Curriculum alignment · United States

AI4K12 Five Big Ideas in AI (US, working drafts)

We checked the LittleAIMaster Grade 1–12 AI course against all 44 rated requirements of this framework: 12 fully covered, 17 mostly covered, 15 partly covered. Open a requirement to see what learners do and the exact lessons.

66%
Fully covered 12Mostly covered 17Partly covered 15of 44 requirements1 taught earlier than the framework asks

Source documents (1)

Five Big Ideas in Artificial Intelligence
AI4K12 Initiative (joint project of AAAI and CSTA, NSF-funded)
Working drafts, released on a rolling basis 2020-2022 (Big Idea 1 v.0.1 2020-05-28; Big Idea 2 v.0.1 2021-06-28, links updated 2021-07-30, later v3 PDF variant also posted 2022-03; Big Idea 3 v.0.1 2020-11-19; Big Idea 4 v.0.2 2022-03-14; Big Idea 5 v.0.1 2022-12-22)

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Every AI4K12 (US) requirement, rated

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Open a requirement to see what learners do and the exact lessons.

Showing 44 of 44 requirements

Five Big Ideas in Artificial Intelligence

Big Idea 1 - Perception: Sensing (Living Things)

Progress from identifying the five human senses and sensory organs (K-2), to comparing how different animals perceive the world differently than humans, e.g. sonar, ultraviolet vision, colorblindness (3-5), to giving examples of how humans combine information from multiple sensory modalities to resolve ambiguous signals (6-8); AI4K12 marks 9-12 N/A for this topic since it is considered adequately covered by the lower bands (leaving deeper biology/psychology to other courses).

Framework grades 1–2, 3–5, 6–8
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Animal Senses' and 'Two Senses', learners identify sense organs, compare animal senses, and combine touch and smell to solve a mystery.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Senses: Mine, Animals', Machines'', learners identify sense organs, compare animal differences, and combine touch and smell evidence to work out an ambiguous object.

Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Senses: Mine, Animals', Machines', learners match each sense organ to what it catches, match animals to their special senses (owl, bat echoes, bee colours), and settle a fooled sense with a second one, as with radar and camera in fog.

Where it is taught

Big Idea 1 - Perception: Sensing (Computer Sensors)

Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
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.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Senses: Mine, Animals', Machines'', learners look around their own tablet or phone for camera and microphone openings, find the hidden motion sensor, and compare human and machine sensing strengths.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Input and Output', learners sort nine devices as input, output or both and match six input devices to what each detects. Grade 8 and Grade 7 chapters name smart-home sensors and compare human and computer sensing.

Our Grades 9–10 Fully covered
What learners do

Grade 2 'Steps, Patterns and Fixing' covers the camera and microphone as sensors, Grade 7 'How AI Sees' compares human and computer vision, and Grade 8 'Maya's Discovery' covers IoT sensor types.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'AI in the Real World' matches five sensors to what they measure, and Grade 12 'AI Eyes' compares human and computer vision. Grade 7 'How AI Sees' covers vision in depth.

Big Idea 1 - Perception: Sensing (Digital Encoding)

Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.

Framework grades 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Pictures and Sounds as Numbers' and Grade 5 'Everything Is a Code', learners encode and decode letters and binary quantities, rebuild pixels and read successive sound samples.

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.

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
Our Grades 11–12 Fully covered
What learners do

Learners study bits, bytes and place value in Grade 6 'How Computers Count - Binary', pixels, RGB and hex in Grade 7 'Pixels and Pictures', numeric encoding in Grade 9, and spectrograms in Grade 12.

Where it is taught

Big Idea 1 - Perception: Processing (Sensing vs. Perception)

Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'From Sound to Sentence' and 'Notice, Then Do', learners tell captured signals from interpreted meaning, collect movement signals, and demonstrate a local motion classifier on new examples.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Clue Finders' and 'Change the Examples, Change the Guess', learners tell captured signals from interpreted meaning, collect movement signals, and demonstrate a local motion classifier on fresh examples.

Our Grades 6–8 Fully covered
What learners do

Learners tap whether eight smartwatch readings come from a sensor or software, split detection from recognition, and train and run object and gesture recognisers in Grade 7 'Object Recognition' and Grade 8 'Maya's Discovery'.

Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Train, Measure, Read the Card', learners teach a doodle classifier and read its accuracy and confusion matrix. In Grade 9 'Signal or Guess?', they sort eight outputs as measured signal or inferred guess.

Still to add

Sensing and perception are never named as two stages of a perception system, and the demo is doodle recognition, not speech transcription or photo object recognition.

Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'AI in the Real World', learners sort a driverless car's work into sense, perceive, plan and act. In the capstone, they train a drawing classifier and test it on other people's drawings.

Still to add

The only perception tool run is drawing recognition, which can be skipped; no speech transcription is run, and learners do not label which part is sensing and which is perception.

Big Idea 1 - Perception: Processing (Feature Extraction)

Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Tiny Squares', learners recognise a colour-change edge in simple rows of pixels.

Still to add

Learners do not run a feature detector across a signal or extract texture or sound features in Grades 1-2.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Clue Finders', learners apply a colour-change rule across a row of dots and compare texture and sound-formant clues.

Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Clue Finders', learners find an edge as a colour change along a row of dots, apply a one-rule edge detector to rows, and match textures and sound fingerprints. Grade 7 then walks the full edge pipeline with Sobel.

Our Grades 9–10 Fully covered
What learners do

Grade 10 'Convolution Operations' and 'Introduction to CNNs' cover image features, and in Python Lab Module 45 learners generate sound, compute FFT features and plot a spectrogram.

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

In Python Lab Module 45, learners complete a convolution and run an edge kernel. Grade 11 'Paint Like Picasso' has Gram-matrix texture features by hand, and Module 49 'Find the Formants' extracts sound features from synthetic vowels.

Big Idea 1 - Perception: Processing (Abstraction Pipeline - Language)

Progress from describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'From Sound to Sentence', learners order capture, sounds, words and sentence, and use the sentence to settle a 'sea' versus 'see' ambiguity.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Layer by Layer: Seeing and Hearing', learners order wave, sounds, words, sentence and meaning, and use ice-cream versus I-scream context to settle ambiguous sounds.

Our Grades 6–8 Fully covered
What learners do

In Grade 8 'Aisha's Helpful Project' and Grade 7 'Voice Assistants Explained', learners order a five-step speech pipeline (capture, filter, extract, match, context), with context resolving ambiguity.

Our Grades 9–10 Mostly covered
What learners do

In Grade 2 'From Sound to Sentence', learners follow sound caught as a wave of numbers, into small sound pieces, then words, then a sentence, and see the sentence choose between same-sounding words such as pear and pair.

Still to add

Each step is a separate picture task; learners do not yet trace the whole chain from sound to sentence themselves.

Our Grades 11–12 Covered earlier · Grade 5
What learners do

In Grade 5 'Layer by Layer: Seeing and Hearing', learners order wave, small sounds, words, sentence and meaning, then use the sentence to pick between 'ice cream' and 'I scream', seeing a higher layer fix a lower-layer mistake.

Where it is taught

Big Idea 1 - Perception: Processing (Abstraction Pipeline - Vision)

Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Tiny Squares', learners order pixels, edges, shape and object, and tell foreground, background and hidden objects apart.

Still to add

Surfaces and contours are simplified into shape; learners recognise the pipeline rather than build an image-perception model.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Layer by Layer: Seeing and Hearing', learners trace dots, edges, outlines, surfaces and objects, and test figure and background and occlusion with a paper shape.

Our Grades 6–8 Fully covered
What learners do

Grade 7 'Pixels and Pictures', 'Finding Edges and Shapes' and 'Object Recognition' take learners from pixels to edges to shapes to objects, with a seven-step photo-to-shape pipeline, occlusion cases and subject and background separation.

Our Grades 9–10 Fully covered
What learners do

Grade 7 'How AI Sees' covers pixels, edges and shape grouping with occlusion. In Grade 10 'Introduction to CNNs' and 'Deep vs Shallow Networks', learners order edges, shapes and faces in a hierarchy.

Our Grades 11–12 Fully covered
What learners do

Grade 7 chapters such as 'Pixels and Pictures' and 'Finding Edges and Shapes' cover pixels, edges, false and missed edges, shape grouping and occlusion. Grade 10 'Introduction to CNNs' covers features built up layer by layer.

Where it is taught

Big Idea 1 - Perception: Domain Knowledge (Types)

Progress from describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'What Byte Needs to Know', learners infer a ball or fruit from its setting and identify the captioned examples that would supply that background knowledge.

Still to add

Background knowledge is shown through supplied scene cards; learners do not inspect records in a real dataset.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'What Byte Doesn't Know', learners identify scene knowledge in seen and inferred pairs and build caption-labelled examples to cover missing rainy-street contexts.

Still to add

The caption-labelled examples are supplied illustrations, not a real dataset or a scene model that uses them.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'What Byte Doesn't Know', learners sort what a story says from what everybody just knows, trace that knowledge to captions people wrote, spot facts rarely written down, and add captioned photos to fill a gap.

Our Grades 9–10 Covered earlier · Grade 4
What learners do

In Grade 4 'What Byte Doesn't Know', learners see that machines get world knowledge from captioned photo sets, spot facts that rarely get written down, and add captioned examples to fill a gap. Grade 2 shows place as a clue.

Our Grades 11–12 Covered earlier · Grade 4
What learners do

In Grade 4 'What Byte Doesn't Know', learners sort what a picture shows from what the world supplies, see that machines get that knowledge from human-written captions in a photo set, why obvious facts are rarely written, and add missing examples.

Where it is taught

Big Idea 1 - Perception: Domain Knowledge (Inclusivity)

Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Fair and Good for Everyone?', learners explain why too few examples of Gran's language or a child's voice can increase mistakes.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'What I Now Know' and Grade 4 'AI and Creativity', learners see that languages with fewer online pages translate worse and that hot-climate or child-only training sets fail others, so varied voices are needed.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Can Be Unfair' and Grade 7 'AI and World Languages', learners study Gender Shades, accents, children's speech, darker skin tones, low-resource languages and uneven text recognition across scripts.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Face Recognition' and 'Multilingual NLP', learners study skin-tone accuracy gaps, low-resource languages and the digital divide. Accent bias in speech data is covered in Grade 8.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Voice Revolution' and 'AI Eyes', learners study accent and dialect bias in speech, skin-tone failures in vision and low-resource language gaps, each with its data cause.

Where it is taught

Big Idea 2 - Representation & Reasoning: Representation (Abstraction)

Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly 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.

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.

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
Our Grades 9–10 Mostly covered
What learners do

In Python Lab Module 13, learners implement a simplified stateful garden model and transfer the pattern to a bus-stop simulation. Grade 9 'Numbers in AI' covers feature vectors.

Still to add

The relevant state and omitted detail are supplied; learners do not choose and justify abstractions in a new model on their own.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Solving Constraints in Code', learners write their own graph, tree or if-then rules for a real system and name the form; in Grades 11-12 they list what the model and a system blueprint leave out and why it is safe.

Where it is taught

Big Idea 2 - Representation & Reasoning: Representation (Symbolic representations)

Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Maps, Paths and Trees', learners turn a room route into dots and links and follow a legal path.

Still to add

The symbolic vocabulary is introductory; naming the modality and translating into tree or logic forms are limited.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Dots, Lines and Moves' and Grade 4 'Make a Model', learners draw labelled dot-and-link and tree diagrams and translate a real route or clock behaviour into a symbolic model.

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.

Our Grades 9–10 Mostly covered
What learners do

In Grade 3 'Dots, Lines and Moves', learners turn a real bus route into dots and lines, match folders to a tree and pick graph, tree or rule for six real things, naming the form each time.

Still to add

Learners match and sort given examples but do not yet draw their own graph or tree for a new problem.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Solving Constraints in Code', learners choose a structure from their own life, write it as an edge list, parent-child tree or at least three if-then rules, name the modality, and explain why it fits.

Where it is taught

Big Idea 2 - Representation & Reasoning: Representation (Data structures)

Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 1 'Maps, Paths and Trees', learners identify dots, links, and a tree's root and leaves.

Still to add

Knowledge graphs, schemas and parent relations are not covered in Grades 1-2.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Dots, Lines and Moves', learners identify immediate parents and leaves and trace a typed camel, desert, dry knowledge graph, separating a valid schema from a true fact.

Our Grades 6–8 Fully covered
What learners do

In Python Lab Module 8 'Follow the Links', learners query a labelled dish, ingredient and category graph, use its schema, and explain the relationships.

Where it is taught
Our Grades 9–10 Covered earlier · Grade 3
What learners do

In Grade 3 'Dots, Lines and Moves', learners name a tree's root, parents, children, links and leaves and read a small web of facts with its rule card, called a schema.

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

In Grade 11 'Bayesian Networks', learners use nodes, arrows and parents. In Python Lab Module 22 'Triples and Two Hops', they implement typed triples, a schema check and a two-hop query. Grade 12 covers knowledge graphs in retrieval.

Big Idea 2 - Representation & Reasoning: Representation (Feature vectors)

Progress from identifying the features that make objects in a collection unique and organizing them into a feature table (K-2), to constructing a feature-vector representation for a set of objects and showing that similar objects lie close together in feature space (3-5), to explaining how word embeddings represent words as numeric vectors for tasks like machine translation (6-8), to describing how a transformer network maps sequences of word-embedding vectors to output sequences (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Byte's Clue Card', learners fill the same ordered number, yes-or-no and colour clues for each animal, and see that animals sharing more clues sit closer together.

Still to add

Clue cards are not called vectors or embeddings, and closeness is counted by matching clues rather than measured as distance.

Where it is taught
Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Close Together on the Map', learners place numeric clue rows on a two-feature map, compare distances and choose features that separate confusing items. Embeddings are introduced as a word map.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'Close Together on the Map', learners turn clues into rows of numbers, place things on a clue map, choose clues that pull a muffin and puppy apart, and see similar words sit close together on a word map.

Where it is taught
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

Big Idea 2 - Representation & Reasoning: Search (State spaces and operators)

Progress from illustrating a next possible board state in tic-tac-toe (K-2), to illustrating how a computer represents game play as a linear sequence of board positions connected by legal moves (3-5), to illustrating how a computer solves a maze, finds a map route, or reasons over a knowledge graph by drawing a search tree (6-8), to identifying real-world problems (task planning, scheduling, resource allocation) as search problems and describing their states and operators (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Maps, Paths and Trees', learners build a route between adjacent squares that avoids blocked squares and compare alternative routes.

Still to add

Scheduling and planning are not identified as the same kind of search problem.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Dots, Lines and Moves', Grade 4 'Make a Model' and Grade 5 'Choose the Right Tool', learners represent legal moves and routes, and choose search for a vacuum task, justifying the map input and constraints.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'AI Game Masters', 'The Sudoku Champion' and 'The Delivery Challenge', learners work with game trees of legal moves, state-space search and constraint satisfaction, and see routing and delivery as search problems.

Our Grades 9–10 Fully covered
What learners do

Grade 7 'AI Game Masters' teaches state-space search, legal moves, backtracking and constraint satisfaction, and frames routing and scheduling as search problems.

Our Grades 11–12 Fully covered
What learners do

Grade 7 chapters such as 'AI Game Masters', 'The Chess Challenge' and 'The Delivery Challenge' teach state-space search, constraint satisfaction and pathfinding. Grade 12 'From Chatbots to Agents' covers task planning.

Big Idea 2 - Representation & Reasoning: Search (Combinatorial search)

Progress from drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 1 'Maps, Paths and Trees', learners compare two routes and their lengths.

Still to add

No expanded search tree and no comparison of breadth-first, depth-first and best-first search is taught in Grades 1-2.

Our Grades 3–5 Partly covered
What learners do

In Grade 3 'Dots, Lines and Moves', learners compare breadth-first and depth-first visits and route lengths.

Still to add

Best-first, A* and Dijkstra are not taught, and drawing and expanding full comparative search trees is limited.

Our Grades 6–8 Mostly covered
What learners do

In the Search Sandbox create tool, learners draw a maze and compare computed breadth-first, depth-first, greedy and A* paths, frontiers and expansion traces.

Still to add

Dijkstra is absent in Grades 6-8, so the full comparison is not met.

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

In Python Lab Module 53, learners implement breadth-first search, inspect parent-child search-tree links, and compare hops, costs and expansion lists for BFS, DFS, Dijkstra and A* on one map.

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

In Python Lab, learners write breadth-first search, print its search tree and compare hops, cost and expansions with depth-first, Dijkstra and A*. In Search Sandbox they build one maze and step BFS, DFS, greedy and A* while recording expanded cells and path length.

Still to add

Dijkstra and the other comparison searches are supplied helpers that learners run, not write; no Grade 11-12 lesson sends learners to either tool.

Where it is taught

Big Idea 2 - Representation & Reasoning: Reasoning (Types of reasoning problems)

Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Rules or Examples?', learners classify jobs as name-it, guess-next or find-a-way and explain a plant rule decision.

Still to add

Sequential decision-making and logical inference are not both classified as separate problem families.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Choose the Right Tool', learners sort new tasks into classification, prediction, search, sequential decisions and rule inference, giving reasons.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Choose the Right Tool', learners sort jobs into naming, guessing what comes next, finding a way, trying and scoring, and following AND-OR rules, then match methods and build a tool check.

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

In Grade 9 'Welcome, AI Engineers!', learners sort eight systems into logic rules, optimise or learn, and in 'ML Workflow Overview' sort questions into classification, regression or clustering.

Still to add

Sequential decision-making is absent from Grades 9-10, and the two sorts use different frames, so no task places one problem among all five types.

Our Grades 11–12 Fully covered
What learners do

Learners repeatedly sort problems into technique groups, including judging which scenarios are reinforcement learning and which are classification, in Grade 11 'The Gaming Champion' and Grade 9 chapters such as 'Classification Problems'.

Where it is taught

Big Idea 2 - Representation & Reasoning: Reasoning (Reasoning algorithms)

Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Rules or Examples?' and Grade 1 'Clue Questions', learners choose between rules and examples and use yes/no questions to classify.

Still to add

Naming an algorithm and justifying it against both the data and the decision complexity is limited.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Choose the Right Tool', learners choose search, written rules or learning from examples for a stated job and justify the choice from the inputs and complexity.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Unit 5 Review: AI Games & Puzzles', learners match seven search methods to their jobs. In Grade 8 'The AI Hammer' they match six AI tools to problems and justify their choice.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Which Algorithm Wins?', learners compare algorithms on accuracy, interpretability and speed and match algorithms to situations. Grade 10 'The AI Blueprint' matches architectures to data and task.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Training Your Model' and Grade 12 'The Blueprint Before the Building', learners match technical choices such as MobileNetV2, DistilBERT, XGBoost and transfer learning to their reasons, and drag problems to the best-fit model family.

Big Idea 3 - Learning: Nature of Learning (Humans vs. machines)

Progress from describing and giving examples of how people vs. computers learn (K-2), to differentiating human flexible learning from computer pattern-finding/trial-and-error learning (3-5), to contrasting human learning characteristics with narrowly-defined machine learning problems (6-8), to defining supervised, unsupervised, and reinforcement learning and giving human-learning analogues for each (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Can a Machine Think?' and 'Rules or Examples?', learners match three learning analogies and contrast lived experience with machine examples.

Still to add

The comparison of how people learn and how models train is scattered rather than one explicit contrast.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'How Machines Learn', learners compare people's few-example, purposeful learning with numeric-pattern training and match the three machine learning families to human analogues.

Our Grades 6–8 Covered earlier · Grade 2
What learners do

In Grade 2 'Rules or Examples?', learners match how they learn to how a machine learns: shown names, sorting alike things, and trying for a star. In Grade 7 they contrast human and machine learning in detail.

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

In Grade 10 '86 Billion Neurons!', learners compare how brains learn with how networks are trained and sort brain versus network facts. Grade 9 mentions labels as an answer key.

Still to add

Grades 9-10 give no human analogue for unsupervised or reinforcement learning; these appear only in Grade 7.

Our Grades 11–12 Covered earlier · Grade 5
What learners do

In Grade 5 'Three Ways to Learn', learners match a machine learning from named examples, from unnamed grouping and from a score to the way they learn the same thing; in Grade 7 they sort nine jobs into answer key, no teacher or scoreboard.

Big Idea 3 - Learning: Nature of Learning (Finding patterns in data)

Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
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.

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.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'What is Training?' and 'Learning from Examples', learners follow a guess, compare and nudge training loop, find clues from labelled photos, and draw to train, testing on unseen examples.

Our Grades 9–10 Fully covered
What learners do

Grade 9 'ML Workflow Overview' and 'The Learning Moment' deepen training on examples, pattern finding and prediction on new input, building on Grade 2 'Learning From Examples'.

Our Grades 11–12 Fully covered
What learners do

Learning from examples, generalising to unseen data and predicting on new input are taught and practised from Grade 2 'Learning From Examples' through Grade 7 chapters such as 'What is Training?'.

Where it is taught

Big Idea 3 - Learning: Nature of Learning (Training a model)

Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Notice, Then Do' and 'Check Byte's Work', learners train on movement samples or labelled tiles, test fresh examples and discuss a real miss.

Still to add

Preparing a dataset, training a model and writing a performance report are not joined into one documented workflow at this level.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Demo Day: My Own Helper', learners carry their chosen dataset into training, test unseen cards, report the actual counts and misses, and keep the results in a presentation.

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.

Our Grades 9–10 Fully covered
What learners do

In the Grade 9 'Science Fair Star' capstone, learners use their own data, keep locked test rows, report the test score beside a no-model baseline and name a limit. In Grade 10 they also report the weakest category of a model they trained.

Our Grades 11–12 Fully covered
What learners do

Grade 9 projects such as 'The Messy Data Disaster' and 'Science Fair Star' take learners through collecting, cleaning, splitting, training, testing and reporting with scikit-learn. Grade 7 learners train and test an image classifier on their own photos.

Where it is taught

Big Idea 3 - Learning: Nature of Learning (Constructing vs. using a reasoner)

Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Training' and Grade 5 'How Machines Learn', learners sort moments into training or using, and order an AI's life as collect, train, test, use. A chatbot is shown to use what it learned without adding to it.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'Training and Using', learners sort moments such as learning from millions of photos or asking for a spelling into training or using. Grade 7 learners also teach, then test, in Raise Your AI.

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

In Grade 9 'The Learning Moment' and 'Model Ready to Work', fit and predict are taught as separate chapters, and learners sort actions into before-training and after-training groups.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Your Own AI!' and Grade 9 chapters such as 'The Learning Moment' and 'Model Ready to Work', learners study pre-training versus fine-tuning, fit versus predict, and training cost versus use cost, and classify activities by stage.

Where it is taught

Big Idea 3 - Learning: Nature of Learning (Adjusting internal representations)

Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Inside a Learning Machine', learners match changes to a tree's questions, a neural network's weights and a regression line after errors, and explain what moved.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Inside a Learning Machine', learners match and sort what a tree learner, a network and a line learner each change after a mistake: a question, a weight dial, or the position of the line.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Learning Moment' and Grade 10 'The Secret Ingredients!', learners drag algorithms to what each one learns: coefficients, rules, tree structure or memorised examples, with weights covered separately.

Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Training Your Model', learners see the internal form of each model family side by side and do one gradient update by hand. In Python Lab Module 46 they code a tree split choice.

Still to add

No activity has learners compare what training changes inside a decision tree, a neural network and a regression on the same data; the side-by-side view is read-only.

Big Idea 3 - Learning: Nature of Learning (Learning from experience)

Progress from explaining how reinforcement learning lets a computer learn from trial-and-error experience with a reward signal, e.g. a grid-world navigation demo (3-5), to explaining the difference between supervised learning (told the correct output) and reinforcement learning (told only how well it's doing) (6-8), to selecting the appropriate learning-algorithm family (supervised, unsupervised, or reinforcement) for a given reasoning problem (9-12). K-2 is marked N/A.

We start Grade 2 · framework Grade 3Framework grades 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Remember: How Machines Learn', learners meet the names supervised, unsupervised and reinforcement learning, then sort six new jobs by the family that fits. Grade 2 'Rules or Examples?' already pairs the three ways with human learning.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Supervised Learning Basics', learners meet definitions of supervised, unsupervised and reinforcement learning, group unlabelled drawings, and sort nine described problems into answer key, no teacher or scoreboard.

Where it is taught
  • Grade 7Unit 2: Teaching Machines › Supervised Learning Basics
    What is Supervised Learning?Learning Without LabelsGroup the Unlabelled DrawingsA Third Way: Learning From RewardsThree Ways to LearnKey VocabularyNani's Spotted LeavesInput or Label?The Two Piles Nobody Can NameTraining PhaseFair Exam or Not?Every Label Is a Human DecisionConcept MatcherOne Wrong Label Bends Every Prediction After ItClassification ExplainedBinary or Multi-class?Regression ExplainedWhere the Numbers Come From
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'How Machines Learn', learners see supervised, unsupervised and reinforcement learning defined as named examples, grouping and trying for a score, then sort six new jobs into the fitting family.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'The Gaming Champion', 'AI in Finance' and 'AI in Retail', learners define the three families, repeatedly sort tasks into supervised, unsupervised or reinforcement learning, and run k-means.

Where it is taught

Big Idea 3 - Learning: Neural Networks (Structure)

Progress from illustrating that a 1-3 neuron network is a function that computes weighted-sum/threshold outputs (3-5), to illustrating the layered structure (input/hidden/output) of a neural network and its transfer functions (6-8), to describing feed-forward, 2D convolutional, recurrent, and generative adversarial network architectures and their typical uses, e.g. deepfakes (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Inside a Learning Machine', learners calculate weighted-unit thresholds and identify the roles of input, hidden and output layers.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Inside a Learning Machine', learners add weighted clues, check the total against a line to switch a unit on, and sort jobs into input, hidden and output layers. Grade 6 repeats this and trains a network in the playground.

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

In Grade 10 'Welcome to Grade 10!', 'The Missing Magic!' and 'From LEGO to Networks!', learners study input, hidden and output layers, weighted units, activation functions and dense-layer parameter counting.

Our Grades 11–12 Fully covered
What learners do

In Grade 10 chapters such as '86 Billion Neurons!' and 'The Secret Ingredients!', learners study a neuron as a weighted sum plus activation, layer roles, activation functions and parameter counting.

Where it is taught

Big Idea 3 - Learning: Neural Networks (Weight adjustment)

Progress from demonstrating how weights are assigned in a neural network to produce a desired input/output behavior (3-5), to demonstrating a learning rule (e.g. perceptron rule) that adjusts weights of a one-layer network in response to errors (6-8), to training a multilayer neural network with the backpropagation algorithm and describing how weights and hidden-unit outputs change during learning (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 3–5 Partly covered
What learners do

In Grade 5 'Inside a Learning Machine', learners adjust a paper clue weight after an error and explain the resulting threshold decision.

Still to add

No multilayer trainer or backpropagation workflow is used; one threshold-unit exercise does not cover training a network.

Our Grades 6–8 Partly covered
What learners do

In Grade 6 'The Deep Learning Revolution', learners train a one-hidden-layer network on XOR in the Neural Network Playground. Grades 7-8 teach the guess, compare and adjust loop in chapters such as 'What is Training?'.

Still to add

No learning rule or account of backpropagation is shown, training is in an in-app toy rather than Keras or TensorFlow, and the one tool step can be skipped.

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

In Python Lab Module 41 'Train an MLP for Real' and Grade 10 'Backpropagation Basics', learners train a scaled multilayer perceptron, inspect the loss change and held-out accuracy, and connect error-based weight adjustment to backpropagation.

Our Grades 11–12 Fully covered
What learners do

In Grade 10 chapters such as 'Backpropagation Basics' and 'Your First Neural Network', learners study loss, gradient descent, the chain rule and backpropagation, then compile and fit multilayer models in Keras.

Where it is taught

Big Idea 3 - Learning: Datasets (Feature sets)

Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Byte's Clue Card', learners choose numeric, yes/no and colour features, fill clue cards and explain why a misleading feature confuses Byte.

Still to add

The app saves feature choices separately from labelled training examples and does not build one typed feature table for the classifier.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'A Table of Clues, a Key With Two Sides' and Grade 5 'Demo Day: My Own Helper', learners build a labelled selection and a table of numeric, text and yes/no features and explain misleading feature choices.

Still to add

The typed feature table is not the representation actually given to the learner's trained app model.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Teach the Teacher' and Grade 7 'Features and Attributes' and 'Kabir's Excitement', learners write a category with YES and NO examples, build labelled photo sets under rules and sort features into keep or remove.

Our Grades 9–10 Fully covered
What learners do

In Grade 7 'Aisha's Big Idea', learners collect and label their own image and sound classes. In Grade 9 'The Hidden Information' and 'Predicting Numbers', they engineer features and explain encoding effects.

Our Grades 11–12 Fully covered
What learners do

In Grade 7 chapters such as 'Learning from Examples', 'Good Examples vs Bad Examples' and 'Aisha's Big Idea', learners collect and label their own datasets, design class labels and engineer features.

Where it is taught

Big Idea 3 - Learning: Datasets (Large datasets)

Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'This Really Happened', learners examine how ImageNet was collected and labelled, its scale, the labour involved and routes for bias.

Still to add

The lesson presents information about the dataset; learners do not inspect a sample of its actual records and labels.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'This Really Happened', learners work out ImageNet's size, sort how photos got in, order how it was labelled by paid workers, and use a gap checklist to find where it leans to one country.

Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Build a Vision AI' and Grade 9 'The Unsinkable Ship', learners read about ImageNet's size and crowd labelling, match tasks to datasets, check the real Titanic file, and plan collection for their own data.

Still to add

Learners do not work through a large training dataset; how ImageNet or COCO was gathered and labelled, its labelling cost and where bias entered are not examined at these grades.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Data is Everything' and 'The Standard Measures', learners examine ImageNet, COCO, GLUE and SQuAD for task, scale and status, and trace selection, annotation, time and geographic bias to how the data was collected.

Big Idea 3 - Learning: Datasets (Bias)

Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Which Pictures Are Missing?' and 'Project: Teach Byte Fairly', learners sort what is missing from examples, compare bar lengths for imbalance, predict wrong outcomes, add missing examples and retest.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Wrong, and How Sure', learners see a red-apple-only model fail on yellow apples and a child-only app miss a grandad. They spot the gap, take photos, add them and test again.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Teach the Teacher' and Grade 7 'Good Examples vs Bad Examples', learners judge ten dataset scenarios, match flaws to the damage they cause, and create an imbalance on purpose to see it bias a model.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Unsinkable Ship' and Grade 10 'Bias in AI Systems', learners examine missing-data bias and imbalance in Titanic data and trace landmark bias cases to data gaps.

Our Grades 11–12 Fully covered
What learners do

Learners repair datasets in Grade 7 (such as 'Good Examples vs Bad Examples') and analyse bias types in real benchmarks in Grade 12 'Data is Everything', predicting failures and proposing data fixes.

Big Idea 4 - Natural Interaction: Natural Language (Structure of language)

Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'From Sound to Sentence' and 'Words Have Jobs', learners clap sound parts, tell naming words from doing words and build a grammatical sentence, showing that order changes meaning.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Sentence Rules and Two Meanings', learners use a who, does, what grammar to build and swap sentences, separating grammatical structure from plausible meaning.

Our Grades 6–8 Mostly covered
What learners do

In Grade 3 'Sentence Rules and Two Meanings', learners order sounds, words, phrases and sentences, sort parts into a who-does-what pattern, and build sentences with it. They see that a pattern machine checks form, not meaning.

Still to add

The sentence pattern is small and built from fixed choices; learners do not write grammar rules or parse sentences.

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

In Python Lab Module 47 'A Tiny Grammar' and 'Parse It', learners implement a small recursive grammar that generates sentences and a parser that returns phrase structure for alternatives.

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

Grade 7 'AI Understands Words' teaches syntax and part-of-speech tagging, and Grade 11 'Hidden Markov Models' describes part-of-speech taggers. In Python Lab Module 47 learners write a tiny grammar and a parser.

Where it is taught

Big Idea 4 - Natural Interaction: Natural Language (Ambiguity of language)

Progress from illustrating ambiguity via homophones/homonyms resolved by context (K-2), to illustrating pronoun reference ambiguity resolved by world knowledge (3-5), to illustrating prepositional-phrase attachment ambiguity resolved by meaning (6-8), to describing polysemy (multiple word senses) and how statistical NLP systems resolve it less reliably than human common sense (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'From Sound to Sentence' and 'Words Have Jobs', learners use sentence context to resolve sea versus see and an ambiguous word that Byte misreads.

Still to add

Pronoun reference and phrase-attachment ambiguity are not shown at this level.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Sentence Rules and Two Meanings', learners draw alternative phrase attachments and use context and clarifying questions to resolve reference and a mistaken river-bank translation.

Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Two Readings' and 'When the Machine Picks Wrong', learners resolve 'saw the boy with the torch', a trophy-and-bag 'it', and bank or bark by their context. They see people use what they know and a machine guesses from nearby words.

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

In Grade 7 'AI Understands Words', Grade 9 'Emotion Detection' and Grade 3 'Machines Joined Up', learners study word ambiguity, idioms, sarcasm and code-mixing, with context-based disambiguation and where AI fails.

Our Grades 11–12 Fully covered
What learners do

Grade 7 chapters such as 'AI Understands Words' and 'Family Reunion' teach word ambiguity, idioms, homophones and context. Grade 11 'Self-Attention Mechanism' works through pronoun resolution.

Big Idea 4 - Natural Interaction: Natural Language (Reasoning about text)

Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Words Have Jobs', learners group words by meaning and resolve an ambiguous word using context.

Still to add

Embedding-style analogy and grammatical agreement are not both taught and practised at this level.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Close Together on the Map' and Grade 3 'Sentence Rules and Two Meanings', learners follow an analogy step on a word map, choose grammatical agreement and explain bias and context failures.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'Close Together on the Map', learners turn clues into number rows, find the odd word on a word map and solve puppy-is-to-dog analogies. They fill agreement gaps and see where a word machine slips. In Grade 5 context picks the word.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Word Embeddings' and 'Word2Vec Basics' and Grade 9 'Emotion Detection', learners sort word analogies by relationship, compare cosine similarity, study context learning and see negation and sarcasm failures.

Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Attention Is All You Need' and 'Meet BERT!', learners compute attention weights for context. Grade 12 'Cosine by Hand' and Python Lab Module 47 cover embedding similarity, and Grade 10 'Word Embeddings' covers analogies and failures.

Still to add

Grammatical agreement is not exercised, and the vectors used are deliberately invented.

Big Idea 4 - Natural Interaction: Natural Language (Applications)

Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Byte Guesses Feelings', learners sort junk messages, select a summary and explain recognition, translation and sentiment functions.

Still to add

Named-entity recognition is absent, and the sorting is done by hand rather than with an NLP tool.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Feelings, Names and Junk' and Grade 3 'Sentence Rules and Two Meanings', learners label named entities, sort sentiment and junk, and build a small text-response rule model.

Still to add

The activities are transparent simulations; using a full NLP recognition, translation or summarisation tool is not established.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Sentiment Analysis' and 'Voice Assistants Explained' and Grade 8 'Simple Text Classifier', learners study speech recognition, translation, spam filtering and summarisation, and build a classifier and sentiment detector in Python.

Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Spam Invasion' and Grade 10 'Unit 4 Review', learners cover speech recognition, translation, summarisation, sentiment, entity recognition and spam filtering, and build a Python chatbot in Grade 8 'Inside a Chatbot'.

Our Grades 11–12 Fully covered
What learners do

Grade 9 chapters such as 'The Spam Invasion' and 'Emotion Detection' have learners build a spam filter and a sentiment classifier in Python. Grade 12 covers speech recognition, translation, summarisation, question answering and entity recognition.

Where it is taught

Big Idea 4 - Natural Interaction: Commonsense Reasoning

Progress from giving commonsense explanations of human behavior useful for a robot to know, e.g. choosing a plausible story ending (K-2), to explaining what everyday cultural and naive-physics knowledge a computer needs to understand a story (3-5), to explaining the naive-physics inference rules (mass, volume, conservation, forces) needed to solve a physics reasoning problem (6-8), to explaining the cultural, naive-physics, and folk-psychology knowledge needed to interpret a fable, fairytale, or Winograd-schema sentence (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'What Byte Needs to Know', learners separate said facts from inferred facts in a milk story and explain why a cup falling upward shows missing knowledge of the lived world.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'What Byte Doesn't Know', learners separate seen evidence from inferred knowledge, such as rain and birthdays, and explain the limits of learning from examples without lived experience.

Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'What Byte Doesn't Know', learners sort what a picture shows from what people know, what a story leaves out, where machine knowledge comes from, and why rare or local facts are missed.

Where it is taught
Our Grades 9–10 Covered earlier · Grade 4
What learners do

In Grade 4 'What Byte Doesn't Know', learners separate what a story or picture shows from what everyone just knows, see where machines get such knowledge from captions, and why rarely written or local facts are missed.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'The Unsolved Mysteries' teaches common sense, grounding, causal reasoning and compositional generalisation as open problems, with sorting and written explanation. Grade 11 'Movies from Words' relates to it.

Big Idea 4 - Natural Interaction: Understanding Emotion

Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Byte Guesses Feelings', learners combine a word clue and a voice clue, recognise uncertainty and choose how an app changes its response.

Still to add

The response activity is fixed-choice; learners do not build and test an emotion-response system against misleading inputs.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Feelings, Names and Junk', learners combine text, face and voice clues, run a response tree and test a posed-smile or sarcastic case while keeping a human choice.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Sentiment Analysis' and Grade 8 'Sentiment Detection', learners score text sentiment with negation and aspects, study six emotions, see a support inbox reordered by negativity and train a happy and sad face classifier.

Our Grades 9–10 Covered earlier · Grade 4
What learners do

In Grade 4 'Feelings, Names and Junk', learners read face, voice and word clues, see sarcasm fool a sentiment tool, combine clues, and build a break-or-carry-on tree for an app that changes what it does from a guessed feeling.

Where it is taught
Our Grades 11–12 Covered earlier · Grade 4
What learners do

In Grade 4 'Feelings, Names and Junk', learners see how a program guesses feeling from face, voice and word clues, why sarcasm and posed smiles fool it, why two clues beat one, and how an app offers a break the child can refuse.

Where it is taught

Big Idea 4 - Natural Interaction: Philosophy of Mind

Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Can a Machine Think?', learners tell a machine's feeling words from a person's experience and give a reason for gentle care.

Still to add

Fixed choices and a teacher replay do not give an evidence-based debate between positions on consciousness and rights.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Does It Understand?', learners use observed replies from an imitation test, state the limits of the evidence, and argue opposing positions after restating a partner's reason.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

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

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.

Big Idea 5 - Societal Impact: Ethical AI (Diversity of Interests and Disparate Impacts)

Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
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.

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.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Can Be Unfair', Grade 7 'Computer Vision in the Real World' and Grade 6 'AI and Jobs', learners meet documented cases such as Gender Shades, proctoring that wrongly flags students and the digital divide.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Bias in AI Systems' and Grade 9 'The Biased Hiring Model', learners see COMPAS, the healthcare algorithm, Amazon hiring and Apple Card as documented unequal impact by demographic group.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The AI Radiologist' and 'The Algorithm That Discriminated', learners study healthcare AI that helps some patients while misdiagnosing women or failing rural clinics, plus COMPAS and hiring cases, using group-level metrics.

Where it is taught

Big Idea 5 - Societal Impact: Ethical AI (Ethical Design Criteria)

Progress from discussing what makes a system fair vs. unfair through a fair-distribution exercise (K-2), to evaluating whether an AI system meets the design criteria of transparency and explainability (3-5), to evaluating whether an AI system meets the design criteria of accountability and respect for privacy (6-8), to analyzing whether an AI system satisfies the full set of ethical design criteria: fairness, transparency, explainability, accountability, respect for privacy, and adherence to societal values (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Fair and Good for Everyone?', learners judge Talky's four behaviours against safe, fair and people-in-control criteria and say which falls short.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Judge One Machine', learners judge one school-camera case against several named ethical criteria, justify failures and prioritise an immediate human remedy.

Our Grades 6–8 Covered earlier · Grade 5
What learners do

In Grade 5 'Judge One Machine', learners check a school gate camera against fairness, privacy, transparency, explanation, human oversight and harm, give a verdict for each principle, and choose what to fix first.

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

In Grade 10 'Why AI Ethics Matters' and 'Building Your AI Ethics Framework', learners apply four ethics pillars to eight scenarios and use PAUSE to approve or reject use cases. 'Explainable AI (XAI)' judges explainability separately.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'AI Ethics in Practice' and Grade 12 'The Council Decision', learners apply fairness, accountability, transparency and ethics criteria and governance principles to systems, judging where each falls short.

Big Idea 5 - Societal Impact: Ethical AI (Practicing Ethical Design)

Progress from creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'The Helper's Report Card' and 'Demo Day: My Own Helper', learners make an eight-part report using saved scores where available, mark coverage gaps and recommend tests and remedies.

Still to add

Several report fields are chosen from prepared cards, so a fully independent audit of the child's own model is limited.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Teaching by Showing', learners train their own drawing model and write an eight-line model card covering purpose, prohibited use, training counts, measured performance, fairness check, confusion, privacy and remedy.

Where it is taught
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

Big Idea 5 - Societal Impact: AI & Culture (AI in Daily Life)

Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
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.

Our Grades 3–5 Fully covered
What learners do

In Grade 2 'Machines That Talk' and Grade 3 'Sorting and Deciding', learners name assistants, face recognition, recommenders, route planning and game characters and what each does for the user.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI in Your Home', 'AI in Your Phone' and 'AI Recommends For You', learners hunt for AI at home, on the phone and in games, travel, shopping, health and school, naming what each does for the user.

Our Grades 9–10 Fully covered
What learners do

Grade 9 'Welcome, AI Engineers!' continues spotting everyday AI, and in Grade 10 'Transformer Preview' learners decide which phone activities use Transformer AI. Earlier grades start this from Grade 1.

Our Grades 11–12 Fully covered
What learners do

Grade 6 chapters such as 'AI in Your Home' and 'AI Recommends For You' have learners identify assistants, recommenders, speech and image recognition and cameras and name what each does. Grade 11 'AI in Healthcare' extends this.

Where it is taught

Big Idea 5 - Societal Impact: AI & Culture (Trust and Responsibility)

Progress from analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Check a Real Rule', learners debate a surveillance or profiling restriction using stakeholders, a concrete requirement and a checker, distinguishing guidance from law.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Computer Vision in the Real World' and Grade 6 'AI Debate Club', learners debate school face recognition stopped by regulators, proctoring misflags, who may view footage, opting out, and the EU AI Act and DPDP.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI Governance and Regulation' and 'Face Recognition', learners critique surveillance and profiling, sort risk-based regulation, and order and explain the five steps of creating AI regulation.

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

Big Idea 5 - Societal Impact: AI & the Economy (Impacts of AI on Sectors of Society)

Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 1 'Long Ago and Now' and 'Helpers and Jobs', learners compare farm hand, horse, tractor and drone, and forecast one changing task and skill.

Still to add

Analysis tied explicitly to earlier industrial revolutions is not developed at this grade band.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Then, Now, Next', learners compare farm automation with steam, electric and computer changes, analyse task counts and defend a sector forecast.

Our Grades 6–8 Mostly covered
What learners do

In Grade 6 'AI and Jobs' and 'AI in 2030', learners sort cases into 'only a task' or 'the whole job', match shrinking jobs to the jobs that grew beside them, and write their own 2030 prediction with evidence and who might be left out.

Still to add

The comparison is with recent single-technology job shifts, not the industrial revolutions, and the learner's forecast is general rather than tied to the sector analysed.

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

In Grade 10 'AI and the Future of Work', learners sort jobs as growing or shrinking, audit one legal-services role, choose a near-term headline from options and read workforce forecasts critically.

Still to add

No Grade 9-10 lesson compares AI's effect on a sector with earlier industrial revolutions, and learners never write their own near-term sector forecast.

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

In Grade 11 sector chapters such as 'AI in Healthcare', 'AI in Finance' and 'AI in Agriculture', learners study sector-by-sector AI change; Grade 12 'Jobs Transformed' frames it against earlier industrial revolutions with near-term forecasts.

Big Idea 5 - Societal Impact: AI & the Economy (Effects on Employment)

Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Helpers and Jobs', learners compare the tasks a post worker keeps with what a machine sorts, predict a new robot job and choose a skill to develop.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Then, Now, Next', learners compare retained and changing farm tasks, forecast how a sector will change next, and name a skill and an uncertainty.

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.

Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work', learners sort eight jobs as growing or shrinking, order five steps of how AI transforms a job, and identify five future-ready skills.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and Grade 11 'AI Careers', learners match jobs to automation, augmentation, creation or transformation, and identify the skills each change implies.

Big Idea 5 - Societal Impact: AI for Social Good (Democratization of AI Technology)

Progress from describing and using AI extensions/plugins in a familiar programming framework such as Scratch, e.g. speech-to-text, face recognition (3-5), to creating a novel application using AI extensions/plugins in a chosen framework such as Scratch, MIT App Inventor, or Calypso (6-8), to creating a novel application using AI tools of the student's choice, now commonplace and freely available without advanced degrees or expensive equipment (9-12). K-2 is marked N/A.

Framework grades 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 3–5 Partly covered
What learners do

In Grade 3 'Change the Examples, Change the Guess' and Grade 5 'Demo Day: My Own Helper', learners customise examples and run a local no-code learner.

Still to add

Learners do not integrate the model into a separate application or customise an external model, API or extension.

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.

Our Grades 9–10 Fully covered
What learners do

In Python Lab Module 44 and 45, learners reuse fixed pretrained digit feature weights, train a new classifier head and implement classify_drawing returning a predicted label and probability.

Our Grades 11–12 Fully covered
What learners do

Learners export Teachable Machine models into Scratch apps in Grade 8 (such as 'Arjun's Smart Game') and fine-tune pre-trained Keras models for custom classes in Grade 10.

Where it is taught

Big Idea 5 - Societal Impact: AI for Social Good (Using AI to Solve Societal Problems)

Progress from describing how AI can be used to solve a societal problem, e.g. Code.org's AI for Oceans classifying wildlife vs. debris (K-2), to designing a solution to a societal problem using AI technology, e.g. environmental protection, energy conservation (3-5), to researching a societal problem and describing how AI perception/reasoning/learning technologies could address it, referencing the UN Sustainable Development Goals (6-8), to evaluating an actual AI-for-social-good project in terms of the problem addressed and its real or potential impact (9-12).

Framework grades 1–2, 3–5, 6–8, 9–10, 11–12
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 1 'Machines Help Farms and Water' and Grade 2 'A Helper for Everyone', learners study crop needs and test a supplied leaf helper for a farmer.

Still to add

Research and evaluation of real societal impact stay separate from the supplied helper's card score.

Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Did It Really Help?', learners research attributed fact cards, design a goal and evaluate benefits, misses and stopping thresholds.

Still to add

Impact data are supplied stories rather than measured effects of the child's own AI solution.

Our Grades 6–8 Mostly covered
What learners do

In Grade 8 'The Global Goals Project', learners match local problems to the 17 goals, scope ideas, train a mini Bin Buddy in Raise Your AI, plan evidence and pitch their own SDG project.

Still to add

The learner's own research and impact judgement are planned but never carried out, and the build is for the worked example rather than the learner's chosen problem.

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

In Grade 8 'Global Goals', learners research a local problem, train a mini Bin Buddy in Raise Your AI, plan evidence that counts harms, and pitch an SDG project. In Grade 5 they weigh impact beyond the score and choose keep, change or stop.

Still to add

Learners do not yet research a local problem with their own evidence or judge the impact of their own solution.

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

In Grade 11 'SDG Research-and-Build Project', learners link a goal to a local problem, vet open data, set a baseline, run and change a canteen forecast, estimate impact and footprint, consider harms and pitch their own project.

Still to add

The learner's own SDG project stops at a written pitch; impact is measured only on supplied canteen data, and no capstone or rubric covers it.

Where it is taught

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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.