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Lessons / First lessons with micro:bit CreateAI

First lessons with micro:bit CreateAI

Unit of work

6 lessons

CreateAI, MakeCode

9-12 yrs

These lessons use unplugged activities to introduce AI, link it to familiar technology, and unpack what students already know about sorting data.

Students then get hands-on with the micro:bit CreateAI tool. They collect movement data and sort their physical movements into categories when creating, testing and improving their own ML (machine learning) models. Students then use their ML model as a new input in a MakeCode program that runs on the micro:bit.

Students also have opportunities to discover the risks of bias and importance of diverse training data to make AI tools that work for all people, deepening an understanding of the human role in designing and using AI.

AI literacy:

Collecting training data

How machines learn

Human role in AI design

Impact of AI

Iterating ML models

Perceptions of AI

Testing ML models

Types of AI

Understanding AI

Data literacy:

Cleaning data

Collecting data

Data bias

Download unit of work

Lesson 1 details

Overall key learning

  • Identify different types of technology that do and do not use AI
  • Make connections between how humans and machines sort data according to patterns
  • Collect data for different categories of movements, and clean data sets - identifying and removing outliers
  • Train and test an ML (machine learning) model using physical movement
  • Use ML models to enhance traditional, algorithmic code on a physical computing device
  • Improve an AI system by evaluating and improving the diversity of data used to create it.

Additional skills

Collaboration, logical thinking, critical thinking, presenting, modifying, evaluation, iterative process, testing.

Lesson 1: Introducing AI

This unplugged lesson introduces you and your students to AI. Beginning with a general overview, this lesson focuses on discussion and exploration with a series of slides (or alternatively a worksheet) to prompt discussion on what technologies do and do not use AI.

Key learning:

  • I can describe what AI is in simple words.
  • I can describe different kinds of technology that do and do not use AI.

AI literacy:

Perceptions of AI

Types of AI

Understanding AI

Lesson 1 details

Lesson 2: Exploring patterns in data

In this unplugged lesson, students build on what they already know about data by sorting cards, identifying patterns and differences, and the rules they used to sort them.

Key learning:

  • I can identify patterns and differences in data.
  • I can apply rules to sort data into categories.
  • I understand how machines use rules and pattern recognition to sort data.

AI literacy:

How machines learn

Understanding AI

Lesson 2 details

Lesson 3: Collecting and cleaning data

This lesson introduces your students to the micro:bit’s movement sensor which they use to collect data samples from their own physical movements using micro:bit CreateAI.

Key learning:

  • I know ML models need training data.
  • I can use an accelerometer to collect movement data samples.
  • I can clean data to help my model work well.

AI literacy:

Collecting training data

How machines learn

Human role in AI design

Understanding AI

Data literacy:

Cleaning data

Collecting data

Lesson 3 details

Lesson 4: Training and testing a machine learning model

In this lesson students will train and test a machine learning (ML) model using their own movement data and improve their model through adding more data.

Key learning:

  • I can train and test a machine learning (ML) model to react to different kinds of movements.
  • I can describe that an ML model matches new data to types of data it has been trained on.
  • I can improve an ML model by adding more data.

AI literacy:

How machines learn

Human role in AI design

Iterating ML models

Testing ML models

Understanding AI

Lesson 4 details

Lesson 5: Enhancing code with machine learning

In this lesson students will explore and improve block code that uses their ML model in a Microsoft MakeCode program, download their project to their micro:bit and test it out.

Key learning:

  • I can read block code that uses my ML model.
  • I can use my ML model as an input.
  • I can transfer a program that uses my ML model to a micro:bit and test it when it is disconnected from CreateAI.

AI literacy:

Human role in AI design

Testing ML models

Understanding AI

Lesson 5 details

Lesson 6: Strengthening models through adding diverse data

In this lesson students put their project on a micro:bit and allow other students to test it out. They identify how adding more data could strengthen the ML model by making it work better for more people.

Key learning:

  • I can evaluate an ML model and code running on a micro:bit using live data from different people.
  • I can identify how to make an ML model more robust by adding more data from different people.
  • I understand that ML models perform better if they have been trained on data from different groups of people.

AI literacy:

Human role in AI design

Impact of AI

Testing ML models

Data literacy:

Data bias

Lesson 6 details

This content is published under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) licence.

England National Curriculum

Computing

Aims

  • can understand and apply the fundamental principles and concepts of computer science, including abstraction, logic, algorithms and data representation
  • can evaluate and apply information technology, including new or unfamiliar technologies, analytically to solve problems
  • are responsible, competent, confident and creative users of information and communication technology.

KS2 subject content

  • design, write and debug programs that accomplish specific goals, including controlling or simulating physical systems; solve problems by decomposing them into smaller parts.

KS3 subject content

  • understand how instructions are stored and executed within a computer system
  • undertake creative projects that involve selecting, using, and combining multiple applications, preferably across a range of devices, to achieve challenging goals, including collecting and analysing data and meeting the needs of known users

Scotland Curriculum for Excellence

Technologies: Digital literacy

  • I can explore the latest technologies and consider the ways in which they have developed. (TCH 1-05a)

Technologies: Computing science

  • I understand the instructions of a visual programming language and can predict the outcome of a program written using the language. (TCH 1-14a)
  • I understand how computers process information. (TCH 1-14b)

Northern Ireland Curriculum

Primary using ICT - Desirable features - Computational thinking and coding

  • test and debug at regular intervals and collaborate with others to solve problems as they arise.
  • share their work (possibly using digital tools), respond to feedback and comment on others’ work.

Digital skills curriculum

  • Consider the moral and ethical impact of digital technology on society.

Statutory requirements for mathematics and numeracy - mathematics with financial capability at KS3

  • Develop knowledge and understanding of handling data.

Primary using ICT - Desirable features - computational thinking and coding

  • Test and debug at regular intervals and collaborate with others to solve problems as they arise.

Curriculum for Wales

Digital Competence Framework

Data and computational thinking, problem solving and modelling

Progression step 2

  • I can detect and correct mistakes which cause instructions (a solution) to fail (debug).
  • I can change instructions to achieve a different outcome.

Data information literacy

Progression step 4

  • I can perform analysis on simple data sets including grouping data as appropriate.

Science and technology

Progression step 4

  • I can evaluate and identify ways of improving the reliability of data, taking anomalies into account.

USA CSTA Standards

CSTA K–12 Computer Science Standards, Revised 2017

Computing Systems

Hardware & Software

1B-CS-02 - Model how computer hardware and software work together as a system to accomplish tasks.

Troubleshooting

1B-CS-03 - Determine potential solutions to solve simple hardware and software problems using common troubleshooting strategies.

Data & Analysis

Collection Visualization & Transformation

1B-DA-06 - Organize and present collected data visually to highlight relationships and support a claim.

Inference & Models

1B-DA-07 - Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.

Algorithms & Programming

Modularity

1B-AP-12 - Modify, remix, or incorporate portions of an existing program into one's own work, to develop something new or add more advanced features.

Program Development

1B-AP-15 - Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended.

Impacts of Computing

Culture

1B-IC-19 - Brainstorm ways to improve the accessibility and usability of technology products for the diverse needs and wants of users.

PK-12 Foundational CS Standards, Revised 2026 - Draft 3.0

Disclaimer: This unit will be reviewed for realignment after the final version of the revised PK-12 standards are released in July 2026.

Algorithms & Design

Machine Learning

E4-ALG-02 - Train an AI model to make a classification or prediction.
E5-ALG-02 - Analyze relationships between the properties of training data and an AI model’s output.

Impacts of Algorithms and Design

E4-ALG-03 - Evaluate how different algorithms may affect outcomes, situations, and people with a wide range of needs.

Programming

Programming Development

E4-PRO-05 - Collaborate with a team by offering a meaningful contribution to creating a program.

Programming Fundamentals

E5-PRO-04 - Create a novel program by modifying or combining elements of existing programs.

Data & Analysis

Data Investigation

E5-DAA-10 - Analyze a dataset to identify the nature and possible sources of variability in the data.

Impacts of Data Science

E5-DAA-11 - Analyze the benefits and risks of computing technology that uses collected data.

OECD Empowering Learners for the Age of AI (draft)

Knowledge

The Nature of AI

K1.3. Generative AI uses probabilities to generate human-like outputs across various modalities (e.g. text, audio, visuals) but lacks authentic understanding and intent.

AI Reflects Human Choices and Perspectives

K2.1. Building and maintaining AI systems relies on humans to design algorithms, collect and label data, and moderate harmful content. These systems reflect human choices, assumptions, and labour practices, shaped by unequal global conditions.

K2.4. AI systems are trained to identify patterns among data elements that humans have selected, categorised, and prioritised.

AI’s Capabilities and Limitations

K4.1. AI excels at pattern recognition and automation but lacks emotions, ethical reasoning, context, and originality.

Competences

Engaging with AI

1. Recognise AI’s role and influence in different contexts.
4. Explain how AI could be used to amplify societal biases.

Managing AI

1. Decide whether to use AI systems based on the nature of the task.

Designing AI

2. Compare the capabilities and limitations of AI systems that follow algorithms created by humans with those that make predictions based on data.
3. Collect and curate data that could be used to train an AI model by considering relevance, representation, and potential impact.
4. Evaluate AI systems using defined criteria, expected outcomes, and user feedback.
5. Describe an AI model’s purpose, intended users, and its limitations.

UNESCO AI competency framework for students

Human centred-mindset

4.1.1.1 Foster an understanding that AI is human-led
4.1.1.2 Facilitate an understanding on the necessity of exercising sufficient human control over AI
4.3.1.1 Foster awareness of being a critical AI citizen
4.3.1.3 Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI

Ethics of AI

4.3.2.1 Build awareness and understanding on ‘ethics by design’

AI techniques and applications

4.1.3.1 Exemplify the definition and scope of AI
4.1.3.2 Develop conceptual knowledge on how AI is trained based on data
4.1.3.4 Concretize human-centred considerations in the design and use of AI
4.2.3.2 Provide opportunities to acquire age-appropriate technical skills in AI programming
4.3.3.3 Equip students with skills to test and optimize their self-crafted AI tools

AI system design

4.1.4.1 Scaffold critical thinking skills on when AI should not be used
4.3.4.1 Develop the skills to critique AI systems
4.3.4.2 Foster students’ self-identities as co-creators in the AI era