our Grades 9–10 Partly covered
What learners do

Grade 10 lessons such as 'AI for Global Good' cover accessibility needs (captions, text-to-speech, image descriptions) and barriers such as language and low connectivity, and learners match barriers to design fixes.

Still to add

No interface is designed or evaluated against diverse users at Grades 9-10, and no usability heuristics or accessibility standard such as WCAG are named or applied.

Algorithms and Programming (3A-AP-22)

Design and develop computational artifacts working in team roles using collaborative tools.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Team Roles, a Shared Board, a Shared Repo', learners write their own Gantt chart with owners and dependencies, then run a Repo Lab mission: two commits, a restore, a branch, and a reviewed pull request that closes an issue.

Still to add

Teammates in Repo Lab are scripted, merge conflicts are not in the Grade 9 missions, and no real team build with shared tasks runs to completion.

Where it is taught

Algorithms and Programming (3A-AP-23)

Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.

We start Grade 8 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners practise edge-case testing and debugging in Grade 8 'Inside a Chatbot'. In Grade 9 'The Missing Map' and 'The Helpful Review', they work on README files, comment quality, a data dictionary and peer review.

Algorithms and Programming (3B-AP-08)

Describe how artificial intelligence drives many software and physical systems.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 11 gives a chapter each to AI in Healthcare, Finance, Agriculture, Manufacturing, Transportation and Entertainment, with the named AI task in each sector.

Algorithms and Programming (3B-AP-09)

Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Building Your App' and Grade 12 'The Blueprint Before the Building', learners choose their own problem, build and test an assistant in Prompt Lab, and write a pitch. Grade 12 also trains and tests a Raise Your AI companion.

Still to add

The finished build is not delivered as a recorded talk at these grades, and the build is a system-prompt assistant rather than a model or pipeline the learner owns.

Algorithms and Programming (3B-AP-10)

Use and adapt classic algorithms to solve computational problems.

We start Grade 8 · framework Grade 11Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Backtracking in Ten Lines', learners predict outputs and call counts of a recursive solver and compare two search methods. In Grade 12 'Big-O by Hand', they work out growth rates, such as about 20 halvings for binary search.

Still to add

The complete linear-search comparison is missing, and Grade 11-12 learners trace and compare algorithms but do not implement search or sorting there.

Where it is taught

Algorithms and Programming (3B-AP-11)

Evaluate algorithms in terms of their efficiency, correctness, and clarity.

We start Grade 8 · framework Grade 11Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Backtracking in Ten Lines', learners predict outputs and call counts of a recursive solver and compare two search methods. In Grade 12 'Big-O by Hand', they work out growth rates, such as about 20 halvings for binary search.

Still to add

The complete linear-search comparison is missing, and Grade 11-12 learners trace and compare algorithms but do not implement search or sorting there.

Where it is taught

Algorithms and Programming (3B-AP-12)

Compare and contrast fundamental data structures and their uses.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'The Q Revolution', learners trace a replay queue and sort situations into queue or stack. In Grade 12 'The Whiteboard Challenge' they match problems to patterns, and Python Lab Module 28 covers stacks, linked lists and trees.

Where it is taught

Algorithms and Programming (3B-AP-13)

Illustrate the flow of execution of a recursive algorithm.

We start Grade 8 · framework Grade 11Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Backtracking in Ten Lines', learners predict outputs and call counts of a recursive solver and compare two search methods. In Grade 12 'Big-O by Hand', they work out growth rates, such as about 20 halvings for binary search.

Still to add

The complete linear-search comparison is missing, and Grade 11-12 learners trace and compare algorithms but do not implement search or sorting there.

Where it is taught

Algorithms and Programming (3B-AP-14)

Construct solutions to problems using student-created components, such as procedures, modules and/or objects.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Python Lab Module 17 'Challenge: Split the Pipeline', learners write a package of modules and import functions from it. In Module 28 they define objects with Enum, NamedTuple and dataclasses.

Algorithms and Programming (3B-AP-15)

Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Solving Constraints in Code', learners model a new system (club rooms or bus seats) as variables, domains and a checker, run the same unchanged solver on it, and write what the model leaves out and why that is safe.

Still to add

The own-model task is optional, and the write-up on what the model leaves out is not assessed.

Where it is taught

Algorithms and Programming (3B-AP-16)

Demonstrate code reuse by creating programming solutions using libraries and APIs.

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Python Lab Module 23 and Module 17, learners write an API client function and build a small package of modules. In Grade 11 'Power at Your Fingertips' they read real API code and match errors to fixes.

Still to add

Tasks have fixed goals rather than extending existing code with new functionality, and benefit and risk interpretation is prescribed rather than a learner discussion of unintended effects.

Algorithms and Programming (3B-AP-17)

Plan and develop programs for broad audiences using a software lifecycle process.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Development Phase 3', learners let three people try their assistant on one real task, log issue severity, make one change from the feedback, re-run the eight-case suite twice, and record before and after pass counts.

Where it is taught

Algorithms and Programming (3B-AP-18)

Explain security issues that might lead to compromised computer programs.

Framework grades 11–12
Partly covered
Our Grades 11–12 Partly covered
What learners do

In Grade 11 'The Silent Failures', learners match attack traces such as abuse traffic and prompt injection to proportionate defences. Grade 12 'Smart Grid Hero' covers a grid cyber attack and defence practices.

Still to add

Malware, ransomware, DDoS, botnets and social engineering beyond email are not explained, and learners do not write their own proportionate security plan.

Where it is taught

Algorithms and Programming (3B-AP-19)

Develop programs for multiple computing platforms.

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Building Your App', learners study interface design rules, compare Streamlit and Gradio, and write an inclusive design brief. In Python Lab Module 52 they build an assistant front end.

Still to add

Learners do not test on two platforms, and there is no substantial independently scoped larger application.

Where it is taught

Algorithms and Programming (3B-AP-20)

Use version control systems, integrated development environments (IDEs), and collaborative tools and practices (code documentation) in a group software project.

We start Grade 9 · framework Grade 11Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Research as a Team', learners run a practice repository with scripted or paired teammates: fill a roles table, open issues with owners and due days, review pull requests, resolve a merge conflict, re-plan a slipped task, and write a team log.

Still to add

The team build happens in a practice repository with scripted teammates, not on a real shared project, and the team log is self-reported.

Algorithms and Programming (3B-AP-21)

Develop and use a series of test cases to verify that a program performs according to its design specifications.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Building Your App' and Grade 12 'The Blueprint Before the Building', learners write test cases before running them, run each twice, record pass or fail, fix a line and re-run the whole suite.

Note

Review is of executable tests, not an independent peer review session.

Where it is taught

Algorithms and Programming (3B-AP-22)

Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).

Framework grades 11–12
Mostly covered
Our Grades 11–12 Mostly covered
What learners do

In Python Lab Module 23 and Module 17, learners write an API client function and build a small package of modules. In Grade 11 'Power at Your Fingertips' they read real API code and match errors to fixes.

Still to add

Tasks have fixed goals rather than extending existing code with new functionality, and benefit and risk interpretation is prescribed rather than a learner discussion of unintended effects.

Algorithms and Programming (3B-AP-23)

Evaluate key qualities of a program through a process such as a code review.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Building Your App' and Grade 12 'The Blueprint Before the Building', learners write test cases before running them, run each twice, record pass or fail, fix a line and re-run the whole suite.

Note

Review is of executable tests, not an independent peer review session.

Where it is taught

Algorithms and Programming (3B-AP-24)

Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.

We start Grade 9 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Python Lab Modules 24, 27, 28 and 46, learners write graded programs using types, loops, conditionals and arrays. In Module 27 'Look Inside Python', they look at how Python code is translated and run.

Data and Analysis (1A-DA-05)

Store, copy, search, retrieve, modify, and delete information using a computing device and define the information stored as data.

Framework grades 1–2
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Tally It, Chart It', learners collect friends' travel counts, enter and save bars, and relate the saved chart to a paper tally.

Still to add

A second digital format and an explicit retrieve-and-modify lesson are limited at this level.

Data and Analysis (1A-DA-06)

Collect and present the same data in various visual formats.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Tally It, Chart It', learners choose tally marks or bars for a purpose, collect actual counts and show a bar chart to classmates.

Data and Analysis (1A-DA-07)

Identify and describe patterns in data visualizations, such as charts or graphs, to make predictions.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Tally It, Chart It', learners use a growth chart to make a prediction with a reason and tell rain causing puddles from simply liking tall bars.

Data and Analysis (1B-DA-06)

Organize and present collected data visually to highlight relationships and support a claim.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Pick the Right Chart', learners choose bars or a time-series line, enter a collected chart and use its saved counts in a poster for an audience.

Data and Analysis (1B-DA-07)

Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Thinking Tools, Real Names' and Grade 5 'Did It Really Help?', learners predict from a dated pattern, interpret an impact comparison and explain what the observations support or fail to show.

Data and Analysis (2-DA-07)

Represent data using multiple encoding schemes.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'How Computers Count - Binary' and Grade 7 'Pixels and Pictures' and 'Colors for Computers', learners convert binary both ways, compute pixels and megapixels, and convert RGB to hex by hand.

Data and Analysis (2-DA-08)

Collect data using computational tools and transform the data to make it more useful and reliable.

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Patterns in Data' and Python Lab Module 31 'Data Cleaning', learners clean a synthetic canteen log by removing duplicates, handling missing data, spotting an outlier, encoding and scaling.

Still to add

The dataset is invented, and the preprocessing is a whole-table cleaning exercise rather than preparation using training data only.

Data and Analysis (2-DA-09)

Refine computational models based on the data they have generated.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Testing What AI Learned' and Grade 8 'Testing AI Solutions', learners use train, validation and test splits, compute accuracy by hand, drag results into a confusion matrix, and use the precision and recall formulas.

Note

The F1 score is not covered at this level; it arrives in Grade 9.

Data and Analysis (3A-DA-09)

Translate between different bit representations of real-world phenomena, such as characters, numbers, and images.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Numbers in AI' and Python Lab Module 48, learners work with number types, pixels and binary, and compute sample times and frequency features from sampled sound arrays.

Still to add

Bit-depth quantisation and a complete account of how audio is stored are not yet included.

Where it is taught

Data and Analysis (3A-DA-10)

Evaluate the tradeoffs in how data elements are organized and where data is stored.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 22 and Grade 9 'Loading Data', learners create linked tables with keys, write and test grouped JOIN queries, reject an invalid foreign key, and compare a rename in normalised versus duplicated stores.

Where it is taught

Data and Analysis (3A-DA-11)

Create interactive data visualizations using software tools to help others better understand real-world phenomena.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Graphs Tell Stories', 'Bar Charts and Histograms' and 'A Picture Worth 1000 Rows', learners choose among chart types, build line, scatter and histogram charts and a topic-filtered four-panel dashboard.

Still to add

Data and chart choices are largely prescribed or synthetic; independently chosen charts for a real collected pattern are limited.

Where it is taught

Data and Analysis (3A-DA-12)

Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 33 'Simulate, Then Check Reality', learners build a birthday-collision simulation and compare its estimate with the exact probability and a published observed count, then evaluate consistency and limits.

Where it is taught

Data and Analysis (3B-DA-05)

Use data analysis tools and techniques to identify patterns in data representing complex systems.

We start Grade 7 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 9 chapters such as 'Graphs Tell Stories', 'Scatter Plots' and 'Correlation Basics' teach trend reading, correlation versus causation, confounders and pattern shapes, then use them to justify predictions from charts.

Where it is taught

Data and Analysis (3B-DA-06)

Select data collection tools and techniques to generate data sets that support a claim or communicate information.

We start Grade 3 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

Grade 9 chapters such as 'Introduction to Pandas', 'Loading Data' and 'The Messy Data Monster' have learners load CSV, Excel, JSON, SQL and HTML data into pandas, filter, clean and modify it and present it as charts.

Where it is taught

Data and Analysis (3B-DA-07)

Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'SDG Research-and-Build Project', learners run a supplied canteen forecast over ten days and compare it with a baseline. Grade 12 chapters teach hypothesis-first experiments and a 'Run the Method' activity.

Note

The counts used are invented, so this is hypothesis testing in a simplified simulation, not study of a real phenomenon.

Where it is taught

Impacts of Computing (1A-IC-16)

Compare how people live and work before and after the implementation or adoption of new computing technology.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Long Ago and Now', learners compare letters with video calls and maps and tills before and after computing, and explain a possible future helper.

Impacts of Computing (1A-IC-17)

Work respectfully and responsibly with others online.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Keep It Safe Online', learners order do-not-reply and show-an-adult steps for unknown online contact, and protect a photo and a password.

Impacts of Computing (1A-IC-18)

Keep login information private, and log off of devices appropriately.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Keep It Safe Online', learners choose family-only versus class-visible information and justify keeping account details private.

Impacts of Computing (1B-IC-18)

Discuss computing technologies that have changed the world, and express how those technologies influence, and are influenced by, cultural practices.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Then, Now, Next', learners compare earlier messages, maps and work routines with computing, then forecast a current innovation with evidence and uncertainty.

Impacts of Computing (1B-IC-19)

Brainstorm ways to improve the accessibility and usability of technology products for the diverse needs and wants of users.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'A Screen for Everyone', learners replace barriers on a paper interface and walk through tasks for diverse fictional users against accessibility rules.

Still to add

Testing with real users is optional; feedback from actual diverse end users is not required.

Impacts of Computing (1B-IC-20)

Seek diverse perspectives for the purpose of improving computational artifacts.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Check a Real Rule', learners fill a stakeholder want-and-worry row, swap roles, explain the opposing view and debate a requirement.

Impacts of Computing (1B-IC-21)

Use public domain or creative commons media, and refrain from copying or using material created by others without permission.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Who Owns It?', learners apply permission and licence badges, leave out an unapproved photo, credit reused work and explain creator control and reuse.

Impacts of Computing (2-IC-20)

Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI and Jobs' and 'AI Jobs of Tomorrow', learners match shrinking jobs to the new jobs beside them, sort eight jobs as mostly gone or newly created, and write and justify a future skill.

Impacts of Computing (2-IC-21)

Discuss issues of bias and accessibility in the design of existing technologies.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Text to Speech' and Grade 6 'Group AI Project', learners see how missing image descriptions leave screen-reader users with nothing, and look at a banknote reader, a grandparent-friendly interface and text size.

Impacts of Computing (2-IC-22)

Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 6 'Group AI Project', learners match strengths to team roles and sort good and struggling team habits. In Grade 7 'Aisha's Big Idea' they order a plan's weeks and meet Trello cards.

Still to add

Learners never work in a team role on a build or use a collaborative tool or version control; timelines handled are fixed plans for a character.

Where it is taught

Impacts of Computing (2-IC-23)

Describe tradeoffs between allowing information to be public and keeping information private and secure.

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI and Privacy', learners keep personal information private, never share passwords, see how harmless details combine to identify a person, and weigh sharing before posting.

Impacts of Computing (3A-IC-24)

Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Deepfakes and Misinformation', 'AI and the Future of Work' and 'AI for Global Good', learners examine deepfakes, job loss, governance, education, culture and the digital divide, including how effects differ between communities.

Impacts of Computing (3A-IC-25)

Test and refine computational artifacts to reduce bias and equity deficits.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Responsible AI Development' and 'Fairness in Machine Learning', learners choose two categories, collect fresh checks, measure group errors, record a cause, add training variation and rerun the same checks.

Where it is taught

Impacts of Computing (3A-IC-26)

Demonstrate ways a given algorithm applies to problems across disciplines.

Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 13 'GardenBed: State and Behaviour', learners implement the state and behaviour of a GardenBed model and then apply the same object pattern to a BusStop.

Where it is taught

Impacts of Computing (3A-IC-27)

Use tools and methods for collaboration on a project to increase connectivity of people in different cultures and career fields.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Team Roles, a Shared Board, a Shared Repo', learners write their own Gantt chart with owners and dependencies, then run a Repo Lab mission: two commits, a restore, a branch, and a reviewed pull request that closes an issue.

Still to add

Teammates in Repo Lab are scripted, merge conflicts are not in the Grade 9 missions, and no real team build with shared tasks runs to completion.

Where it is taught

Impacts of Computing (3A-IC-28)

Explain the beneficial and harmful effects that intellectual property laws can have on innovation.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'The Data Hunter' and 'Open Data Revolution', learners judge eight items against licences such as CC0 and Terms of Service, credit sources under charts, and match a code licence to what others may legally do.

Still to add

Grades 9-10 do not discuss how intellectual-property law affects innovation; learners apply licence terms but do not explain the trade-off between rewarding creators and giving others access.

Impacts of Computing (3A-IC-29)

Explain the privacy concerns related to the collection and generation of data through automated processes that may not be evident to users.

We start Grade 3 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Privacy and AI', learners survey what phones, apps and smart-home devices quietly collect, building on digital footprints in Grade 6 and data collection in Grade 3.

Impacts of Computing (3A-IC-30)

Evaluate the social and economic implications of privacy in the context of safety, law, or ethics.

Framework grades 9–10
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Privacy and AI', learners study rights of access, erasure, opt-out and objection under GDPR, CCPA and India's DPDP Act, and match five situations to the right that applies.

Still to add

Duties of the organisation holding the data, such as breach notification and controller roles, appear only in Grade 12; learners do not apply the law to a whole data flow.

Impacts of Computing (3B-IC-25)

Evaluate computational artifacts to maximize their beneficial effects and minimize harmful effects on society.

We start Grade 10 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and 'The Council Decision' and Grade 11 'AI in Entertainment', learners study job loss, deepfakes, governance, education and culture effects, and how they differ across communities and countries.

Where it is taught

Impacts of Computing (3B-IC-26)

Evaluate the impact of equity, access, and influence on the distribution of computing resources in a global society.

We start Grade 10 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Goes Global' and Grade 12 'India's AI Revolution', learners study AI sovereignty, low-resource languages, the digital divide, offline AI, and designing for budget phones and unbanked users.

Where it is taught

Impacts of Computing (3B-IC-27)

Predict how computational innovations that have revolutionized aspects of our culture might evolve.

We start Grade 6 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and 'The AI Revolution', learners compare the Industrial Revolution with the AI Revolution feature by feature; Grade 6 lessons trace life before and after computing with prediction tasks.

Impacts of Computing (3B-IC-28)

Debate laws and regulations that impact the development and use of software.

We start Grade 10 · framework Grade 11Framework grades 11–12
Fully covered
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision' and 'The Algorithm That Discriminated', learners critique surveillance, profiling and dark patterns, and weigh proactive versus reactive regulation and governance principles.

Where it is taught

Networks and the Internet (1A-NI-04)

Explain what passwords are and why we use them, and use strong passwords to protect devices and information from unauthorized access.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Keep It Safe Online', learners explain the account lock, sort strong and weak passwords, and compare password, fingerprint and face access.

Networks and the Internet (1B-NI-04)

Model how information is broken down into smaller pieces, transmitted as packets through multiple devices over networks and the Internet, and reassembled at the destination.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'How a Message Travels', learners reassemble packet slips, request a missing packet, route to an address and explain DNS, protocols and protected messages.

Networks and the Internet (1B-NI-05)

Discuss real-world cybersecurity problems and how personal information can be protected.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Kinds of Attack', learners tell phishing, malware, hacking, request flooding and manipulation apart, and explain their effects and proportionate protections.

Networks and the Internet (2-NI-04)

Model the role of protocols in transmitting data across networks and the Internet.

We start Grade 3 · framework Grade 6Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 8 networks chapters learners order a packet's route, judge IP addresses and read TCP resends. In Grade 3 'How a Message Travels' they order a DNS look-up, reroute round a broken path and meet locked packets.

Still to add

Network topology such as star, mesh and bus is never taught, and how design choices affect security is not examined.

Networks and the Internet (2-NI-05)

Explain how physical and digital security measures protect electronic information.

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Kinds of Attack', learners sort phishing and social engineering tricks, malware downloads, hacking defences and DDoS cases, match each attack to its effect on data, and build a protection plan that fits each attack.

Networks and the Internet (2-NI-06)

Apply multiple methods of encryption to model the secure transmission of information.

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

Grade 8 'Clients, Servers and APIs' and Grade 7 lessons mention that https encrypts a connection and that audio and biometric data are encrypted. Keys, ciphers and certificates are taught in Grade 10.

Still to add

This outcome is taught in Grade 10, later than the framework places it. Compression is not taught in any grade.

Networks and the Internet (3A-NI-04)

Evaluate the scalability and reliability of networks, by describing the relationship between routers, switches, servers, topology, and addressing.

Framework grades 9–10
Partly covered
Our Grades 9–10 Partly covered
What learners do

Learners build on Grade 8 'How a Message Crosses the Internet' (packets, addresses, routers, TCP). In Grade 10 'Keeping Secrets: Encryption' they follow a message across café Wi-Fi and study HTTPS.

Still to add

DNS and network topology are not taught, and neither is how network design affects reliability or security.

Where it is taught

Networks and the Internet (3A-NI-05)

Give examples to illustrate how sensitive data can be affected by malware and other attacks.

We start Grade 5 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'Kinds of Attack', learners meet phishing, social engineering, malware, hacking and DDoS, and match each attack to a proportionate protection. In Grade 10 they add threat modelling and defences for AI systems.

Where it is taught

Networks and the Internet (3A-NI-06)

Recommend security measures to address various scenarios based on factors such as efficiency, feasibility, and ethical impacts.

We start Grade 5 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'Kinds of Attack', learners meet phishing, social engineering, malware, hacking and DDoS, and match each attack to a proportionate protection. In Grade 10 they add threat modelling and defences for AI systems.

Where it is taught

Networks and the Internet (3A-NI-07)

Compare various security measures, considering tradeoffs between the usability and security of a computing system.

We start Grade 5 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'Kinds of Attack', learners meet phishing, social engineering, malware, hacking and DDoS, and match each attack to a proportionate protection. In Grade 10 they add threat modelling and defences for AI systems.

Where it is taught

Networks and the Internet (3A-NI-08)

Explain tradeoffs when selecting and implementing cybersecurity recommendations.

We start Grade 5 · framework Grade 9Framework grades 9–10
Fully covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'Kinds of Attack', learners meet phishing, social engineering, malware, hacking and DDoS, and match each attack to a proportionate protection. In Grade 10 they add threat modelling and defences for AI systems.

Where it is taught

Networks and the Internet (3B-NI-03)

Describe the issues that impact network functionality (e.g., bandwidth, load, delay, topology).

Framework grades 11–12
Partly covered
Our Grades 11–12 Partly covered
What learners do

In Grade 11 'From Prototype to Planet-Scale' and 'Beyond the Model', learners study load balancing, caching, edge deployment and redundancy. Packets, protocols and addressing are taught earlier in Grade 8.

Still to add

Packets, protocols, IP addressing, DNS and network topology are not taught at Grades 11-12, and network design for reliability is covered only as service redundancy.

Networks and the Internet (3B-NI-04)

Compare ways software developers protect devices and information from unauthorized access.

Framework grades 11–12
Partly covered
Our Grades 11–12 Partly covered
What learners do

In Grade 11 'The Silent Failures', learners match attack traces such as abuse traffic and prompt injection to proportionate defences. Grade 12 'Smart Grid Hero' covers a grid cyber attack and defence practices.

Still to add

Malware, ransomware, DDoS, botnets and social engineering beyond email are not explained, and learners do not write their own proportionate security plan.

Where it is taught

Computing Systems (1A-CS-01)

Select and operate appropriate software to perform a variety of tasks, and recognize that users have different needs and preferences for the technology they use.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Parts and Apps', learners name the roles of chip, storage and apps, trace how components communicate and choose safe responses to app, Wi-Fi and screen faults.

Computing Systems (1A-CS-02)

Use appropriate terminology in identifying and describing the function of common physical components of computing systems (hardware).

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Parts and Apps', learners name the roles of chip, storage and apps, trace how components communicate and choose safe responses to app, Wi-Fi and screen faults.

Computing Systems (1A-CS-03)

Describe basic hardware and software problems using accurate terminology.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Parts and Apps', learners name the roles of chip, storage and apps, trace how components communicate and choose safe responses to app, Wi-Fi and screen faults.

TeachAI / CSTA AI Learning Priorities for All K-12 Students (2024)

Humans and AI — The Nature of Humans and of AI

Compare and contrast the nature of humans versus the nature of AI (e.g., living versus nonliving).

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Can a Machine Think?' and 'Rules or Examples?', learners compare a child's thinking and checking steps with a machine's data, rules and output, and challenge the label 'smart'.

Compare and contrast the ability of humans and of AI to perform various tasks and serve in various roles.

We start Grade 1 · framework Grade 3Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'AI at Work', learners see that a calculator beating a person is fast but not clever, map robot parts to body parts, and learn that reading symbols is not understanding.

Identify the assumptions inherent in the operation and output of an AI model and how these assumptions might have different implications for different people.

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Good Examples vs Bad Examples' and 'Image Classification', learners see cases such as a classifier that spots snow instead of the animal, and sort examples of who a model's data leaves out.

Still to add

Assumptions appear case by case; learners do not list a model's built-in assumptions or trace which users each one fails.

Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.

Framework grades 9–12
Partly covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'Does It Understand?', learners judge the imitation game, sort evidence for and against a chatbot understanding, weigh robot rights, and build an argument card with a claim, reasons and the other side's best reason.

Our Grades 11–12 Partly covered
What learners do

Grade 12 'Can AI Really Think?' and 'The Unsolved Mysteries' present the understanding debate, machine consciousness and AI rights and personhood as reading and reveal cards, with one true/false item.

Still to add

Learners do not take a position on machine understanding, consciousness or rights or weigh evidence against criteria. The Turing test is not revisited after Grade 6.

Humans and AI — The Human Role in Creating AI

Understand that AI is a tool created by humans to make decisions or to generate something (e.g., an image).

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'What Is AI?', 'Machines That Hear' and 'Teach Byte', learners see that a parrot can say hello without meaning it, a robot's sad face is not sadness, and reading is not understanding.

Describe the role of humans in the creation of AI.

We start Grade 2 · framework Grade 3Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'AI at Work' and Grade 4 'Thinking Well', learners see that a robot with a sad face is not sad, that machines cannot know, choose or care, and that chat AI writes feelings it does not have.

Describe the roles that humans play (including in data curation and labeling) in creating and refining AI models.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Jobs of Tomorrow', learners map data scientist, AI trainer, ethics officer, safety tester and ML engineer to pipeline steps. The ethics reviewer asks who is treated unfairly.

Evaluate and analyze the roles of humans and human decision-making in the creation of AI.

We start Grade 6 · framework Grade 9Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 9 'Welcome, AI Engineers!' and Grade 10 'Responsible AI Development' cover the AI engineer role, labelling cost, bias auditing, model cards, diverse teams, accountable owners and monitoring after launch.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Ethics in Practice', learners order an eight-step ethical review. Other chapters name the roles in a machine learning pipeline, human approval points and the AI ethics reviewer career.

Humans and AI — The Choice to Use AI

Evaluate when AI is or is not a helpful resource to carry out a task.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Being Kind' and Grade 4 'AI and Jobs', learners decide between a calculator and AI for a simple sum, which answers are fine to use versus check first, and asking AI to explain rather than do.

Debate when humans should or should not use AI to perform a specific task.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Hammer', learners run a suitability checklist over data, complexity, cost and ethics, sort green-light from red-flag cases and write a justification.

Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.

We start Grade 8 · framework Grade 9Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery', learners use an AI suitability checklist, overkill test and cost-benefit analysis; Grade 10 'Spark of Inspiration' asks whether a project needs AI.

Our Grades 11–12 Fully covered
What learners do

In Grade 8 lessons such as 'The AI Hammer' and 'The Wrong Solution', learners use an AI suitability checklist and overkill warnings; Grades 11-12 add sorting needs-AI versus simple-rule cases and judging reinforcement learning against direct programming.

Representation and Reasoning — Understanding Representation

Understand how a representation is an abstraction that focuses on some features and leaves others out.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Make a Model', learners draw a room map for a purpose and justify the features kept and left out.

Understand that representation includes modalities (text, speech, audio, image, video) and symbolic mappings (text, graphs).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Making Decisions - IF-THEN' and 'Decision Tree Fun', learners rebuild real decisions as decision trees written as IF-THEN-OTHERWISE rules. Grade 8 'Folders Inside Folders' matches data to nesting structures.

Describe how current AI models (e.g., LLMs) use data representation.

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 10 'Word Embeddings' and 'Face Recognition' and Grade 9 'Numbers in AI' cover dense embeddings, embedding space, cosine similarity, word arithmetic, feature vectors and face embeddings.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'When AI Needs to Look Things Up' and Grade 10 'Word Embeddings' teach embeddings, a shared embedding space and cosine similarity, with similar items sitting close, and reuse them for retrieval and recommenders.

Where it is taught

Representation and Reasoning — Creating a Representation

Create a representation of a physical object (e.g., line art drawing).

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Maps, Paths and Trees', learners draw a room map, keep the door and board, and explain why a moving bag is left out.

Create an abstract representation of a physical system that can be used to solve a problem (e.g., a map).

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Make a Model', learners draw a room map for a purpose and justify the features kept and left out.

Create and evaluate different abstract representations (e.g., subway map).

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

Grade 7 'Pixels and Pictures' shows a ready-made 8x8 smiley and the trade-off of pixel detail, and Grade 6 chapters have learners sort robot design choices. An optional suggestion invites them to shade squares to draw a shape.

Still to add

Learners do not make a representation in the app or say which features it keeps and leaves out; the shading idea is an untracked suggestion.

Where it is taught

Choose and use an appropriate representation of complex data for processing by an AI algorithm.

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 10 'Word Embeddings' and 'Face Recognition' and Grade 9 'Numbers in AI' cover dense embeddings, embedding space, cosine similarity, word arithmetic, feature vectors and face embeddings.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'When AI Needs to Look Things Up' and Grade 10 'Word Embeddings' teach embeddings, a shared embedding space and cosine similarity, with similar items sitting close, and reuse them for retrieval and recommenders.

Where it is taught

Representation and Reasoning — Reasoning

Explain how binary choices (e.g., up/down, on/off, under/over) can be used to make decisions that lead to a specific goal by either a human or a machine.

Framework grades 1–2
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Clue Questions', learners choose a question chain from a bank and run it on animal data.

Still to add

The app offers a constrained first-yes chain; designing branching rules independently is not fully established.

Train a model that can make decisions based on defined criteria (e.g., a dichotomous key to determine which movie to see).

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'A Table of Clues, a Key With Two Sides' and 'Build as a Team', learners build a two-sided key, trace both branches, compare valid waste-rule chains against test data, and revise a question after a failure.

Identify the kinds of AI models (e.g., classifier, predictor, recommender) people interact with in their daily lives.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Unit 3 Review - AI Scavenger Hunt!', learners sort ten everyday examples as recognise or predict. Classifiers, predictors and recommenders each get a chapter with their limits stated.

Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Classification Problems' and 'Which Algorithm Wins?', learners study classifiers, predictors and recommenders with worked strengths and limits, then compare them for selection.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'How Does It Know?' and Grade 11 'AI in Retail' give classifiers, forecasters and recommenders dedicated chapters, comparing strengths and limits such as cold start and filter bubbles. Grade 9 chapters cover classification and regression.

Where it is taught

Machine Learning — Sensing

Compare and contrast human sensing with computer sensors.

Framework grades 1–2
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Notice, Then Do' and Grade 1 'Parts and Apps', learners identify input parts of devices and compare tipping and shaking signals with human sensing.

Still to add

Learners are not explicitly asked to locate the camera or microphone on their own device.

Describe various ways that a human might interact with an AI system (e.g., through voice, text, or gestures).

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'A Way In for Everyone', learners match voice, text, gesture and image modes to named users' access needs and defend a saved mode-and-reason card.

Use sensors to collect data, and then train an AI model using the sensor data.

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Python Lab Module 48 learners read live phone motion and write code that reacts to gestures taught in the Motion trainer. Grade 7 'Lily's Big Moment' gives Teachable Machine setup steps for camera projects.

Still to add

Microphone data is never used to train a model, camera training happens only outside the app, and no Grade 6-8 chapter sends learners to the motion tools.

Where it is taught

Using sensor data (e.g., from autonomous vehicles), train an AI model.

We start Grade 7 · framework Grade 9Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery' and Grade 7 'Aisha's Big Idea', learners capture image, sound and pose classes with the camera and microphone, train a model and export it into a Scratch app.

Our Grades 11–12 Fully covered
What learners do

In Grade 7 chapters such as 'Lily's Big Moment' and Grade 8 'Maya's Discovery', learners photograph their own classes, train image, sound and pose models in Teachable Machine and export a model into a working Scratch app.

Where it is taught

Machine Learning — Data

Explore how AI models learn from data.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Learning From Examples', learners watch Byte guess better as it sees two, then ten cats, and then guess new pictures it has not seen.

Explore the relationship between the properties of training data (e.g., size, features, biases) and an AI model's output.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Data' and 'Counting, Charts and Scores' and Grade 4 'Wrong, and How Sure', learners ask who collected the data, whose it is, whether there was consent, what is missing, how many examples there are and what failures follow.

Describe the ways that bias can be introduced and mitigated in an AI model.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Refinement Week!' and 'Kabir's Excitement', learners train a deliberately imbalanced classifier, fix only the weakest class, re-test and record whether the gap closed.

Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Data Hunter' and 'The Biased Hiring Model' and Grade 10 'The Data Hunt', learners evaluate source, size, coverage, consent, quality versus quantity and bias, using five data-quality signs and how they shape behaviour.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Data is Everything', Grade 11 'Finding Your Data' and Grade 9 chapters such as 'The Unsinkable Ship', learners evaluate source, coverage, cleanliness, consent and bias and judge how data properties will shape model behaviour.

Where it is taught

Machine Learning — How Computers Learn

Understand how computers learn from data and patterns.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Learning From Examples', learners watch Byte guess better as it sees two, then ten cats, and then guess new pictures it has not seen.

Investigate how AI models learn by using data (including why examples and non-examples are required in training sets) and algorithms to find patterns and generate output.

We start Grade 2 · framework Grade 3Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Training', Byte is shown examples round after round, tested on unseen pictures, and predicts on new input from the patterns it found.

Create and evaluate an appropriate AI algorithm (e.g., a decision tree classifier) to accomplish a task.

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 7 'Features and Attributes', learners work out nearest-neighbour placement by hand, and 'Decision Trees Deep Dive' covers tree design. Create tools such as Pixel's models let learners train a tree.

Still to add

Learners never choose an algorithm for a task or justify the choice, and the k-NN and tree activities are never compared on one task.

Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Which Algorithm Wins?', learners compare k-NN and a tree on the same cross-validation folds, choose with a reason, and evaluate k-NN on a held-out real Iris split.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

Grade 9 chapters such as 'K-Nearest Neighbors', 'Decision Trees in ML' and 'Linear Regression' have learners implement and compare k-NN and decision trees with scikit-learn, with model selection and justification taught explicitly.

Where it is taught

Machine Learning — Building and Using AI Models

Use data to construct a model for making decisions (e.g., a decision tree to determine what to wear based on the weather).

Framework grades 1–2
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Clue Questions', learners choose a question chain from a bank and run it on animal data.

Still to add

The app offers a constrained first-yes chain; designing branching rules independently is not fully established.

Using a dataset, develop an AI model to classify inputs.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Change the Examples, Change the Guess' and Grade 5 'Program a Gadget', learners train a motion classifier from labelled movements, test fresh movements and add varied examples after a miss.

Using a dataset and a machine learning pipeline, develop an AI model, and consider the impact of the model on various users.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 chapters such as 'Lily's Big Moment' and 'Refinement Week!', learners plan data, shoot balanced photos, train in Teachable Machine, hold out a test set, and report accuracy per category and failures.

Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.

Framework grades 9–12
Mostly covered
Our Grades 9–10 Fully covered
What learners do

In the Grade 9 'Science Fair Star' capstone and Python Lab Module 44, learners audit and curate data, state assumptions, fit a baseline, make one logged change and run one final evaluation.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'SDG Research-and-Build Project', learners frame a problem, check data sources, set a baseline, run a forecast and pitch the project. In the Grade 12 capstone they train a model, test it on others and defend a model card.

Still to add

Assumptions are written down at only two steps, impact and evaluation, rather than in a log covering every step.

Where it is taught

Ethical AI System Design and Programming — Ethical Design Criteria

Investigate an example of AI decision making, considering if it is fair, as well as what it means to be fair.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'This Really Happened', learners investigate a documented exam-grading case, choose a meaning of fairness and defend it with case facts against a partner's challenge.

Explore strategies to turn ethical considerations into actions, such as mitigating bias in datasets.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Refinement Week!' and 'Kabir's Excitement', learners train a deliberately imbalanced classifier, fix only the weakest class, re-test and record whether the gap closed.

Evaluate an AI model (e.g., using a model card) to determine the model's features as well as its biases, explainability, fairness, privacy, accuracy, and transparency.

Framework grades 9–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Responsible AI Development', learners train a companion, measure accuracy, weakest category and balance, write a four-part model card and say what they would change to raise the weakest number.

Still to add

The taught card and the capstone have no explainability field and no privacy field.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Capstone Exhibition', learners train a drawing classifier, test it on other people's drawings, explain actual misses, and write an eight-section model card including explainability and privacy, then defend its headline number to a judge.

Where it is taught

Ethical AI System Design and Programming — Ethical Evaluation of AI Systems

Explore how an AI system can help and harm different groups at the same time.

Framework grades 1–2
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Fair and Good for Everyone?', learners compare who a voice helper helps and who its training coverage leaves out.

Still to add

The case is fictional and does not use documented evidence of unequal impact by demographic group.

Investigate examples of AI, considering differences in experience by different people in different contexts.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'This Really Happened', learners compare documented speech-recognition error figures across groups and explain who bears more mistakes.

Describe the properties, biases, and assumptions of various kinds of AI models (e.g., classifier, predictor, recommender).

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Good Examples vs Bad Examples' and 'Image Classification', learners see cases such as a classifier that spots snow instead of the animal, and sort examples of who a model's data leaves out.

Still to add

Assumptions appear case by case; learners do not list a model's built-in assumptions or trace which users each one fails.

Evaluate the design, motivation, outcomes, and potential impacts of AI systems using ethical design criteria and/or ethical frameworks.

Framework grades 9–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Building Your AI Ethics Framework' and Grade 9 'The Biased Hiring Model', learners place a hiring AI in EU AI Act risk tiers and name the missing UNESCO principle, then write principles their own project meets.

Still to add

Applications to real cases are fixed-answer with pre-written feedback; learners never write a justified judgement on a real case.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision' and 'The Ethics Question', learners apply consequentialist, deontological, virtue and care frameworks to a lethal-drone dilemma, and apply FATE and IRB frameworks to cases.

Ethical AI System Design and Programming — Ethical Creation of AI Systems

Describe an AI design process that considers the impact on end users and others who are impacted by the AI system.

Framework grades 3–5
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Demo Day: My Own Helper' and 'The Helper's Report Card', learners record affected users, consent, limits and review responsibility, and make test-driven changes.

Still to add

Ethical criteria are not explicitly revisited at every step of the learner's own model build.

Create a program using available AI tools, AI plugins, APIs, and/or AI models, with ethical considerations for the model's end users and others impacted: fairness, bias, and accuracy, and then create a model card.

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 7 'Teaching by Showing' and Grade 8 'The Global Goals Project', learners choose their own categories, draw examples and train a small classifier. Python Lab Module 48 and the Motion Trainer add gesture models.

Still to add

Building an application around the model is not guided, Scratch AI extensions are only described, and the Raise Your AI missions can be completed by tapping reveals.

Train, iteratively improve, and then develop a model card for an AI model with ethical considerations for end users and others impacted: fairness, bias, safety, security, intellectual property, privacy, robustness, explainability, accuracy, transparency, and accountability.

Framework grades 9–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Responsible AI Development', learners train a companion, measure accuracy, weakest category and balance, write a four-part model card and say what they would change to raise the weakest number.

Still to add

The taught card and the capstone have no explainability field and no privacy field.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Capstone Exhibition', learners train a drawing classifier, test it on other people's drawings, explain actual misses, and write an eight-section model card including explainability and privacy, then defend its headline number to a judge.

Where it is taught

Societal Impacts of AI — Individual Impacts

Identify where AI is being used in daily life.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Machines That Talk' and 'What Is AI?', learners trace a day through a smart speaker, news app, map app, shop, game character and TV recommender, and match each to its job.

Explore how one's actions may result in the collection of data.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'AI and School Work', learners see that searches, photos and likes leave traces on company servers, that photos carry metadata, and that their posts become examples a machine learns from.

Explore the tradeoffs related to human agency (including privacy, safety, creativity, autonomy, and intellectual property) when AI is used.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'You Control AI' and Grade 8 'Liam's Art Dilemma', learners sort tool-versus-boss habits, learn AI has no feelings, debate artists' work used without consent, and name lived experience as what AI cannot supply.

Evaluate how AI use impacts an individual's decision making and other behavior.

We start Grade 8 · framework Grade 9Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work' and 'Privacy and AI', learners see AI as human-led collaboration and discuss privacy, autonomy and creativity tradeoffs; Grade 8 covers AI-art copyright and ownership.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and 'Art Partner' and Grade 11 'AI in Education', learners see AI framed as human-led and examine tradeoffs to privacy, autonomy, creativity and intellectual property.

Where it is taught

Societal Impacts of AI — Societal Impacts

Explore how some people use AI in their jobs and in their communities.

Framework grades 1–2
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Helpers and Jobs', learners match builder and checker tasks to skills and choose a personal job-and-skill card.

Explore ways in which some jobs involve the creation and/or use of AI.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 4 lessons such as 'Who Was Left Out', learners match roles such as engineer, designer, checker, safety person, tester and data collector to their work, meet a fairness checker and robot mender, and pick a job for themselves.

Identify the intended and unintended impacts of AI on society, including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Real vs Fake: Deepfakes', 'AI and Jobs' and 'AI in 2030' and Grade 7 'Computer Vision in the Real World', learners study deepfakes, job change, surveillance and proctoring backlash, and the digital divide across communities.

Evaluate the intended and unintended impacts of AI on society (e.g., deep fakes, job loss), including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.

Framework grades 9–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Deepfakes and Misinformation', 'AI and the Future of Work' and 'AI for Global Good', learners examine deepfakes, job loss, governance, education, culture and the digital divide, including how effects differ between communities.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and 'The Council Decision' and Grade 11 'AI in Entertainment', learners study job loss, deepfakes, governance, education and culture effects, and how they differ across communities and countries.

Where it is taught

Societal Impacts of AI — Environmental Impacts

Explore the impact of AI on the environment.

Framework grades 3–5
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'AI's Footprint', learners account for materials, power and water, chart suitable tools, plan an evidence-based footprint message for an audience and say it to a friend or adult.

Investigate the positive and negative environmental impacts of AI (e.g., minimizing deforestation via application of AI, energy use by AI).

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 5 'Being Kind' and Grade 6 'AI in 2030' and 'AI for Good', learners cover energy cost, data centres and model right-sizing, and weigh a project's benefit against the electricity and water it uses.

Design ways to minimize negative environmental impacts of AI and communicate those ways to others.

Framework grades 9–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 chapters such as 'GPU vs CPU', 'Early Stopping' and 'Model Optimization', learners read quantified energy, water and hardware figures and levers such as smaller models and early stopping, with a couple of multiple-choice energy items.

Still to add

Learners never design or communicate a way to reduce AI's footprint, accounting is mostly read, and water is never computed; the task exists in Grades 8 and 11.

Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Smart Grid Hero', learners work through chips, cooling water and a CO2 formula, and compute a footprint by hand; in Grade 11 'SDG Research-and-Build Project' they use a footprint calculator for a school-wide chatbot.

Still to add

Learners do not design and communicate their own footprint-reduction plan with before and after numbers, water is quoted but not estimated, and one sorting item has a wrong answer key.

Where it is taught

CSTA PK-12 Computer Science Standards, 2026 Revision (in progress; anticipated release summer 2026)

Algorithms & Design

AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.

Framework grades K-12 (all bands)
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Rules or Examples?', learners choose a written rule for an exact doorbell task and examples for varied cat pictures, and explain why.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Robots' and 'AI at Work', learners sort machines as told-the-steps or shown-examples and see that nobody could write every step for spotting a cat.

Our Grades 6–8 Fully covered
What learners do

Grade 8 'What Makes AI Intelligent?' is a whole chapter on rule-based versus learning-based AI with app sorting. Learners also sort jobs into needs-a-rule or needs-training.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Welcome, AI Engineers!', learners sort eight tasks into traditional programming or machine learning and get feedback on which approach suits each.

Our Grades 11–12 Fully covered
What learners do

Grade 8 chapters such as 'What Makes AI Intelligent?' teach rule-based versus learning-based AI with code. Grade 12 sorts product ideas into needs-AI versus needs-a-rule.

Where it is taught

Programming

Students read, evaluate, and critique AI-generated code as part of the Programming strand.

Framework grades K-12 (all bands)
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Check Byte's Work', learners read a supplied machine command sequence, spot a missing pickup and explain the repair.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Check Byte's Code, Leave Good Notes', learners trace supplied AI command code, predict the unsafe final state, repair it and document expected versus actual cases.

Our Grades 6–8 Fully covered
What learners do

In Python Lab 'Module 10: Errors and Assertions', learners run failing tests on an AI-written average function, diagnose its fixed-divisor error, repair it and rerun cases of varying length.

Where it is taught
Our Grades 9–10 Fully covered
What learners do

In Python Lab 'Module 44: ML Capstone', learners debug an AI-written data pipeline, repairing a pandas API call and a preprocessing leak, fitting only training rows and recording both fixes.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'AI That Writes Code', learners cover AI coding assistants, including hallucinated APIs, outdated patterns, licence risk, security checks and developer responsibility for shipped code.

Data & Analysis

Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.

Framework grades K-12 (all bands)
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Which Pictures Are Missing?', 'Label It Right' and 'A Helper for Everyone', learners judge size, coverage and labels, choose their own or consented pictures for a build and connect missing kinds to errors.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Data' and 'Counting, Charts and Scores' and Grade 4 'Wrong, and How Sure', learners ask who collected the data, whose it is, whether there was consent, what is missing, how many examples there are and what failures follow.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Teach the Teacher', Grade 7 'Good Examples vs Bad Examples' and Grade 8 'Data for AI', learners judge data on clarity, accuracy, representativeness, variety, consent and bias, and match each flaw to the model behaviour it causes.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Data Hunter' and 'The Biased Hiring Model' and Grade 10 'The Data Hunt', learners evaluate source, size, coverage, consent, quality versus quantity and bias, using five data-quality signs and how they shape behaviour.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Data is Everything', Grade 11 'Finding Your Data' and Grade 9 chapters such as 'The Unsinkable Ship', learners evaluate source, coverage, cleanliness, consent and bias and judge how data properties will shape model behaviour.

Where it is taught

Systems & Security

Students examine security implications and environmental impacts of AI systems within Systems & Security.

Framework grades K-12 (all bands)
Partly covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Big Computers Get Hot', learners trace the hardware, power and cooling that remote computers need, choose a saver card and communicate a family action.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'AI's Footprint', learners account for materials, power and water, chart suitable tools, plan an evidence-based footprint message for an audience and say it to a friend or adult.

Our Grades 6–8 Fully covered
What learners do

In Grade 5 'Being Kind' and Grade 6 'AI in 2030' and 'AI for Good', learners cover energy cost, data centres and model right-sizing, and weigh a project's benefit against the electricity and water it uses.

Our Grades 9–10 Partly covered
What learners do

In Grade 10 chapters such as 'GPU vs CPU', 'Early Stopping' and 'Model Optimization', learners read quantified energy, water and hardware figures and levers such as smaller models and early stopping, with a couple of multiple-choice energy items.

Still to add

Learners never design or communicate a way to reduce AI's footprint, accounting is mostly read, and water is never computed; the task exists in Grades 8 and 11.

Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Smart Grid Hero', learners work through chips, cooling water and a CO2 formula, and compute a footprint by hand; in Grade 11 'SDG Research-and-Build Project' they use a footprint calculator for a school-wide chatbot.

Still to add

Learners do not design and communicate their own footprint-reduction plan with before and after numbers, water is quoted but not estimated, and one sorting item has a wrong answer key.

Where it is taught

Computing & Society

Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.

Framework grades K-12 (all bands)
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Can a Machine Think?', learners order a simplified timeline from a question to a room-sized machine, the phone and generative AI.

Still to add

The Dartmouth naming, AI winters and the big-data and deep-learning shift are not in this timeline.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'The Story of AI', learners order seven dated milestones and explain AI winters and the difference between a question and a demonstrated capability.

Our Grades 6–8 Fully covered
What learners do

Grade 6 'Birth of Computers', 'AI Gets Its Name', 'The AI Winter' and 'The Deep Learning Revolution' cover the Turing test, Dartmouth 1956, AI winters, AlexNet and ChatGPT, with repeated oldest-to-newest ordering games.

Our Grades 9–10 Fully covered
What learners do

Grade 6 'What is AI?' covers the Turing test, Dartmouth 1956, AI winters, Moore's law, deep learning and large language models. In Grade 10 'The First Learning Machine!', learners order milestones chronologically.

Our Grades 11–12 Fully covered
What learners do

Grade 6 chapters cover the Turing test, Dartmouth 1956, AI winters and deep learning. Grade 11 'The Grand Finale!' adds ordering Transformer, generative AI and reinforcement learning milestones.

School and system rules (not rated)

These apply to schools and education systems (hours, delivery, assessment, staffing, age rules). A course cannot meet them on its own.

  • A dedicated high-school specialty pathway offers deeper study of AI for students pursuing it beyond the core sequence.

Bring the course to your school or region

Schools, school groups, resellers and partners: we will walk you through the alignment for your board and the lessons behind it.

For schoolsFor partners and resellersContact us

See how the course maps to the other 13 curricula on the curriculum alignment overview, or browse the course grade by grade.

Our own reading of our own course, ratings reviewed 22 October 2026. It is not an endorsement, certification or accreditation by any authority named here, and it makes no claim about learning results.