![\"A](\"https:\/\/www.prime-wow.com\/wp-content\/uploads\/2023\/06\/CoolestProjects_03232019-44.jpg\")
<\/figure>\n<\/div>\nSo when we started designing materials for the Experience AI<\/a> learning programme in partnership with leading AI unit Google DeepMind<\/a>, we decided to create short explanations of key AI and machine learning (ML) terms. The explanations are doubly useful:<\/p>\n As an example, here is our explanation of the term \u201cartificial intelligence\u201d for learners aged 11\u201314:<\/p>\n Artificial intelligence (AI) is the design and study of systems that appear to mimic intelligent behaviour. Some AI applications are based on rules. More often now, AI applications are built using machine learning<\/strong> that is said to \u2018learn\u2019 from examples in the form of data<\/strong>. For example, some AI applications are built to answer questions or help diagnose illnesses. Other AI applications could be built for harmful purposes, such as spreading fake news. AI applications do not think. AI applications are built to carry out tasks in a way that appears to be intelligent.<\/p>\n<\/blockquote>\n You can find 32 explanations in the glossary that is part of the Experience AI Lessons<\/a>. Here\u2019s an insight into how we arrived at the explanations.<\/p>\n In order to ensure the explanations are as precise as possible, we first identified reliable sources. These included among many others:<\/p>\n Vocabulary is an important part of teaching and learning. When we use vocabulary correctly, we can support learners to develop their understanding. If we use it inconsistently, this can lead to alternate conceptions (misconceptions) that can interfere with learners\u2019 understanding. You can read more about this in our Pedagogy Quick Read on alternate conceptions<\/a>.<\/p>\n Some of our principles for writing explanations of AI terms were that the explanations need to:\u00a0<\/p>\n We engaged in an iterative process of writing explanations, gathering feedback from our team and our Experience AI project partners at Google DeepMind<\/a>, and adapting the explanations. Then we went through the feedback and adaptation cycle until we all agreed that the explanations met our principles.<\/p>\n An important part of what emerged as a result, aside from the explanations of AI terms themselves, was a blueprint for how not<\/strong> to talk about AI. One aspect of this is avoiding anthropomorphism, detailed by Ben Garside from our team here<\/a>.<\/p>\n As part of designing the the Experience AI Lessons, creating the explanations helped us to:<\/p>\n One of the ways education research informed the explanations was that we used semantic waves<\/a> to structure each term\u2019s explanation in three parts:\u00a0<\/p>\n Most explanations also contain \u2018middle of the wave\u2019 sentences, which add additional abstract content, bridging the \u2018bottom of the wave\u2019 concrete example to the \u2018top of the wave\u2019 abstract content.<\/p>\n Here\u2019s the \u201cartificial intelligence\u201d explanation broken up into the parts of the semantic wave:<\/p>\n Some of the explanations went through 10 or more iterations before we agreed they were suitable for publication. After months of thinking about, writing, correcting, discussing, and justifying the explanations, it\u2019s tempting to wonder whether I should have just prompted an AI chatbot to generate the explanations for me.<\/p>\n I tested this idea by getting a chatbot to generate an explanation of \u201cartificial intelligence\u201d using the prompt \u201cExplain what artificial intelligence is, using vocabulary suitable for KS3 students, avoiding anthropomorphism\u201d. The result included quite a few inconsistencies with our principles, as well as a couple of technical inaccuracies. Perhaps I could have tweaked the prompt for the chatbot in order to get a better result. However, relying on a chatbot\u2019s output would mean missing out on some of the value of doing the work of writing the explanations in collaboration with my team and our partners.<\/p>\n The visible result of that work is the explanations themselves. The invisible result is the knowledge we all gained, and the coherence we reached as a team, both of which enabled us to create high-quality resources for Experience AI. We wouldn\u2019t have gotten to know what resources we wanted to write without writing the explanations ourselves<\/a> and improving them over and over. So yes, it was worth our time.<\/p>\n The process of creating and iterating the AI explanations highlights how opaque the field of AI still is, and how little we yet know about how best to teach and learn about it. At the Raspberry Pi Foundation, we now know just a bit more about that and are excited to share the results with teachers and young people.<\/p>\n You can access the Experience AI Lessons and the glossary with all our explanations at experience-ai.org<\/a>. The glossary of AI explanations is just in its first published version: we will continue to improve it as we find out more about how to best support young people to learn about this field.<\/p>\n Let us know what you think about the explanations and whether they\u2019re useful in your teaching. Onwards with the exciting work of establishing how to successfully engage young people in learning about and creating with AI technologies.<\/p>\n The post How we\u2019re learning to explain AI terms for young people and educators<\/a> appeared first on Raspberry Pi Foundation<\/a>.<\/p>\n
What do we talk about when we talk about artificial intelligence (AI)? It\u2019s becoming a cliche to point out that, because the term \u201cAI\u201d is used to describe so many different things nowadays, it\u2019s difficult to know straight away what … Continue reading \n
\n
Reliable sources<\/h2>\n
\n
Explaining AI terms to Key Stage 3 learners: Some principles<\/h2>\n
\n
![\"A](\"https:\/\/www.prime-wow.com\/wp-content\/uploads\/2023\/06\/MaxGruber-Cecinest-pas-une-banane-2000x1334-1-500x334-1.jpg\")
\n
Using education research to explain AI terms<\/h2>\n
\n
\n
![\"Our](\"https:\/\/www.prime-wow.com\/wp-content\/uploads\/2023\/06\/Screenshot-2023-06-12-at-14.41.36-800x206-1.png\")
Was it worth our time?<\/h2>\n
![\"A](\"https:\/\/www.prime-wow.com\/wp-content\/uploads\/2023\/06\/RensDimmendaal-JohannSiemensDecisionTree-reversed-3000x1000-1-800x267-1.jpg\")
What do you think about the explanations?<\/h2>\n
\n
\nSource: Raspberry Pi – How we\u2019re learning to explain AI terms for young people and educators<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"