The AI4K12 project: Big ideas for AI education

What is AI thinking? What concepts should we introduce to young people related to AI, including machine learning (ML), and data science? Should we teach with a glass-box or an opaque-box approach? These are the questions we’ve been grappling with since we started our online research seminar series on AI education at the Raspberry Pi Foundation, co-hosted with The Alan Turing Institute.

Over the past few months, we’d already heard from researchers from the UK, Germany, and Finland. This month we virtually travelled to the USA, to hear from Prof. Dave Touretzky (Carnegie Mellon University) and Prof. Fred G. Martin (University of Massachusetts Lowell), who have pioneered the influential AI4K12 project together with their colleagues Deborah Seehorn and Christina Gardner-McLure.

The AI4K12 project

The AI4K12 project focuses on teaching AI in K-12 in the US. The AI4K12 team have aligned their vision for AI education to the CSTA standards for computer science education. These Standards, published in 2017, describe what should be taught in US schools across the discipline of computer science, but they say very little about AI. This was the stimulus for starting the AI4K12 initiative in 2018. A number of members of the AI4K12 working group are practitioners in the classroom who’ve made a huge contribution in taking this project from ideas into the classroom.

Dave Touretzky presents the five big ideas of the AI4K12 project at our online research seminar.
Dave gave us an overview of the AI4K12 project (click to enlarge)

The project has a number of goals. One is to develop a curated resource directory for K-12 teachers, and another to create a community of K-12 resource developers. On the AI4K12.org website, you can find links to many resources and sign up for their mailing list. I’ve been subscribed to this list for a while now, and fascinating discussions and resources have been shared. 

Five Big Ideas of AI4K12

If you’ve heard of AI4K12 before, it’s probably because of the Five Big Ideas the team has set out to encompass the AI field from the perspective of school-aged children. These ideas are: 

  1. Perception — the idea that computers perceive the world through sensing
  2. Representation and reasoning — the idea that agents maintain representations of the world and use them for reasoning
  3. Learning — the idea that computers can learn from data
  4. Natural interaction — the idea that intelligent agents require many types of knowledge to interact naturally with humans
  5. Societal impact — the idea that artificial intelligence can impact society in both positive and negative ways

Sometimes we hear concerns that resources being developed to teach AI concepts to young people are narrowly focused on machine learning, particularly supervised learning for classification. It’s clear from the AI4K12 Five Big Ideas that the team’s definition of the AI field encompasses much more than one area of ML. Despite being developed for a US audience, I believe the description laid out in these five ideas is immensely useful to all educators, researchers, and policymakers around the world who are interested in AI education.

Fred Martin presents one of the five big ideas of the AI4K12 project at our online research seminar.
Fred explained how ‘representation and reasoning’ is a big idea in the AI field (click to enlarge)

During the seminar, Dave and Fred shared some great practical examples. Fred explained how the big ideas translate into learning outcomes at each of the four age groups (ages 5–8, 9–11, 12–14, 15–18). You can find out more about their examples in their presentation slides or the seminar recording (see below). 

I was struck by how much the AI4K12 team has thought about progression — what you learn when, and in which sequence — which we do really need to understand well before we can start to teach AI in any formal way. For example, looking at how we might teach visual perception to young people, children might start when very young by using a tool such as Teachable Machine to understand that they can teach a computer to recognise what they want it to see, then move on to building an application using Scratch plugins or Calypso, and then to learning the different levels of visual structure and understanding the abstraction pipeline — the hierarchy of increasingly abstract things. Talking about visual perception, Fred used the example of self-driving cars and how they represent images.

A diagram of the levels of visual structure.
Fred used this slide to describe how young people might learn abstracted elements of visual structure

AI education with an age-appropriate, glass-box approach

Dave and Fred support teaching AI to children using a glass-box approach. By ‘glass-box approach’ we mean that we should give students information about how AI systems work, and show the inner workings, so to speak. The opposite would be a ‘opaque-box approach’, by which we mean showing students an AI system’s inputs and the outputs only to demonstrate what AI is capable of, without trying to teach any technical detail.

AI4K12 advice for educators supporting K-12 students: 1. Use transparent AI demonstrations. 2. Help students build mental models. 3. Encourage students to build AI applications.
AI4K12 teacher guidelines for AI education

Our speakers are keen for learners to understand, at an age-appropriate level, what is going on “inside” an AI system, not just what the system can do. They believe it’s important for young people to build mental models of how AI systems work, and that when the young people get older, they should be able to use their increasing knowledge and skills to develop their own AI applications. This aligns with the views of some of our previous seminar speakers, including Finnish researchers Matti Tedre and Henriikka Vartiainen, who presented at our seminar series in November

What is AI thinking?

Dave addressed the question of what AI thinking looks like in school. His approach was to start with computational thinking (he used the example of the Barefoot project’s description of computational thinking as a starting point) and describe AI thinking as an extension that includes the following skills:

  • Perception 
  • Reasoning
  • Representation
  • Machine learning
  • Language understanding
  • Autonomous robots

Dave described AI thinking as furthering the ideas of abstraction and algorithmic thinking commonly associated with computational thinking, stating that in the case of AI, computation actually is thinking. My own view is that to fully define AI thinking, we need to dig a bit deeper into, for example, what is involved in developing an understanding of perception and representation.

An image demonstrating that AI systems for object recognition may not distinguish between a real banana on a desk and the photo of a banana on a laptop screen.
Image: Max Gruber / Better Images of AI / Ceci n’est pas une banane / CC-BY 4.0

Thinking back to Matti Tedre and Henriikka Vartainen’s description of CT 2.0, which focuses only on the ‘Learning’ aspect of the AI4K12 Five Big Ideas, and on the distinct ways of thinking underlying data-driven programming and traditional programming, we can see some differences between how the two groups of researchers describe the thinking skills young people need in order to understand and develop AI systems. Tedre and Vartainen are working on a more finely granular description of ML thinking, which has the potential to impact the way we teach ML in school.

There is also another description of AI thinking. Back in 2020, Juan David Rodríguez García presented his system LearningML at one of our seminars. Juan David drew on a paper by Brummelen, Shen, and Patton, who extended Brennan and Resnick’s CT framework of concepts, practices, and perspectives, to include concepts such as classification, prediction, and generation, together with practices such as training, validating, and testing.

What I take from this is that there is much still to research and discuss in this area! It’s a real privilege to be able to hear from experts in the field and compare and contrast different standpoints and views.

Resources for AI education

The AI4K12 project has already made a massive contribution to the field of AI education, and we were delighted to hear that Dave, Fred, and their colleagues have just been awarded the AAAI/EAAI Outstanding Educator Award for 2022 for AI4K12.org. An amazing achievement! Particularly useful about this website is that it links to many resources, and that the Five Big Ideas give a framework for these resources.

Through our seminars series, we are developing our own list of AI education resources shared by seminar speakers or attendees, or developed by us. Please do take a look.

Join our next seminar

Through these seminars, we’re learning a lot about AI education and what it might look like in school, and we’re having great discussions during the Q&A section.

On Tues 1 February at 17:00–18:30 GMT, we’ll hear from Tara Chklovski, who will talk about AI education in the context of the Sustainable Development Goals. To participate, click the button below to sign up, and we will send you information about joining. I really hope you’ll be there for this seminar!

The schedule of our upcoming seminars is online. You can also (re)visit past seminars and recordings on the blog.

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I love the way Raspberry Pi is pushing computer education. It’s nice to know you’re buying a product that helps support the spread of knowledge. The open source aspect is especially important to me.

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AI in the world of education in the future is an unavoidable part. So, even though some of the teachers in countries with the level of technological development are lagging behind, it would be wise to follow the development of IA in the world of education. at least read information like this.

Reply to Administrasi Kurikulum

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