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Learners play “AI or Not” with their teacher. Given examples, such as smart home devices, social media or apps, they can categorise each based on whether it uses AI.

Learners compare examples of “for you” landing pages on social media platforms and reflect on how and why they are influenced by what appears.

Learners explore screenshots from AI chatbots and apps to identify when it may be designed to persuade, rather than inform. With a teacher, they discuss how features like “streak” counts, character names or overly-agreeable responses might be engineered to increase engagement.

As their teacher introduces common phrases used to describe AI use (e.g. “AI thinks,” “AI understands,”), learners identify which ones are misleading and why. Learners develop better descriptions using technically accurate and age-appropriate language, then reflect on how language shapes their understanding of what AI can or cannot do.

Learners identify a common AI myth among peers (e.g. “AI understands like humans” or “AI is always correct”). They create an artefact, such as a poster, short video or podcast to address the misconception by explaining how AI works.

Learners review different types of AI tools and compare design choices that make the systems appear human-like, friendly or intelligent. In small groups, learners practise explaining how the tools produce outputs, focusing on programmed processes rather than human qualities. As a class, they share and refine their explanations and reflect on how different design features affect understanding and trust in AI systems.

Learners review an AI-powered tool’s solution to a math problem and accompanying explanation, then compare the AI-generated content to the process that their teacher has introduced to the class.

In small groups, learners plan an itinerary for a tourist visiting their hometown. When the teacher provides AI-generated recommendations for local restaurants, learners compare the recommendations using other sources to determine if the information is outdated, inaccurate or a “hallucination.” From there, learners draw from their own knowledge and experience to decide whether the locations would be appealing to a visitor.

When their teacher presents different AIgenerated interpretations of a historical event, learners turn to trusted secondary or primary sources to verify the accuracy of the interpretations and argue which one they would include in a final report.

Learners make a list of movie and television recommendations for a favorite book character, justifying their choices with textual evidence. As a class, they share their lists and discuss their knowledge of the characters, then compare the process to the ways AI generates recommendations.

Learners compare a music app’s recommendations to a list of their own favorite songs and artists, then discuss when the algorithms reinforced existing preferences or introduced them to new genres.

Learners review case studies of how social media recommendation systems have affected public understanding of an issue, then discuss how this might impact individuals’ voting behaviours.

Learners list the classroom devices that must be charged and cooled and recognise that AI systems also require electricity and hardware to run.

Learners create an informational artefact (e.g. written article, podcast or video) explaining how training large AI models compares to household or individual energy use.

Learners research the environmental impacts of AI use and data centre development across different communities. They then identify policies and strategies that promote responsible AI use and development.

With their teacher, learners explore different types of data that can be collected about a person (e.g. height, favorite colour). They note the nuances that data can capture effectively, but draw from their knowledge of each other to identify what is left out.

Learners explore how and why facial recognition technology performs differently across demographics and discuss possible real-world impacts when it is used for decision making in everyday life.

With a teacher, learners evaluate real-world examples of AI being used to influence an event in the real world (e.g. misinformation and disinformation on social media), then explore how an institution’s choices regarding AI use might benefit certain groups while having a negative effect on others.

Learners identify recognisable aspects of artists’ work across AI-generated examples. With a teacher, they discuss whether the examples constitute fair and appropriate use and identify implications for the human artists.

Learners analyse a case study where an AI tool uses one’s browsing history to advertise discounts and sales on products they have viewed in the past. With their teacher, learners weigh the opportunities to save money with risks the AI tool poses for personal privacy. They discuss the pros and cons of using the tool in the long- and short-term.

Learners explore how AI is used to analyze climate data to spot patterns and make predictions. They evaluate trade-offs between the tool’s predictive power and the reduced transparency of an AI model, then weigh the opportunities for clearer forecasts with the risks of “hallucinations,” or if important uncertainties are misunderstood or ignored.

Learners discuss AI-generated images to create story settings based on their classmates’ ideas (e.g. “a jungle in space”), then write new stories inspired by unexpected results.

In response to a teacher’s assignment, learners brainstorm independently before reviewing AI-generated ideas. They compare their own ideas with the AI-generated ones and discuss how exposure to AI suggestions may shape originality, confidence or decision making.

Learners develop their own arguments for a class debate, then analyse AI-generated counterarguments provided by the teacher. They remix and reject the counterpoints to strengthen their final argument.

Learners choose a theme (e.g. “hope,” “winter” or “community”) and use an AI image generator to create a visual representation. They generate one image and write a short explanation of how specific elements of their prompt shaped the final result, noting any unexpected or surprising aspects of the output. They assess how well the generated result matches the idea they had in mind.

Learners design a concept to share about a new club at their school. They generate multiple mission statements, activity ideas and visual styles using text and image generators. Then they compare how different prompts and tools produce varied interpretations of the same core idea.

Learners use AI tools to design a simple interactive project (e.g. a motion-activated light display). They use a text generator to outline how the system should respond, an image generator to visualise the setup and an AI coding assistant to draft basic control code. They generate examples from different tools, compare outputs and revise their prototypes. Learners ensure the plan, visuals and code align into one functional prototype and seek peer feedback before finalising their work.

Learners are shown two different versions of a prompt designed to get feedback on the same idea. They compare AI-generated responses to the two prompts and analyse which prompt led to an output with more accurate or useful feedback. Then they explain what features (e.g. context, specificity, constraints) made it more effective.

Learners design a three-dimensional model of a geometric garden using ratios and proportions. They compare AI-suggested dimensions to their own calculations to ensure mathematical accuracy and consider new creative approaches to inform the design.

Learners compare an early draft of a writing assignment to a final version that was revised with feedback from an AI tool. They annotate key changes to the assignment that indicate when each revision was influenced by the AI tool or their own thinking. They write a brief reflection explaining how they decided which suggestions to use, adapt or ignore to meet their goals.

Learners examine examples of music generated with AI to imitate a famous artist. With their teacher, learners discuss how this raises concerns about copyright, ownership and attributing proper credit to the artist.

Learners compare original work to AI-generated poems, then discuss with their teacher what makes something “original.” They then identify school-based guidelines for attributing AI use.

Learners are presented with examples of art created entirely by a human, generated by an AI system and created by an artist along with AI. Learners consider what choices a human did or did not make for each piece, who should be credited as the artist, what should be protected by copyright and how AI may change artistic creation in the future.

Learners follow guidance established by their teacher to match appropriate tools with academic tasks (e.g. AI for summarising, search engines for research).

As the teacher calls out examples of everyday tasks for the whole class, learners move to corners of the room labeled “AI Only,” “AI-Supported,” “Human Only,” or “Not Sure” based on how they think AI could be used. Learners then defend and explain their choices to peers who had chosen differently and switch corners if convinced.

Learners consider the steps to write an essay (i.e. choosing a topic, researching evidence, organising arguments, drafting paragraphs, revising and proof-reading), then identify which steps AI could assist with and which steps require their own voice and reasoning. They consult their school’s guidelines for AI use to tailor their approach.

Learners compare different approaches to building AI systems by completing a trash-sorting task. First, they consider why random outputs would be unlikely to reliably sort items. Next, they direct a classmate role-playing as a robot to sort images of trash based on specific criteria. They observe how the “robot” responds to new images that do not match the criteria. Finally, they explore how a system could use patterns from collected data, such as a class survey, to improve sorting decisions and determine which approach is most appropriate for different tasks.

Learners compare technology that follows set rules to execute a task (e.g. calculators, traffic lights, thermostats) with technologies that have been trained on examples from data (e.g., image recognition, translation). With a teacher, they consider why each approach suits the specific task.

Learners analyse real-world examples of AI use (e.g. route planning, content moderation or resource allocation). They justify whether a rule-based or machine learning approach is more appropriate – or whether AI should be used at all – based on the requirements, constraint and trade-offs involved. They build a case to make a recommendation, then present their chosen approaches to their peers in a class debate.

Learners are given examples of different problems to identify (e.g. predicting the weather, comforting a friend, detecting plagiarism) and classify each as a type of problem (e.g. data-based, emotional, creative). As a class, they discuss whether AI could help solve the problem and if AI solutions are always necessary to accomplish a goal.

Learners break down the steps to complete a complex research project and identify steps that AI could support (e.g. summarising primary sources and locating relevant commentaries). They work independently to verify claims, assess potential bias in AI outputs and develop their own arguments and interpretations.

Learners are given a time-bound challenge to produce two versions of the same product, with and without AI. Before starting the AI-supported task, teams decide exactly which steps will rely on AI capabilities and which will depend on human strengths. After comparing outcomes, learners revise their task assignments and explain how changing human and AI roles affected the quality, trustworthiness and purpose of the final product.

Learners explore examples of AI tool use across industries (e.g. sports, journalism, music) to determine if there are tasks where AI use might be advantageous and when a human should play a more active role.

Learners watch short videos of self-driving cars navigating different driving situations. As a class, learners compare how a human might make a different decision than a self-driving car and discuss when human judgement should override the algorithm.

Learners work in teams to create a public awareness campaign about a local issue (e.g. reducing food waste, designing safe bicycle routes). They use AI tools to generate messages or visuals, but pause to compare outputs with audience needs and project goals. When AI-generated content is misleading or off-target, learners adjust roles or workflows to produce an accurate and authentic message.

Learners review model cards that describe how an AI system works, its training data, intended uses and possible limitations. As a class, they discuss appropriate uses of the AI system.

Learners examine several examples of AI tools (e.g. a chatbot, recommendation system or route-planning app). For each, learners identify its primary task and explain in simple terms what input it uses and how the outputs execute the task.

Learners analyse how an AI-enabled navigation app uses traffic data to suggest efficient routes. They discuss how the app might reduce travel time for some while increasing congestion, noise or safety risks in certain neighborhoods.

Learners examine an AI system that recommends new music. In small groups, they define criteria for what would make the system successful and compare the system’s recommendations for users with different interests or interaction histories

Learners identify strategies they would use to solve a word puzzle. The teacher presents a sample algorithm for the same task. Using defined criteria (e.g. efficiency, consistency, clarity), learners compare their peers’ strategies to the algorithm, identifying strengths, limitations and trade-offs in each approach.

In pairs, learners draft proposals for an AI tool and share their ideas. Each learner asks their partner a series of questions about their proposal. Next, each partner fills out a model card to identify intended and unintended users, impacts and use cases for the other’s idea. They provide feedback to each other based on the model cards and revise their own tool proposals accordingly.

Learners are tasked with designing an AI tool that can sort recyclable materials. In small groups, they decide what data they could collect on the internet to train the AI tool. With support from a teacher, they discuss which aspects of the data would be most important to train an effective and accurate AI tool.

Learners compare different methods for organising a set of animals, such as grouping them based on physical characteristics. They discuss what happens when new animals are introduced to the set that do not fit into the existing groups.

Learners conduct a class survey to choose a class pet and represent the results using a bar chart. They compare these results with their individual preferences, as well as data from a grade-level survey and a school-wide survey. Learners discuss how increasing the size and diversity of the dataset changes the outcomes of the survey and consider how an AI system might make different recommendations based on its training data.

Learners discuss everyday challenges in their classroom or school, such as finding study resources or sharing information on time. They explore examples of AI tools and identify simple changes that could make these tools more helpful or inclusive, such as clearer instructions, age-appropriate outputs or options for different needs. Learners explain who would benefit from these improvements and why.

Learners consider the use of an AI tool that would predict which library books are most in demand. They analyse how design choices around data sources, categories and recommendations influence what interests are represented. Learners suggest and plan concrete changes (e.g. adding user input, adjusting categories, including feedback loops) to improve variables like access, inclusion or usefulness,. They identify features that ensure recommendations and interfaces are accessible and supportive for learners with different abilities or learning needs.

Working in teams, learners design or modify a model that uses local environmental data to alert community members with asthma about health risks. They practise making deliberate design decisions about which data to include, how predictions are generated and how users receive alerts from the model. Learners test and refine their system using feedback to explain how their improvements better support human wellbeing and community trust.