Tag Archives: education

Supporting a technical AI-focused qualification for young people in England

Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/

Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support learners who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?

Computer science students at a desktop computer in a classroom.

For learners in the UK, there is currently no established qualification that develops technical AI skills. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for learners from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.

We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.

In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).

An established qualification with room to explore AI

The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.

Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.

A teenager learning computer science.

Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.

We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning model as their EPQ artefact.

Building the foundations for an independent AI investigation

Creating a machine learning model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.

Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.

To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.

The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:

  • Learn about each stage of the data science lifecycle
  • Develop the technical understanding they need to make independent decisions during an AI investigation 
  • Gain practical experience of working with data and building a machine learning model
  • Learn about key concepts behind the techniques they use, through theoretical content that supports them to understand not just what to do, but why

The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.

Supporting independent project work

Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.

A young person in a university computing classroom.

After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.

We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.

What’s next?

The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027. 

The post Supporting a technical AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.

Supporting an AI-focused qualification for young people in England

Post Syndicated from Diane Dowling original https://www.raspberrypi.org/blog/support-for-ai-focused-qualification-young-people-england/

Young people see and use artificial intelligence (AI) systems frequently, and hear a lot about how they are applied across many areas of life. That makes them understandably eager to find out more about how AI works. For some, this interest extends to wanting to experiment with the technology to investigate their own questions and build something themselves. How can schools support students who want to take their interest further and gain recognition for the knowledge and skills they develop along the way?

Computer science students at a desktop computer in a classroom.

Currently, there is no widely available subject qualification in AI for students in the UK. This can make it difficult to create space in the curriculum for dedicated AI learning, especially for students from age 14, for whom preparation for high-stakes exams is prioritised over non-examined learning.

We have been working over the past year to develop a research-informed curriculum framework for data science, which lays out many of the foundational skills and knowledge needed to understand and develop AI models. Alongside this, we have been making the case in England for a Level 3 qualification in data science and AI. However, establishing a new qualification can take years.

In the meantime, we are starting to support students in England who choose an established route for extending their skills and getting that effort recognised: the Extended Project Qualification (EPQ).

An established qualification with room to explore AI

The EPQ is a well-established qualification for 16- to 19-year-olds offered in England, Northern Ireland, and Wales by several exam boards (including AQA, Pearson Edexcel, OCR, Eduqas/WJEC, and City & Guilds). It is completed by around 1 in 10 learners at this stage of education.

Learners pursuing an EPQ undertake an extended, self-directed investigation or project. They choose an area of interest where they want to develop their own project and take responsibility for planning, completing, and evaluating the project work.

A teenager learning computer science.

Its self-directed nature makes the EPQ particularly well suited to learners who want to explore emerging areas, such as artificial intelligence and data science. For students fascinated by AI, an EPQ offers an opportunity to do more than simply learn about the technology. With the right foundations and support, they can investigate a question of their own, build a machine learning model, and use that work to gain a recognised qualification.

We are now working to offer those foundations through a new introductory ‘Data Science and AI’ course that will enable learners to build a machine learning (ML) model as their EPQ artefact.

Building the foundations for an independent AI investigation

Creating an ML model involves much more than choosing a statistical technique and using easily available training data. It involves understanding the problem being addressed, deciding what data to use, exploring and preparing that data, building and evaluating a model, and interpreting the model’s outputs.

Learners who want to do this work for their EPQ need not only technical knowledge and skills, but also skills for critical thinking, research, decision-making, evaluation, and reflection — skills that are valuable for any further study and future career.

To help learners develop these foundations, we are creating an introductory ‘Data Science and AI’ course, based on our curriculum framework for data science and designed to be completed before learners begin their AI-focused EPQ.

The course will guide students through a structured investigation based on a recognised data science lifecycle. By working through it, students will:

  • Learn about each stage of the data science lifecycle
  • Develop the technical understanding they need to make independent decisions during an AI investigation 
  • Gain practical experience of working with data and building a machine learning model
  • Learn about key concepts behind the techniques they use, through theoretical content that supports them to understand not just what to do, but why

The course will consist of 10 units, each involving 2 to 3 hours of independent study for learners. We are designing the activities with a no-code approach to allow students to focus on understanding data science concepts and processes.

Supporting independent project work

Importantly, the work learners produce during this course won’t be the EPQ investigation itself. Working through ‘Data Science and AI’ will give learners the knowledge and skills they need to undertake an AI-focused project with greater independence and confidence.

A young person in a university computing classroom.

After they have completed the course, when they begin their EPQ project pursuing a question that interests them, students can apply their new skills and knowledge to make decisions about their AI investigation and document their progress.

We are looking forward to supporting learners in England to turn their interest in one of today’s most significant technologies into the opportunity to develop independent research and project skills through a recognised qualification.

What’s next?

The ‘Data Science and AI’ course will be available to selected schools in England from September 2026, where students and teachers will test the materials and we will learn from their feedback to improve the course materials. We plan to make the course freely available in 2027. 

The post Supporting an AI-focused qualification for young people in England appeared first on Raspberry Pi Foundation.

How to improve students’ problem-solving skills using subgoal labels

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/how-to-improve-students-problem-solving-skills-using-subgoal-labels/

When faced with a programming problem, computing educators often start solving it without a second thought. Before writing a single line of code, experienced programmers use a range of skills to choose their approach and make other key decisions. Thanks to repeated practice, many of these decisions feel automatic and require little conscious thought. However, for beginner programmers, starting a programming problem can feel overwhelming, requiring intense thought and concentration. 

Graphic displaying the Raspberry Pi Foundation's Subgoal labels Quick Read
The Quick Read on subgoal labels

So how can we be more aware of the automatic steps we take and choices we make so that we can break them down for learners, and make it easier for them to solve problems?

In today’s blog, we look at what ‘subgoal labels’ are and how they can be used as a form of scaffolding to support learners in breaking down computer science and programming problems. 

We also share the new subgoal labels Quick Read, which you can download for free to:

  • Find practical tips for using subgoal labels into your lessons
  • Read a summary of the research related to adopting and using subgoal labels in computing education

What are subgoal labels?

Subgoal labels are simple instructions that can be added to tasks — they are particularly useful when added to worked examples. They act as signposts to provide clear directions for a larger task.

Photo of educators programming on Scratch in a computing classroom

Let’s look at the following example: You’re working through a programming problem with your students. The task requires them to draw a shape using a turtle sprite. Instead of just showing learners a complete script, you could instead add these subgoal labels to the worked example. 

Programming problem: Draw a blue square in the top right corner of the screen using a screen turtle.

  • Subgoal 1: Set pen properties (e.g. width and colour)
  • Subgoal 2: Move turtle
  • Subgoal 3: Orient turtle
  • Subgoal 4: Call function

The labels act as scaffolding for students, helping them break down the task and work through it. While the exact number or specific context of the goals might change from one project to the next, the idea is always to break down tasks into smaller, more manageable actions.

How can you use subgoal labels in practice?

You do not need to redesign your entire curriculum to start seeing the benefits of this approach. Here are three practical ways to introduce subgoal labels in your next computing lesson:

  1. Create some labels on any worked examples in your next lesson. You could use this as an opportunity to collaborate with colleagues, and come up with the labels together.
  2. Co-create with AI. Have your students use a generative AI tool to suggest subgoal labels for a piece of code they’ve written, using it as a starting point for a class discussion on structure.
  3. Teach your students how to write their own subgoal labels. This helps them look past the surface details of a task and build a portable problem-solving strategy they can apply to future projects.

Why use subgoal labels in your classroom?

Integrating these labels into your teaching practice offers lots of benefits, for example:

  • Help reduce mental load: Worked examples offer a brilliant way to introduce new programming concepts, but they can actually add to students’ cognitive load. This is because learners are required to process the specific context a problem is set in, in addition to the actual problem they need to solve. Adding labels eases this mental load by directing attention back to the structural steps needed to solve the problem, rather than its surface-level context.
  • Make implicit knowledge explicit: As discussed above, the skill of programming can feel automatic for experienced educators, making it difficult to explain why you’re doing things the way you are. Subgoal labels help you to clearly break down your thoughts and reasoning, making your decision-making visible and structured for your learners.
  • Boost classroom performance and persistence: Research shows that using subgoal labels can improve student performance. In studies using block-based programming, students using subgoal-oriented materials scored 7 to 8% better on assessments. Additionally, students using subgoal labels as part of text-based introductory courses were half as likely to fail or withdraw compared to students who did not use subgoal labels.

Want to know more?

Download the full Subgoal Labels Pedagogy Quick Read as a PDF:

Download the Quick Read

The post How to improve students’ problem-solving skills using subgoal labels appeared first on Raspberry Pi Foundation.

How to design and present clear computing lessons

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/how-to-design-and-present-clear-computing-lessons-mayers-principles/

Learning something new requires effort. Learners take in new information by listening and observing. When a lot of information is presented at once in a lesson, that can create too much cognitive load for learners — a barrier to understanding and engagement.

To help you design and deliver great computing lessons, we’ve written two new Pedagogy Quick Reads focused on Mayer’s Principles of Multimedia Learning. These research-backed principles give you practical strategies to lower your students’ unnecessary cognitive load during lessons, leading to better learning outcomes.

A snapshot of our pedagogy quick reads.

In this blog, we introduce the two new Quick Reads (Designing multimedia for clarity and Designing multimedia for understanding), which you can download for free to:

  • Find practical tips for how you can apply Mayer’s Principles to your lessons
  • Read a summary of the research behind them

The blog also includes some examples for how to apply the principles in your computing lessons.

If you’d like an introduction to the idea of cognitive load, you can find the Quick Read about cognitive load theory here.

In a computing classroom, a girl looks at a computer screen.

What are Mayer’s Principles?

Mayer’s Principles of Multimedia Learning are practical principles that will help you create clearer resources and present information in a way that avoids unnecessary cognitive load for your learners.

Mayer’s Principles are based on three related facts:

  1. You can present information to learners in auditory form (e.g. spoken explanations) and visual form (e.g. written text, diagrams)
  2. There are limits on how much new information people can take in at the same time
  3. Teaching materials that are not well-structured can cause too much cognitive load, which negatively affects learning

Designing lessons for clarity

Our first new Quick Read focuses on the following Mayer’s Principles for making your lessons as clear as possible, so that learners can connect the information they see and hear in real time.

  • Make all the information you include coherent, meaning that it is directly relevant to the learning objectives and does not distract learners’ attention
  • Guide your learners’ attention by using signals such as arrows, bold text, colour, or auditory cues
  • Avoid redundant information, such as a slide with a diagram and a paragraph explaining the diagram, or a slide that you speak about without adding new, complementing information
  • Present related words and visuals in the same space, e.g. place your text labels, or explanations directly adjacent to diagrams, images or code segments they describe
  • Present related words and visuals at the same time, e.g. by pairing narration with imagery

Designing lessons for understanding

Our second new Quick Read shares three Mayer’s Principles for how you can structure your lesson delivery to support your learners’ understanding:

  • Structure lessons or demonstrations into clear, manageable stages or segments, rather than presenting the information all at once
  • When you start a new topic, begin with some pre-training by introducing key terms, components, or goals and how they relate
  • When you present diagrams, flowcharts, or code examples, explain this visual information using the other modality, meaning spoken narration, instead of using paragraphs of text

Applying Mayer’s Principles to your computing lessons

We suggest you consider implementing Mayer’s Principles when you next design new lessons or want to adapt materials that you reuse regularly.

Here are some ideas on how you use both sets of principles in common computing teaching scenarios.

Live coding and code walkthroughs

When displaying a new Python script or Scratch project, avoid adding long, written paragraphs of commentary to explain the code. Instead, place short text annotations or sub-goal labels directly next to the relevant lines or blocks. As you run through the code, use your pointer or live typing to guide your learners’ focus (signalling) and explain in words how the program works at the same time.

Starting a new topic such as networking

Before students move to a new topic, for example networking, consider what words or concepts your class needs to be familiar with. Allocate a few minutes at the start of your lesson for pre-training to introduce terms like LAN or bandwidth and how they relate to the lesson.

Learners in a computing classroom.

Consider how your lesson can be divided into stages to allow for better understanding (segmenting). Each stage should build on the previous one and feed into the next one. For example, when you explain how data moves across a network, you can introduce each step separately before combining them all into a complete model of a network.

Consider how you display visual information to your class. Ensuring related diagrams and labels appear close together, only include relevant materials and no decoration on your slides (coherence), and avoid simply reading out words on the slide identical forms of information (redundancy).

Supporting multilingual learners with Mayer’s Principles

Mayer’s Principles are even more important for educators teaching multilingual learners or non-native speakers. When learners need to work harder to understand the language, poor lesson design can slow down their learning significantly.

Mayer’s Principles can help you with this challenge:

  • Applying the coherence and redundancy principles will allow you to make your explanations and slides as clear and concise as possible
  • Using signaling will mean you help learners to follow along and know what is most important
  • Presenting diagrams that illustrate computing concepts clearly will help your multilingual learners understand your spoken explanation much more easily (modality)

Intentional design for lasting understanding

By intentionally designing and presenting lessons to give the right amount of information in the clearest way, you make it easier for your students to focus and build a lasting understanding of computing concepts. When your lesson materials align with how our brains process information, learners can build stronger mental models and approach independent learning activities with greater confidence.

Read our new Quick Reads to find out more and discover the research behind Mayer’s Principles:

Download the ‘Designing multimedia for clarity’ Quick Read
Download the ‘Designing multimedia for understanding’ Quick Read

The post How to design and present clear computing lessons appeared first on Raspberry Pi Foundation.

What students and teachers in England want from a computing curriculum

Post Syndicated from Rachel Arthur original https://www.raspberrypi.org/blog/what-students-and-teachers-in-england-want-from-a-computing-curriculum/

The UK Government is undertaking the first major review of England’s curriculum and qualifications system since the current national curriculum was introduced in 2014. We believe that this is an excellent opportunity not simply to update the computing curriculum content, but to reconsider what computing education is for, who it serves, and how it prepares young people for life in a digital society.

Today, we are launching a report featuring 6 key priorities for curriculum reform based on discussions we have had with students and teachers. 

Students and educators sit at a table discussing England's curriculum review and The Future of England's Computing Curriculum.

Putting students and teachers at the heart of the conversation 

Too often, curriculum reform happens around students and teachers rather than with them. Yet these are the people who experience computing education every day, and they have valuable insights into what is working, what is not, and what needs to change. 

Our new report is based on a series of student focus groups and teacher workshops held by us and the University of Cambridge in Manchester, London, and Cambridge during spring 2026.

Teachers sit at a table discussing England's curriculum review and The Future of England's Computing Curriculum.

The student discussions included young people currently studying computer science at GCSE and A level (ages 14–18), as well as young people who had decided to not continue with the subject. We also brought together 18 computing teachers from secondary schools across England to explore curriculum priorities and challenges for implementation. 

Although participants in our workshops had differing perspectives, they consistently pointed to the same underlying challenge: the current curriculum no longer reflects the realities of technology, work, or young people’s lives. 

“AI is everywhere now, we need to understand how it works, not just be told not to use it.”
– Student, London

In particular, many students highlighted a lack of confidence in their practical digital skills despite using technology constantly in everyday life.

“I can code a bit, but I don’t know how to use Excel properly, that’s what I’ll actually need.”
– Student, London

Calls for a practical, relevant, inclusive, and future-facing curriculum

Students and teachers are not calling for a less rigorous curriculum. Nor are they arguing that computing should lose its technical foundations, with teachers consistently emphasising the value of understanding computational thinking, programming, algorithms, data, and computer systems and networks.

Instead, students and teachers want to make computing education more practical, relevant, inclusive, and future-facing.

Teachers sit at a table discussing England's curriculum review and The Future of England's Computing Curriculum.

Teachers particularly emphasised the importance of helping students understand how AI systems function, including issues such as bias, training data, limitations, and ethical implications. Participants argued that computing education should help young people understand the technologies shaping their lives, not simply prepare them for examinations. Perhaps the strongest area of agreement was that curriculum reform will only succeed if it is matched by investment in teaching.

Six key priorities for curriculum reform 

Based on our discussions with students and teachers, we have identified several priorities for curriculum reform.

1. Guarantee a core digital education for every young person

All students should leave school with a strong foundation in digital literacy, online safety, data awareness, and AI literacy. These should be treated as essential components of modern education, not optional extras.

2. Modernise curriculum content

The curriculum should focus on contemporary technologies and concepts, including AI, cybersecurity, and data, while trimming content that is overly specific, outdated, or disconnected from modern practice. Foundations such as programming, algorithms, and computational thinking should remain central.

3. Prioritise practical and applied learning

Computing should be taught through making, experimentation, and problem solving. Project-based learning, physical computing, and relevant, real-world applications should become central approaches across all ages.

4. Introduce greater flexibility and specialisation

The revised curriculum should balance a shared foundation with opportunities for students to specialise in areas aligned with their interests and aspirations.

5. Embed inclusion throughout the curriculum

Any reforms should actively address barriers to participation by ensuring inclusive teaching approaches, diverse, relatable role models, and accessible learning experiences.

6. Invest in teachers and implementation

Curriculum reform must be accompanied by sustained investment in teacher recruitment, professional development, and classroom resources. Without this, reforming the curriculum  risks widening existing inequalities in provision.

Read our full report

We believe listening to students and teachers should be central to curriculum reform. You can read the full report now:

Download the report PDF

The post What students and teachers in England want from a computing curriculum appeared first on Raspberry Pi Foundation.

Support your young people with our AI literacy resources

Post Syndicated from Jenni Hutchings original https://www.raspberrypi.org/blog/ai-education-resources-ai-awareness-day/

At the Raspberry Pi Foundation, we believe that alongside learning to code, a crucial part of computing education is building AI literacy skills. Amidst the rapid pace of development and the growing impact of AI tools, it is increasingly important for educators everywhere to feel equipped to address the topic of AI with their learners, to help young people understand their world, be responsible users of AI technologies, and prepare to become the future creators of these technologies. 

We work at the leading edge of AI education, combining research and industry expertise with practical classroom experience to define what AI means for computing education, and how to best support teachers and learners to understand these technologies. 

Whether you are a teacher, a Code Club mentor, or a parent, we have a wide range of free resources to help you teach your young people about AI, and learn more about it yourself.

Explore our teaching resources

We offer a variety of teaching materials to help you bring AI into your setting. You do not need to be a professional educator or have a background in computer science to use these resources, and we provide everything you need to guide your learners with confidence.

Experience AI is our free AI literacy programme, developed in collaboration with Google DeepMind. Through ready-to-use classroom resources, including lesson plans, presentations, and hands-on activities, the programme helps educators all over the world teach their learners about how AI works, as well as its wider social and ethical implications. You and your learners will investigate AI tools, explore real-world uses of AI, and engage with critical issues such as bias, fairness, and transparency, helping learners to understand AI and use it responsibly. The lessons currently available are designed for learners aged 11–14, and we are releasing resources for other age groups this year.

To help bring Experience AI to more educators and learners around the world, we work with a global network of partner organisations, who help us provide tailored and translated resources and offer localised, high-quality training and support for educators in their regions. Experience AI resources are currently available in 19 languages, and have already been downloaded in more than 180 countries. In recognition of its impact, in 2025 Experience AI was named a laureate of the UNESCO King Hamad Bin Isa Al-Khalifa Prize for the Use of ICT in Education.

“[Experience AI] has definitely changed my outlook on AI. I went from knowing nothing about it to understanding how it works, why it acts in certain ways, and how to actually create my own AI models and what data I would need for that. I would 100% recommend others who don’t know much about AI to try it out.” – Student, Arthur Mellows Village College, UK

If you are looking to introduce school-aged young people to AI with short, beginner-friendly coding  and digital making projects, take a look at our collection of Code Club projects about AI and machine learning. These projects are a great way to spark curiosity and investigate how AI and machine learning works.

In the projects, learners get hands-on with a range of AI tools and platforms, and explore different applications of AI, such as image recognition, voice recognition, and (for learners aged 13 and over) generative AI. For example, in Doodle detector, learners use Machine Learning for Kids with Scratch to create a machine learning application that can identify what they have drawn. 

The projects feature clear step-by-step instructions, and many include video tutorials, to help learners work at their own pace and in a style that suits them. We also provide mentor guidance to help you prepare.

An illustration of an image classification application correctly identifying a drawing of an apple.
Learners can explore topics such as image classification through our collection of Code Club projects about AI and machine learning

For resources to support older learners to build their understanding of AI, head to Ada Computer Science. Ada Computer Science is our free online platform for computer science students and teachers, developed in partnership with the University of Cambridge. It provides comprehensive resources for learners aged 14–19 across the breadth of computer science, including detailed learning materials about AI and machine learning. The materials include helpful definitions, clear explanations, and carefully designed self-marking questions to support young people’s learning.

Learners aged 14–19 can build their knowledge of AI and machine learning on our Ada Computer Science platform
Learners aged 14–19 can build their knowledge of AI and machine learning on our Ada Computer Science platform

Explore our learning and training opportunities for educators

To help you build your knowledge around AI technologies and grow your confidence to teach your young people about this important topic, we offer a range of learning resources and professional development opportunities, all for free. They are open to everyone, so we invite you to dive into any that interest you.

For flexible, self-paced learning options, take a look at the following free online courses, which cover a range of topics within AI:

  • Introducing AI: Investigate how AI systems work and how to evaluate them, and explore the benefits, risks, and ethical issues related to them. This course is made up of 2 modules, and takes around 2–4 hours to complete.
  • AI literacy for teachers and school leaders: Learn how to support your students and staff to understand, use, and critically assess AI technologies. This course takes around 1–2 hours to complete. 
  • Machine learning and AI: Discover machine learning and how it works, and train your own AI models using free online tools. This course is made up of 4 modules, and takes around 4–8 hours to complete.
Learn more about AI through our free online courses for educators
Learn more about AI through our free online courses for educators

You might also be interested in Hello World, our free magazine and podcast for educators teaching computing and AI. Each magazine is packed with resources, discussions, news, and ideas, and you can subscribe to receive each issue as soon as it is released. The next issue, coming in July, will focus on critical thinking in the age of AI. You can download our previous issues to explore articles about a variety of topics within AI too. What’s more, you can continue your learning with the Hello World podcast, which accompanies the magazine and features discussions with educators and researchers from around the world. Visit the podcast page to discover previous episodes exploring vibe coding and programming education, AI education around the world, and more.

Hello World Issue 29 - Safety & security
Hello World Issue 29 – Safety & security

To learn about research-informed teaching strategies to help you as you explore AI with your learners, you can read these short Pedagogy Quick Reads:

  • Anthropomorphism: Explore how to help learners avoid thinking of AI systems as human-like, to support their understanding.
  • Computational Thinking 2.0: Consider how computational thinking is evolving and how to help learners develop the computational thinking skills they need to understand modern digital systems involving AI.
  • Feedback literacy: Explore how feedback literacy can help teachers and learners interact effectively with feedback generated by AI tools.
A Pedagogy Quick Read entitled ‘The effects of anthropomorphisation on students’ mental models of AI’.
A Pedagogy Quick Read entitled ‘The effects of anthropomorphisation on students’ mental models of AI’.

For more insights from computing education research, you can join our monthly online research seminars. Our 2026 research seminar series focuses on teaching about AI across the curriculum, delving into research on teaching and learning about AI from disciplines beyond computer science, including the arts, sciences, and humanities. Our next seminar takes place on 16 June, and you can catch up on previous seminars from our current series here. You can also explore our archive of recordings from previous seminar series, with themes including teaching about AI and data science, teaching programming (with or without AI), and more.

Bring AI into your classroom today

All of these resources and learning opportunities are available to anyone interested in educating young people about AI, anywhere in the world. We hope they help you gain understanding, confidence, and inspiration to guide your young learners to engage with AI safely and responsibly, and to learn valuable skills that will help them navigate their world.

“Let the students explore, advance, and grow. And who knows — maybe one of our students will go on to become a mentor or leader in this field someday.” – Ana Judith Zavaleta, computer science teacher, Mexico, speaking about Experience AI

If you are in the UK, you can also use these resources to get involved with the first-ever AI Awareness Day, a new nationwide campaign designed to build AI literacy across UK schools, which is taking place on 4 June.

The post Support your young people with our AI literacy resources appeared first on Raspberry Pi Foundation.

Professional development: How to stay ahead in a fast-changing subject

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/professional-development-how-to-stay-ahead-in-a-fast-changing-subject/

What does great training for computing teachers look like?

High-quality professional development (PD) is one of the most effective ways to improve your students’ outcomes, and participating in PD is a core part of being a teacher. By changing and refining your teaching practices, you can create a direct, positive impact on the young people we teach.

Image displaying the Professional Development Pedagogy Quick Read, from the Raspberry Pi Foundation.

In this blog, we share our new professional development Quick Read, which you can download for free to:

  • Find practical tips for how to use and design effective professional development opportunities
  • Read a summary of the research behind effective PD

The unique impact of professional development for computer science educators

Professional development is vital for anyone teaching computing. In many subjects, the curriculum stays the same for decades. Computer science, however, can change quickly — as with the development of new technologies such as AI and quantum computing, and new hardware — and PD can help you stay up to date.

An educator teaches students to create with technology.

Another challenge is that many educators who teach computing are not necessarily subject specialists. So educators often benefit from building their own subject knowledge and learning new computing-specific pedagogical approaches through PD.

What makes effective professional development?

Drawing on academic research and our own expertise, we’ve identified several key principles that make professional development effective for computer science educators:

  • Learner-focused and research-informed: PD should be based on evidence and equip you with the skills and confidence to make real changes in your practice
  • Sustained and actionable: The best outcomes happen when you can select your own learning pathways and you’re given the time to test, adapt, and reflect on new approaches
  • Collaborative and contextual: Sharing ideas and new ways of thinking with other educators in a low-stakes environment can help you and your peers benefit from different perspectives and experiences

There may be factors that influence your teaching that you have little control over: you might teach computing as a standalone subject, or you may be required to weave computer science into other lessons across the curriculum. Or you might be working with older devices or limited internet access, all of which have an impact on your practice.

Effective PD should recognise these realities, and offer practical tools that work for your specific classroom and students.

How to find or design PD for computing educators

You can use the principles in our latest Quick Read when you’re looking for your next training course or if you are designing a session for your team. The full list of principles is available in our Quick Read, but here are some ideas for you to consider:

  • Focus on small, manageable changes: Rather than trying to overhaul your entire teaching practice at once, reflect on one approach at a time and adapt it as necessary before moving on to the next
  • Encourage low-stakes rehearsal: Practise new techniques with peers before implementing them in a live lesson
  • Align with school priorities: Ensure your self-directed learning also meets the wider needs of your department or school

You can read more about the principles of effective PD in our Quick Read.

The benefits of professional development

Potential benefits for teachers:

  • Provides a clear structure for updating your subject knowledge and teaching methods
  • Helps you feel more confident teaching 
  • Allows you to take ownership of your career journey and focus on what matters most to your students

Potential benefits for learners:

  • Improved learning outcomes through:
    • Higher quality lessons
    • More engaging lessons 
    • Lessons better suited to their individual needs

Our new Quick Read shares tips on how to best use these principles in your setting.

Download the Quick Read

The post Professional development: How to stay ahead in a fast-changing subject appeared first on Raspberry Pi Foundation.

What does ‘thinking’ mean now?

Post Syndicated from Meg Wang original https://www.raspberrypi.org/blog/what-does-thinking-mean-now/

At a time when artificial intelligence (AI) systems and tools based on large language models (LLMs) are being rapidly introduced into industries and daily life, the basic definition of ‘thinking’ and the essential skills we teach the next generation are being called into question.

Shuchi Grover showing children something on a laptop screen
Dr Shuchi Grover working with learners in a classroom.

In this interview, Dr Shuchi Grover, a leading voice in computing education who has recently become our Director of Research and Impact, shares how her work in computational thinking is evolving.

Can you share the story of your path in computer science (CS) education?

Most people in the education and CS education world know me from my research in computational thinking and K–12 CS education over the last 15 years. What is less known, perhaps, is that I started my career as a software engineer after completing my undergraduate and graduate studies in CS. About 25 years ago, I made a concerted shift to education, completing a Masters in Education from Harvard University in 2003, and then after a gap earning a PhD in the learning sciences (with a focus on K–12 CS education) from Stanford University in 2014.

Over these last two and a half decades, I have trained my efforts on helping young learners and school-aged children develop 21st-century competencies in computer science, data science, AI, and cybersecurity; as well as on STEM and non-STEM learning experiences that integrate computational thinking, AI, CS, and data science. My research has also attended to promoting interest and a sense of belonging in CS among learners from historically underrepresented groups.

Two students use computers in a classroom.

I recently joined the Raspberry Pi Foundation as Director of Research and Impact. I feel very fortunate, as this role builds on all the work I have done over the course of my professional life and also affords me an unparalleled opportunity on a global scale to continue this work I’ve been so passionate about in both formal and non-formal learning settings.

You are well-known for your work on computational thinking. Since the development of LLMs, how has the definition of ‘thinking’ been changing?

This question is deep and thorny, and I’m not sure we have a complete answer to it yet. I believe that thinking as a human endeavour continues to be valid and means what it always has meant: a cognitive process that involves making new connections and creating meaning. In the education literature, thinking is often equated to problem solving. So teaching students ‘thinking skills’ has meant teaching them logic and ways to solve problems — typically in the context of a domain. In the context of K–12 CS education, computational thinking essentially means computational problem solving.

What changes with LLMs is not the definition of thinking itself, but rather what thinking skills students need most urgently. For students, the idea of ‘critical thinking’ has become much more critical (no pun intended) in an era when LLM-based tools offer quick and easy ways to produce answers. Students need to be equipped with the skills to evaluate AI outputs, and to follow up in deliberate and mindful ways to ensure that the AI-generated answer they ultimately take away is factually accurate, unbiased (to the extent that it can be), and valid for their context. They should also have the ability to recognise when an output is not suitable for their purposes, and when they would be better off approaching a problem or project as they would have in the pre-LLM era. These kinds of metacognition and evaluation skills must be crucial elements of AI literacy training.

How has data changed AI, and how has it impacted CS education?

Over the past 5 to 10 years, the scope, pervasiveness, and complexity of computing applications have grown substantially. This growth has been propelled by developments in AI and machine learning (ML). Many of the ML methods that underpin these developments have been in existence for much longer, but two key ingredients were still needed: large quantities of data, and the requisite computational power to process those quantities of data efficiently. Around 10 years ago, these became a reality. Combining so-called ‘big data’ captured from the countless human activities on the World Wide Web with new, powerful graphics processing units (GPUs) enabled AI scientists to build powerful prediction, classification and, most recently, generative AI models. Thus these scientists ushered in a new paradigm of computing that is data-driven. 

Learners at laptops in a computing classroom.

This has expanded the scope of what we need to teach students as part of CS education. In the context of AI and ML, you now have traditional programs that follow the algorithmic, deterministic paradigm of programming, but also ML applications that follow a data-driven, non-deterministic/probabilistic paradigm. CS curricula must help students develop an understanding of both. And data and data science are the crucial connective tissue between CS and AI/ML, so data literacy (which also captures elements of data agency and data equity) is critical to CS and AI learning experiences. 

Ethical issues in the context of data and AI have become more heightened and pertinent: issues of data privacy, safety, bias, responsible and explainable AI, and most importantly, impacts of AI systems on society. Understanding of these issues — what we can call ‘sociotechnical literacy’ — needs to be much more central to CS education now.

Considering the advances in AI and LLMs, what computing-related skills that we are used to teaching as part of CS are still relevant for young learners?

Let me begin by saying that there is no AI without CS. So understanding CS is important and foundational even in this age of AI and LLMs. The rationale for teaching CS and coding to learners aged 5 to 18 has always been primarily about (a) preparing the next generation to understand, and thrive in, a world where countless aspects of day-to-day life are driven by computing, and (b) providing them with the tools and skills for problem solving and creative expression. That goal has not changed. Foundational coding skills are still important and relevant for learners.

Photo of a class of students at computers, in a computer science classroom.

However, there is the new reality we must contend with: it is now easy to produce accurate code using LLM-based tools. We need good research on what this means in terms of how we teach coding. There are many questions related to this issue for which we need empirical evidence: What are the foundational skills for programming effectively with AI tools? What CS topics, skills, and concepts must we emphasise or de-emphasise? Could teachers be supported by generative AI tools in teaching coding, and if so, how? Will use of AI tools result in poor learning for students? How might students leverage LLM tools in ways that don’t harm their foundational understanding of coding concepts, and at what age and stage? What kinds of LLM tools are safe and suitable, and what preparation must students have before they use them? What bigger, more sophisticated projects might students create with the help of an LLM tool? How might LLM tools aid student learning through formative feedback? Can LLM tools aid in metacognition by prompting reflection at the right moments in a project? These are just some of the many, many questions we need to answer to shape CS education over the coming years.

A version of this interview also appears in issue 29 of Hello World, available as a free download. Subscribe to the magazine to never miss an upcoming issue.

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Computing and AI for all: From classrooms to national dialogue in India

Post Syndicated from Mamta Manaktala original https://www.raspberrypi.org/blog/computing-and-ai-for-all-from-classrooms-to-national-dialogue-in-india/

On 7 February 2026, we witnessed something special in Bhubaneswar: a day where classroom experience took centre stage in conversations about computing and AI education in India at our Computing and AI for All conference.

At this event, we brought together educators, researchers, and system leaders for shared learning, reflection, and professional dialogue where classroom practice was placed at the heart of the discussion.

An event to connect conversations

Across computing and AI education in India, conversations about policy, pedagogy, research, and future readiness often take place in separate spaces. At Computing and AI for All, we wanted to connect these strands in a national forum where classroom practitioners, researchers, and system leaders could speak with each other about computing and AI education. 

The theme ‘Computing and AI for all: Classroom to policy’ connected:

  • Ecosystem and policy
  • Pedagogy and innovation
  • AI and future readiness
  • Research, evidence and impact

Bringing these conversations together helped shape a richer dialogue than we’ve seen in many traditional education forums.

Grounding in classroom experience

The response from educators to the opportunity to attend was both energising and encouraging. 165 educators and 22 invited guests joined us on the day. The event also featured 35 paper and poster presentations, reflecting the breadth of classroom practice, research, and innovation that teachers were keen to share.

An aerial view of the attendees of the Computing and AI for All event.

The conference also brought together diverse contributors. Alongside teachers, there was representation from organisations such as Computer Science Teachers’ Associations, the University of Southampton, Quest Alliance, and Learning Links Foundation. Also part of the event were attendees from government bodies including TTWREIS – Telangana Tribal Welfare Residential Educational Institutes; PSSS – Panchasakha Shikhya Setu Sangathan (a Govt. of Odisha initiative); and IIITMK Kerala. The presence of an international delegate from Nepal further broadened the exchange of perspectives.

First and foremost, the event was rooted in classroom practice. 88% of participants were ICT teachers, with strong representation from Odisha and Telangana, where we have been working on partnerships to support computing educators. A significant proportion of the educators present were attending a computing conference in person for the first time, so it was a milestone in their professional journey.

Teachers present a poster at the Computing and AI for All event.

Teachers stepping onto a national stage to present their classroom experiments, research, and reflections signaled an important shift: computing and AI education in India is no longer something designed solely for teachers. It is increasingly being shaped by teachers and informed by their lived classroom realities and professional expertise.

Ideas, evidence, and inspiration

The conference featured a full-day academic programme beginning with a formal inauguration and an introduction to our work in India.

The group performing the formal inauguration of the Computing and AI for All event.

The keynote by the Foundation’s Director of Research and Impact, Shuchi Grover — ‘Democratizing the future: Why computing and AI literacy matters for every child, everywhere”’ — set an equity-driven, future-focused tone. It reminded us that AI literacy is not optional; it’s foundational for learning and opportunity.

In the panel discussion on ‘Embedding ethical AI in everyday teaching: From principles to classroom practice’, teachers, researchers and leaders exchanged ideas about how to integrate responsible AI without losing sight of learning goals.

Across sessions, teachers and researchers shared a wide range of classroom-centred insights, including:

  • Using generative AI for algebraic discovery
  • Designing AI-supported personalised learning models
  • Inclusive coding practices with Scratch
  • Using AI to reduce teacher workload
  • Low-cost computing innovations in K–12 classrooms
  • Hands-on technical explorations, such as Kali Linux on Raspberry Pi

These sessions reflected both pedagogical creativity and deep engagement with everyday classroom challenges.

The structured poster gallery created space for peer feedback and deeper discussion around critical themes such as:

  • AI for first-generation learners
  • Classroom research evidence
  • AI tools to support ICT engagement

What educators told us

In the feedback we captured, educators described the conference as:

  • Highly educative
  • A space for cross-state collaboration
  • A confidence-building platform for presenting work

One participant shared: “I’m honoured to be part of [the conference]. It was inspiring to listen to educational experts sharing valuable insights on AI tools and education.”

Many participants asked us to expand the event, suggesting a two-day format, extended hands-on AI workshops, and practical demonstrations they could take back to their classrooms.

Looking ahead

Our Computing and AI for All event formed a national, educator-led platform linking classroom innovation, AI literacy, research evidence, and policy dialogue.

What stood out most was the spirit of participation:

  • Teachers sharing real classroom experiments
  • Researchers listening
  • Policy voices engaging
  • First-time presenters stepping into a national forum

If AI is shaping the future of work and society, then teachers need to play a part in shaping the future of AI education. Computing and AI for All was one move in this direction that we were pleased to facilitate — and it is only the beginning.

The post Computing and AI for all: From classrooms to national dialogue in India appeared first on Raspberry Pi Foundation.

‘Using PRIMM to teach programming’: A new short course for educators

Post Syndicated from Kate Irwin original https://www.raspberrypi.org/blog/using-primm-to-teach-programming-a-new-short-course-for-educators/

At the Raspberry Pi Foundation, we believe that learning to program equips young people with the knowledge and skills they need to thrive in an increasingly digital world. For many educators, teaching programming effectively can be challenging, particularly when their learners are at different stages in their programming journey. Ask learners to write code too early, and they might struggle or feel intimidated. Rely too heavily on step-by-step instructions, and you limit learners’ chances to explore ideas or develop deeper understanding.

Using PRIMM to teach programming artwork

The PRIMM framework — Predict, Run, Investigate, Modify, Make — provides educators with a structure for teaching programming. This research-informed teaching approach balances support with independence and helps learners build their understanding before they write their own code, whatever their starting point.

To help educators use this approach confidently, we have launched a new short online course, Using PRIMM to teach programming, which is available on our new Training Hub platform for free.

What is the course about?

This practical, self-paced course gives educators the knowledge they need to use the PRIMM approach to design and adapt programming activities to suit their learners.

The course takes 1–2 hours to complete, and we have designed it for educators working in formal or non-formal learning environments around the world, using any block-based or text-based programming language. All you need is some experience of creating and adapting simple programs.

The course starts with considering the five stages of PRIMM, when and why to use each stage, and how they work together to support learning. It covers how PRIMM aligns with key teaching principles such as scaffolding, managing cognitive load, and progression, and examines how the approach supports formative assessment by making learners’ thinking — and any misunderstandings — more visible.

Active, social learning

Although pedagogy forms the core of this course, we have deliberately avoided a theory-heavy approach. Instead, the course is designed to help you learn through hands-on activities. By reflecting, taking part in discussions with other computing educators, and completing practical tasks, you will explore how PRIMM works in real teaching contexts.

A computer science teacher sits with students at computers in a classroom.

After an introduction to the core ideas of PRIMM, you will design a new programming activity, or adapt an existing one, using the PRIMM structure. This will support you to think carefully about what your learners know and can do, likely misconceptions, and how each stage of PRIMM can be used effectively, including when your learners have varied learning needs and levels of programming experience.

With its emphasis on activity design, the course will support you to develop resources you can use and keep adapting in your own setting. By the end, you will have a complete PRIMM activity designed specifically for your learners, and a clear sense of how to teach programming in a structured and supportive way.

Join the course on the Training Hub

Using PRIMM to teach programming is available on our new Training Hub, where we offer all our professional development courses for free. The Training Hub offers flexible, reflective learning experiences across a range of topics, helping you build your subject knowledge and bring research-informed teaching approaches into your day-to-day practice.

I want to join the course

Whether you are an experienced computing teacher, a volunteer educator, or a parent looking to support their child’s learning, we invite you to join us there.

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How to evaluate your use of classroom technology with the PICRAT framework

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/how-to-evaluate-your-use-of-classroom-technology-with-the-picrat-framework/

There’s always something new to consider when teaching with technology. From the latest advancements in AI, to new software and hardware updates, it can be difficult to know which tools to use and how to incorporate it effectively into your lessons.

In today’s blog, we explore the PICRAT framework and how it can help you reflect on your use of technology in the classroom. 

We also share our new PICRAT Quick Read, which you can download for free to: 

  • Find practical tips on how to use the PICRAT model when planning your lessons
  • Read a summary of the research behind the framework

What is the PICRAT framework?

Technology is constantly changing, and educators must continually decide what tools to use in their practice. To help with this challenge, researchers started developing theoretical models that teachers (especially student teachers) could use to reflect on how they integrate technology in their classrooms.

You might already be familiar with frameworks like TPACK (Technology, Pedagogy, and Content Knowledge) and SAMR (Substitution, Augmentation, Modification, Redefinition). While these models are useful, the PICRAT framework was created to address gaps in these earlier models, offering a clearer, student-focused approach. Significantly, it encourages you to treat technology as a tool to support learning, rather than the goal itself.

It asks two simple questions: “How are students experiencing the technology?” and “How does this impact your practice?”. The answers to these questions form a matrix as pictured below. 

PIC (which runs along the y-axis) refers to the student’s relationship to the technology:

  • Passive – Students receive learning through technology
  • Interactive – Students interact with the content or other learning through technology 
  • Creative – Students construct knowledge using technology

RAT (which runs along the x-axis) refers to how the teacher uses the technology:

  • Replaces – Using technology but with an existing pedagogy
  • Amplifies – Using technology to improve pedagogy or outcomes
  • Transforms – Using technology to create new pedagogical practices

How can I apply the PICRAT model?

First choose the lesson you’re planning to deliver. Consider what activities you’ll be running and the technologies involved. You’ll then be able to plot where they sit on the matrix using the PICRAT acronym.

For example, if you are teaching a lesson on Python loops, you might initially plan for students to watch a pre-recorded coding tutorial on their laptops. In this scenario, the student experience is Passive (receiving info via tech), and the teacher’s use is Replacement because the video simply replaces a live lecture. To move up the matrix, you could instead have students use an online IDE to complete a “Parson’s Problem” puzzle where they rearrange blocks of code to fix a loop. This shifts the activity to Interactive and Amplification, as the digital tool provides immediate debugging feedback that a paper-based exercise could not.

Educator presenting in a classroom.

Next, think about how you might move your practice forwards. Although every position on the matrix has its own value, the framework is hierarchical. The overall goal is to try to move your practice towards the top right of the matrix to be Creative and Transformative.

To help you achieve this, take some time to reflect on your current lessons, activities, and the technologies you use. Ask yourself questions like:

  • What does the technology I’m using offer that could be used to amplify my practice?
    • What benefits would this have for students?
  • Does the technology present opportunities for students to interact with each other, not just the technology?
  • What other technological tools might support collaboration? 

Research highlights that technology is rarely used in ways that allow young people to be creative. By using the PICRAT matrix, teachers can identify missed opportunities and explore ways to transform their lessons, ensuring learners can be creative and thrive.

The benefits of the PICRAT model

Potential benefits for educators:

  • The framework encourages meaningful reflections, allowing teachers to easily evaluate how they’re using technology within their lessons
  • Reflections and the PICRAT matrix helps teachers to identify missed opportunities and gaps in their practice, ultimately leading to better student experiences
Photo of educators sharing ideas in a classroom.

You can use the PICRAT framework as part of your own reflections, or as part of a group activity. It’s a great way to spark discussion about technology integration with colleagues and improve best practices.

Want to find out more about the PICRAT framework?

If you’d like to learn more about the PICRAT model, you can download our Quick Read for free via our new Pedagogy Quick Reads page.

Download the PICRAT Pedagogy Quick Read

The post How to evaluate your use of classroom technology with the PICRAT framework appeared first on Raspberry Pi Foundation.

Build an AI-powered course recommender using Amazon Bedrock and AWS End User Messaging

Post Syndicated from Ruchikka Chaudhary original https://aws.amazon.com/blogs/messaging-and-targeting/build-an-ai-powered-course-recommender-using-amazon-bedrock-and-aws-end-user-messaging/

Educational technology (EdTech) providers face the challenge of maintaining seamless, personalized communication and presenting the right recommendations to their diverse stakeholders. This post explores how combining Amazon Web Services (AWS) End User Messaging and WhatsApp Business API with the advanced AI capabilities of Amazon Bedrock can transform educational engagement.

In this post, we explore use cases that are reshaping the EdTech industry. We discover how application automation can streamline admissions and enrollment processes, making them more efficient and user-friendly. We demonstrate how instant student engagement can be achieved through AI-powered, personalized interactions that keep learners motivated and connected. We showcase how real-time course feedback mechanisms can help educators adapt and improve their teaching methods. We also examine how student support can be automated using intelligent assistants that provide continuous, all-day assistance while maintaining a personal touch.

We show you how to build an AI-powered course recommendation system. We explain how to set up WhatsApp Business API integration with Amazon Bedrock, implement smart search capabilities for course matching, and create a scalable serverless architecture. You’ll learn how to build meaningful analytics dashboards to track engagement and learn best practices for handling errors and maintaining system reliability. Whether you’re an EdTech professional or a cloud architect, this guide gives you practical insights into combining conversational AI with educational services.

Use cases

  • An AI-powered personalized learning pathway generator that automatically recommends customized content based on individual student performance metrics and learning requirements
  • Course improvement suggestions and real-time course feedback
  • A smart communication orchestrator that delivers role-specific, automated notifications and updates across multiple channels to enhance student and parent engagement
  • An early warning system using predictive analytics to identify at-risk students through real-time monitoring of engagement metrics and performance indicators
  • Student support automation with always available AI assistant support, FAQ handling, escalation management, and multilingual support

Prerequisites

  • An AWS account
  • AWS End User Messaging set up with WhatsApp channel enabled
  • A pre-existing WhatsApp Business account
  • Amazon Bedrock setup must be completed with preferred model
  • Amazon Quick Sight for the AWS Region must be enabled

Solution overview

With this solution, users can discover and order educational courses through WhatsApp conversations. Instead of navigating complex websites, the user can send a WhatsApp message saying, “I want to learn Python programming.” They’ll receive personalized course recommendations instantly. The architecture processes WhatsApp messages through AWS End User Messaging, uses Amazon Bedrock for AI-powered conversations, performs semantic search with Amazon OpenSearch Serverless, and captures analytics for business insights. (For step-by-step implementation and rollback guidelines, see the sample course recommendation system.) The following architectural diagram illustrates a modern AI-powered course recommendation system that uses multiple AWS services.

Figure 1: AI-powered course recommendation system

Message processing

When users send WhatsApp messages, AWS End User Messaging captures them and publishes events to an Amazon Simple Notification Service (Amazon SNS) topic. This creates a decoupled architecture where multiple services can process the same message events independently. AWS Lambda functions subscribe to these events, facilitating reliable message processing during high-traffic periods. The decoupled design provides several advantages:

  • If one component fails, others continue operating
  • You can add new message processors without affecting existing ones
  • The system automatically scales based on message volume without manual intervention

AI conversation engine

Amazon Bedrock with Claude 3 Haiku powers natural language understanding. It is configured specifically for WhatsApp with instructions for short paragraphs, relevant emoji, and mobile-optimized responses.

AI agents

The agent maintains conversation context and handles structured actions such as course search, detail retrieval, and booking through defined functions. The following workflow is the agent action flow and sample code:

Agent flow

  1. Greets user → Understands intent → Searches courses → Provides details → Facilitates booking
  2. Maintains context throughout the conversation
  3. Can switch between actions based on user responses
  4. Handles complex queries by combining multiple actions

Sample code

The following is sample code to create a Bedrock agent using AWS CDK:

    agent = bedrock.CfnAgent(foundation_model="anthropic.claude-3-haiku-20240307-v1:0",
     instruction="""
     Format for WhatsApp: short paragraphs,
        focus on technical courses only
       """,
      action_groups=[# Functions for search, details, booking]
)

Semantic search

Traditional keyword search can miss the user’s intent. The application uses Amazon Titan Embeddings in Amazon Bedrock to convert courses and queries into vectors, enabling semantic understanding. When users ask for “cloud computing courses,” the system can understand related terms such as “AWS” and “serverless” without exact matches. Amazon OpenSearch Serverless handles vector similarity matching combined with traditional filters for course price, level, and duration.

Analytics pipeline

Every WhatsApp message interaction generates business intelligence. Messages are stored in Amazon Simple Storage Service (Amazon S3) with date partitioning, catalogued through AWS Glue, and made queryable using Amazon Athena. Teams can analyze user behavior, popular topics, and conversion rates through Quick Sight dashboards. The following dashboard shows example widgets displaying pie-chart breakdown of message delivery status and count of messages per day.

Figure 2: Amazon Quick Sight dashboard

As shown in the following dashboard, Amazon Q in QuickSight enables you to explore and analyze your data using conversational AI capabilities.

Figure 3: Amazon Quick Sight dashboard showing chat window

Error handling and resilience

Such highly scalable and distributed solutions require robust error handling. The application has exponential backoff and retries for API calls, meaning the system can gracefully handle rate limits and temporary service unavailability.

The following is sample code for error handling and resilience:

python
def retry_with_backoff(func, max_retries=5):
retries = 0
backoff = 1
while retries < max_retries:
try:
return func()
except ThrottlingException:
sleep_time = backoff + random.uniform(0, 1)
time.sleep(sleep_time)
backoff = min(backoff * 2, 32)
retries += 1
raise Exception("Max retries exceeded")

Business impact

With the global EdTech market expected to reach $165 billion by 2026, educators and institutions are seeking solutions to prevent student dropouts, improve learning outcomes, and maintain their competitive advantage. Poor personalization can lead to decreased student engagement, lower course completion rates, and ultimately revenue loss.

Implementing AI-driven personalization and communication systems means institutions can significantly improve student retention rates, boost learning outcomes, and create a more engaging educational experience, which directly impacts their bottom line and reputation in an increasingly competitive educational landscape. This solution could transform educational delivery through intelligent personalization and operational excellence. A serverless architecture can help educational institutions focus on content quality rather than infrastructure management while potentially maintaining rapid response times for course searches. The system’s analytics capabilities could offer insights into student behavior and course preferences, helping shape future curriculum development.

With mobile optimization, institutions can better serve the growing population of digital-first learners. The combination of automated scaling and pay-per-use pricing could create opportunities for cost optimization, and real-time dashboards can be used to facilitate data-informed decision-making. Such improvements in user experience and operational efficiency could lead to enhanced student engagement and institutional growth in the evolving education environment.

Sample conversation

The following video shows how a user can interact with the generative AI-powered course recommendation system and receive course recommendations.

Future enhancements

We’re expanding to more messaging platforms, adding voice integration through Amazon Connect, and implementing predictive analytics for personalized recommendations. The serverless architecture makes these additions straightforward without infrastructure changes. Future scenarios could involve:

  • Educator and student support – This solution can be enhanced for student and educator experiences. For educators, it can automate administrative tasks. For students, it can create personalized engagement campaigns, a communication approach that could be significantly more effective than traditional methods.
  • Digital admission process flow – The solution integrates AWS Bedrock AI with WhatsApp Business API to streamline digital admissions. It can enable instant document verification, guide secure payments, and provide automated updates, all within the AWS End User Messaging WhatsApp channel. This AI-powered system could transform the complex admission process into an efficient, chat-based experience, benefiting both institutions and applicants.
  • Parental support and study material management – The system could intelligently distribute learning resources based on student needs, send automated schedule updates, and provide personalized progress reports to parents through WhatsApp. Parents could receive AI-curated study materials and real-time updates about their child’s academic performance, homework assignments, and upcoming assessments through familiar chat interactions. This integration could transform traditional parent-teacher communication into an efficient, automated system while providing timely access to relevant educational resources.

Conclusion

The WhatsApp course recommender agent demonstrates how modern AWS services can create sophisticated, AI-powered conversational experiences that scale automatically and provide rich business insights. The serverless architecture provides cost-effectiveness while maintaining enterprise-grade reliability. Key architectural principles that make this solution successful include event-driven design for scalability, AI integration for natural interactions, semantic search for superior user experience, customizable analytics for business intelligence, and infrastructure as code (IaC) for reliable deployments.

For organizations considering similar implementations, we recommend focusing on user experience optimization, robust error handling, comprehensive monitoring, and gradual feature rollout. The conversational AI environment is rapidly evolving, and solutions that prioritize user experience while maintaining technical excellence can drive the most business value. This implementation can serve as a reference architecture for building production-ready conversational AI systems on AWS, demonstrating patterns that can apply across industries and use cases.

About the authors

How to strategically plan your computing curriculum

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/how-to-strategically-plan-your-computing-curriculum/

Traditionally, curriculum planning has often looked like a linear list: Topic A leads to Topic B, which leads to Topic C. However, as educators we know that learning rarely happens in such a simple, linear way. Concepts are regularly covered in different overlapping topics, and students can often take different routes to reach the same destination.

Image displaying the Learning Graphs Quick Read.

In today’s blog we’re exploring learning graphs, a helpful tool that you can use to plan your computer science curriculum. We’ll share how they can provide educators with a clear, structured way to visualise students’ non-linear progression in a subject.

We also share our new Pedagogy Quick Read about learning graphs, which you can download for free to:

  • Find practical tips on how you can use learning graphs to design your curriculum
  • Read a summary of the research behind them

What is a learning graph?

A learning graph is a visual tool for curriculum planning that moves beyond simple lists. At its core, a learning graph is a network of ‘nodes’ (specific concepts and skills) and ‘links’ (the connections between them).

Image from the Learning Graphs Quick Read that showcases an example of a learning graph.

Learning graphs build on research into ‘learning progressions’ and ‘knowledge maps’. They are a practical tool that educators can use to design and validate different curricula. For example, they can help teachers to:

  • Visualise and map progression
  • Identify curriculum gaps, so educators can shape and restructure learning experiences as necessary
  • Ensure the use of consistent terminology
  • Sequence learning and manage cognitive load

How to create a learning graph

Building a learning graph is an iterative process that helps you think critically about how different parts of your curriculum relate to each other.

Nodes and links

The first step in creating a learning graph is often to identify your start and end nodes. First, you consider the key concepts and skills that your learners must acquire by the end of a series of lessons. This gives you some end nodes to work towards. Then, you think about learners’ existing knowledge, to help determine your start point. You then work backwards and forwards between these points to identify the different nodes that learners need to cover to get from the beginning to the end.

Educators sharing ideas around a table.

Once you have determined your nodes, you add them to your graph and connect them via ‘links’ until your graph is complete. Where knowledge of particular concepts or skills is essential for learning others, you connect the nodes with solid lines. For prior learning that is helpful but not essential, you use dotted lines.

When developing a learning graph, there isn’t a specific level of granularity that you have to work towards. Progression can be as detailed or as high-level as you need. This makes them a helpful tool in creating bespoke learning experiences and curricula for learners.

Collaboration and development

It is most effective to design learning graphs collaboratively within a small group. This allows curriculum designers to discuss their ideas and challenge each other’s thinking, which helps hone the designs.

Educators collaborating using post-it notes, planning currciulum.

When creating learning graphs, it can be extremely useful to use a tool that is dynamic and allows you to move elements and make changes quickly and easily. At the Raspberry Pi Foundation, our team has experimented with a range of tools, including using editable shapes in Google Slides, collaborating in Figma, and arranging sticky notes on paper. We recommend finding a tool that works for you and the educators you are working with. Although it can work, we suggest avoiding using a pen and paper if possible, as designs can quickly become messy and difficult to navigate after lots of iterations.

The process of designing learning graphs has strong links to ABC learning design and the creation of concept maps, which can also be used for curriculum planning.

Learning graphs in your teaching

Once created, learning graphs can support you to design and adapt your curricula and assess your students’ learning.

For example, to help sequence learning, you can track or predict the paths through a topic most commonly taken by learners and use this to inform your curriculum design.

If you are adapting a unit of work for a specific qualification or new context, you can prune nodes that are not relevant and add any further knowledge and skills your learners need, then use the new learning graph to guide you as you develop the unit.

Finally, you can assess which node a learner has completed, and use this to identify the next logical step in their learning, ensuring the difficulty level is always appropriate.

Using learning graphs to support analysis

Another benefit of learning graphs is that they can be combined with lots of other frameworks, for example, Bloom’s taxonomy. This allows you to better assess and validate the learning journeys you have designed, and ensure that they are suitably accessible, challenging, and relevant for your learners.

Photo of an educator presenting at the front of a classroom of other teachers.

There are a number of ways that you could link your learning graphs to other frameworks, such as annotating nodes with extra information, or using colour coding.

As well as working with learning graphs for specific learning experiences, you can connect multiple learning graphs together and analyse how they intersect. This can help identify inconsistencies between connected sequences of lessons. It can also help uncover broader themes of progression and highlight alternative learning pathways you might not have considered.

Find out more about learning graphs

If you’d like to find out more about learning graphs, you can download our Pedagogy Quick Read for free.

Download the learning graphs Pedagogy Quick Read

To find out more about how we use learning graphs when planning curriculum resources at the Raspberry Pi Foundation, take a look at our teaching and learning design principles.

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Fostering Kenya’s computing education ecosystem

Post Syndicated from Sandra Keeru original https://www.raspberrypi.org/blog/fostering-kenyas-computing-education-ecosystem/

In November, our first-ever Kenya Partner Showcase brought together all our partners from across the country for two days of collaboration, learning, and shared strategy. What stood out to us most from the event was not just the diversity of work that the Kenyan partners are doing in computing education, but also the clear alignment that is emerging across partners, government, and communities.

A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone.

Partners used the Showcase to present sessions about their journeys implementing the Foundation’s programmes, sharing achievements and insights that strengthened the collective learning space. The conversations during our two days together reflected a maturing ecosystem and demonstrated how structured government buy-in is accelerating adoption of our localised Computing Curriculum and influencing national policy spaces.

“Through these programmes, we are equipping young people to become future-ready leaders of integrity and impact.” – Betty Oloo Anderson, National Executive Officer, Kenya Girl Guides Association

It was encouraging to see how far computing education has evolved since we began working with Kenyan partners in 2023, signalling the collective effort and commitment driving this work forward.

Partner-led sessions to reflect on what works

The sessions revealed a powerful shift already taking place in classrooms, with partners demonstrating how embedding The Computing Curriculum into Teacher Professional Development frameworks is building teacher confidence and elevating the quality of classroom instruction through stronger digital competencies and culturally relevant pedagogy. The partner presentation on Experience AI, our AI literacy programme, was especially powerful. It reframed the national dialogue from questioning whether AI might take over the classroom to recognising the real capacities and limitations of AI tools, and the central role teachers play in guiding young people to use and create AI tools in safe, ethical ways.

“Running Raspberry Pi Foundation programmes has been a transformative journey. It has challenged us to innovate, document rigorously, and continually place learners at the center of every decision. The structured support, tools, and community of practice have enabled us to deliver programmes with greater impact and accountability.” Joel Kahindi, Programme Coordinator, STEAMLabs Africa

Hands-on demonstrations, from physical computing with Raspberry Pi Pico to teacher development transitions from Scratch to Python, highlighted how practical computing is taking root in both formal and non-formal learning environments.

Attendees have a conversation at the Raspberry Pi Foundation's Kenya Partner Showcase 2025.

Partners from remote and underserved regions shared how locally contextualised computing tools and programmes we offer are reaching learners in ASAL (arid and semi-arid land) regions, strengthening literacy and digital confidence even in low-resource settings. Complementing this, curriculum-focused partners highlighted how structured computing resources are being adapted for ASAL contexts, reinforcing foundational skills at scale. Efforts around community-centred connectivity are enabling schools, especially in underserved communities, to finally access reliable high-speed internet.

“Through [our partnership with the Raspberry Pi Foundation], we are not only strengthening our programmes but also expanding our vision for what meaningful learning can look like across underserved communities.” – Joel Kahindi, Programme Coordinator, STEAMLabs Africa

A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone.

A notable pattern that emerged was how existing collaborations are now opening doors to new ones, with partners building on their relationship with us to form additional partnerships for devices, connectivity, and shared learning, creating a more holistic digital ecosystem for learners.

Solving shared problems, sharing real stories

A major anchor of the Showcase was the Code Club co-creation sprint. Implementing partners worked through real barriers and opportunities: easier onboarding for educators, continuous training, integrating clubs into school calendars, learner-led ownership, and sustainable models that last beyond donor funding. There was strong interest in forming a unified Code Club Kenya partner network to streamline communication, resource-sharing, and visibility, and our team is now exploring what it would take to bring this to life.

A speaker at the Raspberry Pi Foundation's Kenya Partner Showcase 2025, smiling and holding a microphone.

One message echoed across sessions: the power of telling real stories. Partners expressed a shared desire to spotlight creator projects, Code Club leader journeys, and regional experiences more consistently, through social media, case studies and impact stories, and shared platforms, to make progress visible and inspire more schools to adopt computing and digital skills.

From isolation to coordination

By the close of the Showcase, the takeaway was clear: Kenya’s digital learning ecosystem is no longer a set of isolated efforts — it is a coordinated network that shares priorities, evidence-based insights, and a commitment to scaling impact together. As one partner reflected, “This Showcase created a space for joint learning and will help us scale our impact across the country.” It felt evident, too, that bringing these voices into one room is itself part of the work. For us at the Foundation, this Showcase reaffirmed the responsibility and privilege of stewarding a network that is shaping what the future of learning computing in Kenya can look like.

Two attendees of the Raspberry Pi Foundation's Kenya Partner Showcase 2025 smile at the camera.

The Showcase marks the beginning of a new, aligned chapter for computing education in Kenya, one driven by collaboration, clarity, and a shared belief that we go further when we go together. With shared priorities for 2026, the momentum continues, and we will be continuing to highlight where this collective work is headed next.

Thank you to all partners

We want to thank you to all our partners — Frontier Counties Development Council (FCDC), STEAMLabs Africa, Young Scientists Kenya, Kenya Girl Guides Association, Oasis Mathare, Futures Infinite, EmpServe Kenya, Kenya Connect, Tech Kidz Africa, Riara University, and M-Lugha — for your leadership, dedication, and unwavering belief in what is possible when we learn and build together.

Attendees mingle among tables and chairs at the Raspberry Pi Foundation's Kenya Partner Showcase 2025.

This Showcase was a reflection of your work, your commitment to learners, and your vision for a digitally empowered Kenya. We are grateful for your continued partnership and excited for all that lies ahead.

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 Introducing our new ‘Programming with AI’ unit

Post Syndicated from Rajib Sarkar original https://www.raspberrypi.org/blog/introducing-our-new-programming-with-ai-unit/

In the age of AI, we believe kids still need to learn to code. We’re also testing how AI tools can be used to help with teaching and learning programming. This blog dives into our new classroom unit ‘Programming with AI’, which we’ve developed for teachers to introduce students to learning to code using tools built on large language models (LLMs). We share an overview of the unit, and the early excitement of educators and learners with whom we’re exploring this new approach to teaching and learning programming.

Students completing activities from lesson 1
Students completing activities from lesson 1

Bridging the gap in programming education

As India is accelerating its efforts to modernise digital and computing education, it’s the ideal place for us to test how best to introduce learners to AI-assisted approaches to programming and teach them responsible AI use.

We decided to design a ‘Programming with AI’ unit of eight lessons that support students aged 14–16 to progress from block-based programming to text-based Python programming, Python being one of the most widely employed programming languages in industry and academia. In the new unit, students are guided to use LLM-based tools safely, ethically, and responsibly in order to undertake data analysis projects in Python. Students learn how to use the tools, such as Gemini and ChatGPT, to deepen their understanding, experiment with ideas, and connect classroom concepts with real-world technological applications.

A group of male students at the Coding Academy in Telangana.

The ‘Programming with AI’ unit is currently being piloted across five residential schools under our partner, the Telangana Social Welfare Residential Educational Institutions Society. In the coming months, we will evaluate the impact of this AI-supported approach to learning programming on student engagement, motivation, and overall learning outcomes.

Key design decisions during unit development

During the design phase of the unit, we identified three main challenges we wanted to address:

  1. The first was the transition from block-based to text-based coding. The students we were designing for are familiar with block-based programming like Scratch, but Python needs precisely typed code (syntax). This is a huge shift, and we wanted to offer a way to bridge the gap.
  2. We also had to figure out how to teach prompting LLM tools to students who are learning English as their second language.
  3. Finally, LLMs are non-deterministic. This means that LLM tools don’t always produce the same output, even when given the same prompt. They also do not generate correct code consistently.

To solve the first problem, we decided that the initial lessons in the unit should start by showing familiar Scratch blocks right next to the equivalent Python syntax. This direct comparison makes the transition from block-based to text-based programmer smoother.

Comparison of Python syntax with equivalent Scratch block
Comparison of Python syntax with equivalent Scratch block

To teach prompting, we built a gradual path. The unit starts with a debugging code activity, where students get code with errors and a prompt to make an LLM tool fix the errors. Later, they learn to break down a prompt into their goal (what output they want) and the context (additional information for the LLM tool). Eventually, they use a structured framework to prompt the tool to generate complete, bug-free code.

Handling the unpredictable outputs of LLM tools we turned into a learning activity. In one lesson, students are given different chatbot-generated code segments and asked to analyse and reflect on the code quality. This crucial exercise helps them build the critical thinking and code evaluation skills they need for programming with AI tools.

Illustration showing the progression of Python and generative AI concepts covered in the unit
The progression of Python and generative AI concepts covered in the unit

Over the eight lessons, there is a progression of Python concepts and generative AI concepts, as shown in the image above. They lead students to complete two challenging, real-world data analysis projects.

Preparing the educators

To ensure the successful pilot of the ‘Programming with AI’ unit, a comprehensive teacher training program was essential. We understood that teachers needed confidence and competence not only in the subject matter — programming — but also in guiding students to ethically and effectively use LLM tools.

STEM and ICT teachers participated in the training
STEM and ICT teachers participated in the training

The training was an intensive, three-day programme: two days were held in-person for deep collaboration and one day was virtual for reinforcing key information. We covered five critical areas:

  • Hands-on activities to ensure comfort with the Python programming language
  • Exploring the capabilities and limitations of LLM tools
  • Effective prompt writing, which teachers would in turn model for their students to use for code debugging and idea generation
  • Reviewing lesson flow and learning objectives to understand the structure of a lesson
  • Adapting specific pedagogies to integrate an LLM tool in teaching the Python programming language

This training ensured our educators were not just teaching a new unit, but felt like confident facilitators ready to lead the shift towards AI-supported learning.

Initial student reactions: Excitement, familiarity with LLM tools, and seamless transition

The pilot of the ‘Programming with AI’ unit began during the second week of December 2025 across five selected schools. It was immediately met with enthusiastic student engagement — they were highly excited to learn Python programming using LLM tools.

When teachers initiated discussions about AI, students demonstrated their familiarity with LLMs, chatbots, and related concepts. They were not only aware of generative AI but could readily name and describe the functions of LLM tools, telling us they were actively using these tools to create projects in various other subjects.

Students could easily connect with the activities in the lessons as they had foundational understanding of programming, which they developed while learning block-based coding in Scratch. This prior knowledge enabled their seamless transition to the new learning. Perhaps most impressive was the students’ rapid adoption of LLM tools to debug their code. This skill can ease what is traditionally a steep learning curve when students first learn text-based programming.

We are now eagerly anticipating their curiosity and progress in the upcoming lessons.

Looking ahead and collecting impact data

With the initial enthusiasm confirmed, the pilot program moves into its next critical phase. Teachers will continue to deliver the unit over the next few months in the five selected schools in Telangana.

STEM and ICT teachers participated in the training
The teachers and team at our ‘Programming with AI’ training.

During this extended observation period, our primary focus will be on rigorous data collection and impact assessment. We will closely monitor how students interact with the provided learning materials and how they use LLM tools throughout the unit. We will systematically assess whether the new teaching approach successfully helps students achieve the defined learning objectives for Python programming. Crucially, we will capture comprehensive data to understand the impact of this AI-integrated approach on engagement, skill acquisition, and problem-solving compared to traditional methods.

Based on the synthesis and analysis of all the gathered data, we will share our gathered insights early next year. The findings will inform our plans for more widely introducing the AI-integrated unit and for developing similar content for other settings and age groups.

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Designing for every learner in every classroom

Post Syndicated from Rachel Arthur original https://www.raspberrypi.org/blog/designing-for-every-learner-in-every-classroom/

One of the things I love most about my role as Chief Learning Officer at the Raspberry Pi Foundation is hearing from teachers around the world. A teacher in Kenya told me how their students debugged their first programming projects on a shared laptop. In Scotland, another explained how our resources gave them the confidence to teach computing for the very first time. These stories remind me daily why our work matters: every young person, no matter where they live, should have the chance to explore the power of computing.

Young people use laptops to do their coding tasks.

But creating resources that work in such different contexts is not easy. How do we design materials that work in a wide range of learning environments, from a bustling city classroom to a rural school where internet access can vary? How do we make sure that every learner sees themselves reflected in the examples we choose?

That’s where our teaching and learning design principles come in.

What makes our approach different

Over the past decade, we’ve learned a huge amount about what teachers and learners need from us. We’ve made mistakes, we’ve listened, and we’ve refined our practice again and again. The result is a set of design principles that guide the creation of everything we make, from full curricula to one-off projects.

Four students at laptops in a school in India.

These principles are practical and based on real classroom experience. They’re our way of making sure our resources are reliable, inspiring, and flexible, wherever and however teachers use them.

Here’s what that looks like in action:

  • High quality – You can trust our resources to be accurate and classroom-ready. We put every piece of content through rigorous checks because we understand how busy you are.
  • Research-informed – Our choices are grounded in evidence, not guesswork. We blend academic studies with insight from teachers like you and our own evaluations to create approaches that genuinely work.
  • Consistent – We design our materials to fit together, so learners can build skills step by step, without confusion or contradiction along the way.
  • Inclusive by design – We think carefully about accessibility, representation, and language right from the start. When young people see themselves reflected in computing, they see it as a future they can be a part of.
  • Adaptable – No two classrooms are the same. By making our resources editable and flexible, we give you the freedom to shape them for your learners.
Read the full set of design principles (PDF)

Why share these design principles now?

For us, being transparent about our approach is about trust. Teachers make daily decisions about which resources to use, often with limited preparation time. By showing you the principles behind our work, we want to give you the confidence that our content is not only free and adaptable, but also designed with care, expertise, and your learners at the heart.

Educators participate in a teacher training in Kenya.

Looking ahead

The world of computing education is moving fast, from new programming software, to artificial intelligence tools. Our design principles give us a strong foundation to keep innovating while staying true to our mission of enabling young people to realise their full potential through the power of computing and digital technologies.

A boy and teacher in a computing class

And we’d love to hear from you! How do these principles resonate with your teaching? What helps you most in the classroom? Your feedback is what keeps making our work better.

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Celebrating the UK’s National Engineering Day 2025

Post Syndicated from Lou Loxley original https://www.raspberrypi.org/blog/celebrating-the-uks-national-engineering-day-2025/

We’re happy to celebrate National Engineering Day in the UK with our friends at the Royal Academy of Engineering. Today they launch the AI-Z of Engineering, an online collection showcasing more than 100 current engineering jobs, and over 100 more engineering jobs envisaged for the future. Their aim is to inspire more young people to pursue engineering careers in areas ranging from artificial intelligence (AI) and robotics to medicine, software engineering, space, and sustainability.

National engineering day landscape asset

Engineers shape the technologies and industries that define the future — and the systems they build affect jobs and tasks in every sector. As Sir John Lazar, President of the Royal Academy of Engineering and our Chair of Trustees, says about AI technology:

“I’m not a believer that all jobs will just disappear because of AI. If you think of a job as an assemblage of tasks, there’s no question that the tasks in your job will change because AI will work with you on a bunch of things, and this will in turn reshape your job or role.  The people who will thrive through this transformation will be those who engage with curiosity, intellectual rigour, scepticism, creativity, problem-solving and teamwork – and these are the skills and attitudes that are taught by engineering and computational thinking.”

Sir John Lazar, President of the Royal Academy of Engineering and Chair of Trustees, Raspberry Pi Foundation.

Students use their laptops in a classroom, supervised by a teacher.

Why kids still need to learn to code in the age of AI

At the Raspberry Pi Foundation, we believe AI literacy is crucial for all young people. We also believe all young people need to learn to code to be able to shape our future, where AI systems are integrated into all aspects of life. Our position paper “Why kids still need to learn to code in the age of AI” presents five reasons why:

  1. Even though AI tools can be used to generate code, we still need skilled human programmers to critically review that code.
  2. Learning to code remains the most effective way to become a skilled human programmer, and allows better understanding of how computers work and what their potential and constraints are.
  3. Learning to code will open up more economic opportunities, as advances in technology let us solve a wider range of problems using computers.
  4. Coding is a literacy that gives young people agency and a new way to express themselves, to learn, and to make sense of the world. 
  5. Young people who learn to code now will shape the future, and we need that power to sit with young people from all backgrounds so they can design systems that serve everyone.

Our free resources for young people help them learn to code and get creative with technology to bring their ideas into the world, building their confidence. So whether your kids are just starting their coding journey, or are looking for a new challenge, you can use our resources to support them.

Young person learning in the classroom

Understanding coding and computers is critical in many engineering roles, so inspiring kids about engineering can also motivate them to try their hand at coding.

How you can get involved with National Engineering Day

There are plenty of ways you can celebrate National Engineering Day. Inspire young people to embark on careers in engineering by sharing the AI-Z of Engineering collection with your school or college, on social media, or on your organisation’s website. The collection is a living resource that will be updated, and contributions are always welcome — both for current jobs and future ones.To find out more ways to get involved with National Engineering Day, you can go download the toolkit.

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Adapting our computing curriculum resources for Telangana — the journey so far

Post Syndicated from Jaskaran Singh original https://www.raspberrypi.org/blog/adapting-our-computing-curriculum-resources-for-telangana-the-journey-so-far/

This blog is the third and final in our mini-series about the things we’ve learnt from adapting The Computing Curriculum resources, and from training teachers to use them in schools. In the first two blogs, we wrote about our experiences in Kenya and Odisha, India. Here, we focus on our work in Telangana, India. 

Three female students at the Coding Academy in Telangana.

This blog was written by Jaskaran Singh, Impact Manager, and Mamta Manaktala, Senior Learning Manager.

Adapting for unique needs

Every country and region has unique opportunities, challenges, and needs. In a vast country like India, every state is different — what works in Odisha may not work in other locations. Thus, to meet the needs of students in the state of Telangana, we’ve been working on adapting The Computing Curriculum specifically for them.

A group of female students at the Coding Academy in Telangana.

Our work in Telangana began in 2023, when we kickstarted a five-year partnership with the Telangana Social Welfare Residential Educational Institutions Society (TGSWREIS), a society under the Government of Telangana. Through the partnership, we’ve developed an adapted curriculum, along with training for educators working in educational institutions with limited resources. The adapted curriculum includes localised examples and activities, and teaching approaches to make the learning experience feel relevant and meaningful for students in Telangana, while keeping the core learning outcomes aligned with global standards. 

Testing and iterating

Since the start of the partnership, we’ve been testing the curriculum at the Coding Academy School, a co-educational school at Moinabad, and the Coding Academy College, a degree college for women in Shamirpet.

Our work delivering the curriculum in Telangana was our first time using a direct-to-learners model. The Coding Academy School and College gave us unique opportunities to work with students directly and observe first-hand the difference the programme made in their learning journeys. 

A group of students and a teacher at the Coding Academy in Telangana.

During the first year of implementation, we gathered useful feedback from students and teachers. Check out one of our earlier blogs where we share some of the findings. We used these inputs to further develop the curriculum.

This updated version of the curriculum was implemented in the 2024/25 academic year. At the school, our educators worked with 210 students in grades 7–9, while at the college, our educators worked with 382 undergraduate students. As in the first year, we used data from assessments, lesson observations, educator interviews, student surveys, and student focus groups to understand what’s working well and what could be improved. So what did we learn?

What we learnt over the past year

Our evaluation findings show that the updated curriculum worked well and positive outcomes are being achieved for most students. Educators felt prepared to teach the curriculum in this second year and found the ongoing support and spaces for discussion really useful. Moreover, we found that there are potential positive ripple effects beyond the school as well. 

Learning outcomes are being achieved to a high degree

In surveys, 91% of students in the school and 96% of students in the college responded that the lessons helped them get better at computing and coding. Students feel they are not just learning new skills but also finding the content enjoyable: 88% of students in the school and 98% of students in the college responded that they are enjoying their classes. Educators and observers also reported that students were engaged during lessons, and often completed activities without needing any support. 

Students' reflections on the computing curriculum.

Students’ assessment scores further confirmed positive learning outcomes. 4 out of every 5 scores in the school and 9 out of every 10 scores in the college were 60% or above, which was higher than in the first year of the adapted curriculum’s implementation.

The updated curriculum is more aligned to student needs

The changes we made to the curriculum included:

  • Adding more localised examples
  • Simplifying the language 
  • Restructuring the flow of the content

Educators were highly positive about the updates to the curriculum. 

“The students are able to [better] understand the examples because we updated [to] the India context examples.” — Educator, Coding Academy School 

“Students are receiving it very well because we have modified the content this year, and [that includes] the placements of the unit and the connectivity of the lessons and units.” — Educator, Coding Academy School

Additionally, for the college curriculum, we aligned the content more closely with the learning objectives set by Osmania University — with which the college is affiliated. We also included more advanced topics for students specialising in data science. During interviews, educators reported that the content was now much better aligned to student expectations. 

“[The curriculum] we have designed is based as per [the] Osmania University curriculum. [The lessons] are definitely meeting the students’ needs because whatever discussions we are taking in classes, they are [successfully] participating in those discussions and they are doing whatever activities we give them.” — Educator, Coding Academy College

Outside of knowledge and skills in computing, the curriculum is also helping students develop wider life skills. In our survey, college students shared that working on projects gives them a sense of accomplishment and the confidence to solve real-world problems. Many students also reported that through the curriculum they are developing higher-order thinking skills, which will support their future careers. 

“The thrill lies the creativity and problem-solving aspects. I get to turn ideas into reality pieces, and there is something incredible satisfying about debugging code and watching it run flawlessly. It’s like slow, challenging puzzles, frustrating at times but rewarding when everything clicks.” — Student, Coding Academy College

“My favourite thing [about] the computing and coding classes [is the] Scratch programme. I have learnt it [for the] first time. By learning I have enjoyed a lot. During the coding process, it trains our brain to think deeply, identify trouble, and break things up and put pieces together [as] a solution.” — Student, Coding Academy College

Students are inspired to continue engaging 

Students are showing high interest in applying their skills outside of their classes. Almost all students — 100% in the school and 99% in the college — reported that they would like to participate in coding-related competitions. 

A group of female students working on a coding project.

Educators also told us that many students are exploring future job opportunities in the computing and digital technology fields, and are curious about topics outside the curriculum. Interestingly, 93% of the college students who were studying courses not traditionally associated with jobs in computing and digital technology reported that they would like to pursue a job in computing.

The positive benefits go beyond the school

We have also learnt that a high-quality computing education for young people has potentially wider benefits for the community. One educator described how students are helping their families, many of whom have limited experiences, engage more confidently with digital technologies.

“Families don’t know how to use smartphones and laptop computers, but our students know very well so I can say they do teach to their elders how to use these platforms.” — Educator, Coding Academy School

Ongoing support for educators was important

To help educators feel confident and prepared, individualised learning resources were provided throughout the year. These were well received by educators. Educators also found the weekly meetings with our India-based team members useful to discuss ongoing challenges regarding delivery and assessments. 

What could still be improved

There were improvements this year in the availability of equipment, and the use of Wi-Fi dongles addressed internet connectivity issues to some degree. However, educators still faced some challenges. For example, educators in the school faced issues accessing printed worksheets and educators in the college faced issues accessing projectors during their lessons. We are working closely with our delivery partner to address these issues for the new academic year.

A group of male students working on a coding project.

With regard to the content, educators felt the curriculum could benefit from some further amendments. For the school curriculum, these include easing the transition from block-based to text-based coding. For the college curriculum, there were suggestions for more focus on real-world applications of coding and including advanced topics, like machine learning, for undergraduates specialising in computing-related subjects. We have considered all these suggestions and made necessary revisions to the curriculum.

Next steps in Telangana: Scaling up impact

With the success of the pilot, we’re excited to announce that the adapted curriculum will now be implemented at over 350 schools and junior colleges in the state of Telangana. A majority of schools will be with the same partner, TGSWREIS, while some schools and junior colleges will be with other partners. The Coding Academy School will become our hub for trialling new curriculum content and strategies, and conducting research studies and teacher training and support. Additionally, the school will also host inter-school events.

A group of female students working on a coding project.

The progress we’ve seen so far in Telangana is very encouraging. We look forward to continuing these partnerships and helping more young people realise their potential through the power of computing and digital technologies.

What we learnt about adapting curriculum resources for different regions

From our work in Telangana, Odisha, and Kenya, we’ve learnt that a curriculum isn’t a one-size-fits-all product. The local context, culture, and educational provisions are important considerations when adapting learning resources for different regions. We’ve also learnt that building long-term partnerships with organisations who have local expertise is key to understanding these considerations and effectively reaching communities where we can make the biggest difference. Finally, we’ve learnt that adaptation isn’t a one-time activity. It’s a cycle of continuous refinement; listening closely to feedback from the ground is important to ensure that our support for educators and learning experiences for young people have the best possible impact.

Want to learn more about our curriculum resources?

You can access our free Computing Curriculum resources on our website — we are currently working to make the materials for India and Kenya downloadable there.

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How social learning can lead to better outcomes in your computing classroom

Post Syndicated from Sean Sayers original https://www.raspberrypi.org/blog/how-social-learning-can-lead-to-better-outcomes-in-your-computing-classroom/

Throughout our lives, we’re constantly learning from others. Whether we’re interacting with teachers or trainers, or observing friends or strangers, we’re learning either deliberately or inadvertently. This process is known as ‘social learning’. 

In today’s blog, you’ll dive into what social learning is and how you can use it to create more engaging and effective learning experiences in your computing classroom.

Image of our latest Pedagogy Quick Read

You’ll also find our latest Pedagogy Quick Read, which explores social learning. It’s free to download and includes: 

  • Practical tips for how to use social learning and related approaches with your learners
  • A summary of the research behind social learning

What is social learning?

Social learning is simply any learning that involves other people. It can take any form, from watching a video, to taking part in a classroom discussion. It can take place in person or online, and it can happen without people realising they’re learning something.

Social learning is based on modelling and involves people observing and imitating the behaviours that others model. Albert Bandura, the acknowledged originator of social learning theory, suggested that social learning is guided by four related processes:

  • Attention: Recognising and focusing on someone’s behaviour and its vital elements
  • Retention: Creating a mental image and description to help you recall what you observed; practising responses (mentally or actively)
  • Reproduction: Translating the mental image back into actions
  • Motivation: Having a good reason to repeat (or avoid) the behaviours, depending on the rewards or punishments involved

How can I enable social learning?

There’s lots of ways you can involve social learning in your computing classroom, including through other teaching approaches and frameworks. 

4 children social learning in the classroom

To help your learners get the most out of social learning, it’s best to:

  • Create a safe environment for learners to share learnings, ask questions, and actively engage in the learning process
  • Include a mix of resources and activities to ensure inclusion and accessibility
  • Set clear expectations and instructions, and ensure that social learning is key to achieve learning objectives

Applying social learning: Some teaching approaches

Among our pedagogy resources, you’ll find lots of practical advice for teaching approaches that promote social learning. The approaches we recommend for the pedagogy principles ‘Work together’ and ‘Model everything’ are especially suitable.

Work together:

Model everything:

Using a PRIMM (PDF) approach for structuring programming lessons, and encouraging students to talk about code as part of these, also works well for social learning.

Applying social learning: Practical examples

Let’s look at pair programming as an example. In this activity, pairs of learners work together to create a computer program, taking on distinct roles that they swap regularly. One learner acts as the ‘driver’, writing the code, while the other is the ‘navigator’, guiding the process, reviewing the code, and identifying potential issues. 

As they work, each learner is able to observe the other person’s approach, learning with and from their partner throughout the activity. This constant interaction and shared problem solving can help them to understand programming concepts better and to build stronger teamwork skills.

Children in the classroom social learning

Another example is setting your class the task to create shared digital resources on several topics everyone needs to learn about. In this activity, you split learners into small groups or pairs, and assign them a topic to later explain to the whole group. Grouped learners work together to create a resource explaining their topic. As the facilitator, you can either provide the information they need, or let them conduct their own research. At the end of the activity, each group presents their resource to the wider class.

An activity like this helps learners develop their knowledge through working together and talking to each other, and also provides the class with resources they can keep using.

The benefits of social learning

Potential benefits for teachers:

  • Improved student engagement and learning
  • Enhanced professional development experiences, leading to more confident teaching

Potential benefits for students:

  • Improved social skills
  • Opportunities to build higher-level thinking skills
  • Deeper understanding and a greater ability to remember knowledge in the long term
Download the Quick Read on social learning

A social approach to shaping the future

In a world filled with complex challenges, there’s more need than ever for people to work together. By using social learning approaches in your classroom, you help your students to engage more deeply with your teaching and to develop the skills to succeed in collaboration with others. In this way, you’ll prepare them for navigating technological change as well as for shaping a common future where everyone can thrive.

The post How social learning can lead to better outcomes in your computing classroom appeared first on Raspberry Pi Foundation.

Adapting our computing curriculum resources for Odisha — the journey so far

Post Syndicated from Fiona Coventry original https://www.raspberrypi.org/blog/adapting-our-computing-curriculum-resources-for-odisha-the-journey-so-far/

Today’s blog is the second in a mini-series of three sharing our experiences of adapting computing curriculum resources for different contexts, and off training teachers to use them in schools. Last month we wrote about our collaboration with partners in Kenya. Here we discuss our work in Odisha, India.

Teachers at a teacher training in Odisha.

This article has been written by Fiona Coventry, Impact Manager, and Mamta Manaktala, Senior Learning Manager.

A long-term partnership in Odisha

We know that building long-term partnerships with organisations that have local expertise is key to making a real impact for young people. This fact was echoed by people involved in education initiatives worldwide who spoke at the What Works Hub for Global Education 2024 annual conference, which Fiona followed online. Our work in Odisha is an example of this.

Teachers at a teacher training in Odisha.

We have now been working with our government partner in Odisha, Panchasakha Shikhya Setu (formerly Mo School Abhiyan), for four years. Our journey began in 2021, when we worked together to establish a network of Code Clubs in government and government-aided schools in the state. In 2023, our focus shifted to developing a formal computing curriculum for students in grades 9 and 10 (known locally as the Kaushali curriculum), in collaboration with two other partners. 

Work in the 2024/2025 academic year

Adaptation is a crucial aspect of how we ensure our computing resources are accessible to as many young people as possible. For our work in Odisha, we adapted content from The Computing Curriculum and then localised it to fit the requirement of the students.

Teachers at a teacher training in Odisha.

In Odisha’s June 2024 to April 2025 academic year, we rolled out adapted computing curriculum content for grade 10 students, for students who had already learned with adapted grade 9 content in 2023/24. We worked with our partners to develop the curriculum content and trained 310 master teachers from across Odisha, along with 30 State Resource Groups (SRGs) to support them. Before the end of 2024, the 310 master teachers subsequently trained 8109 teachers, who would reach an estimated 880,000 students with the grade 9 and 10 curriculum content. We had an ongoing responsibility to support 1846 of these teachers in our allocated districts, with an estimated reach to around 205,000 students.

Impact of the grade 9 and 10 curriculum

In early 2025 we issued a follow-up survey about student learning, content, and training to a sample of teachers in our allocated districts, and 310 teachers responded. (We used a stratified sampling approach designed to ensure the survey results were representative of all teachers.)

At least 87% of teachers agreed that students achieved the outcomes we asked about, e.g. regarding coding skills, staying safe online, and use of data in machine intelligence. 

Moreover, responses related to our grade 9 curriculum remained similarly high compared to 2024 survey responses.

2025 Odisha teacher survey responses regarding their students' learning.
2025 Odisha teacher survey responses regarding their students’ learning. Click to enlarge.

Teachers also expressed their appreciation for the computing curriculum resources and training in free-text comments and interviews, for example:

“IT and coding is essential nowadays. So a good initiative, adding this to schools’ curriculum.” – Teacher in Odisha

“The training was quite informative, interesting and helpful.” – Teacher in Odisha

“It is very useful training for me. It boosts my knowledge and helps me for classroom transaction.” – Teacher in Odisha

Addressing challenges

An ongoing challenge in Odisha has been supporting those teachers who lack experience with computing and/or with our recommended teaching approaches for computing. We have been working hard to help these teachers develop the knowledge, skills, and confidence to effectively deliver the curriculum content in the limited time they have alongside their other professional commitments.

Teachers at a teacher training in Odisha.

In the 2023/2024 academic year, many teachers had told us they needed further training and support. For this reason, we offered longer training in the 2024/25 academic year. We also adapted our training approach based on learning from earlier phases, such as including activities teachers could complete on their smartphones, enabling more hands-on learning while reducing dependence on available IT equipment. The outcome of this was positive: in the follow-up survey, fewer teachers felt they needed additional training to deliver the lessons, and most teachers we interviewed felt this year’s training was an improvement on the previous year’s.

Our team also ran weekly webinars to support teachers and address their queries. These were very well received by teachers. Of the responses received to feedback form available after each webinar:

  • 97% agreed that the “webinar helped me to understand the topics covered more clearly.”
  • 98% agreed that the “webinar was useful to support my teaching.”

This was supported by comments from teachers, for example:

“All questions were answered. The webinar was good. Gained a lot. Thank you very much.”  – Teacher in Odisha

“I learned many unknown things about Scratch, it will help my classroom teaching.” – Teacher in Odisha

In this year’s follow-up survey, teachers also less frequently indicated they felt they needed “additional content to support students”. They provided useful feedback and suggestions regarding the curriculum content, e.g. further simplifying and localising it, which we will incorporate into future resource development.

Another persistent challenge has been limited access to IT equipment and the internet in schools, and what this means for student-device ratios and how teachers are able to deliver the content. For future resources we are developing, we are therefore adapting the amount of content to be delivered over a series of lessons.

Next steps for our partnership in Odisha

In 2025, we are working with the same partners to implement a curriculum for grades 6 to 8, initially in around 460 schools. We and our partners have developed the curriculum content and are currently in the process of training teachers in preparation for classroom delivery.

We are also continuing to support the teachers previously trained on the grade 9 and 10 curriculum through webinars and school visits.

Want to see our curriculum resources?

You can access our free Computing Curriculum resources on our website — we are currently working to make the materials for India, and for Kenya, downloadable there.

Look out for the final blog in this mini-series next month, which will focus on our computer science curriculum in Telangana, India.

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