In this article, we take you through the updates we’ve made to the Experience AI Lessons based on teachers’ feedback, reveal two new lessons on large language models (LLMs) and biology, and give you the chance to shape the future of the Experience AI programme. 

Updated lessons based on your feedback

In April we launched the first Experience AI Lessons as a unit of six lessons for secondary school students (ages 11 to 14, Key Stage 3) that gives you everything you need to teach AI, including lesson plans, slide decks, worksheets, and videos. Since the launch, we’ve worked closely with teachers and learners to make improvements to the lesson materials.

The first big update you’ll see now is an additional project for students to do across Lesson 5 and Lesson 6. Before, students could choose between two projects to create their own machine learning model, either to classify data from the world’s oceans or to identify fake news. The new project we’ve added gives students the chance to use images to train a machine learning model to identify whether or not an item is biodegradable and therefore suitable to be put in a food waste bin.

Our second big update is a new set of teacher-focused videos that summarise each lesson and highlight possible talking points. We hope these videos will help you feel confident and ready to deliver the Experience AI Lessons to your learners.

A new lesson on large language models

As well as updating the six existing lessons, we’ve just released a new seventh lesson consisting of a set of activities to help students learn about the capabilities, opportunities, and downsides of LLMs, the models that AI chatbots are based on.

With the LLM lesson’s activities you can help your learners to:

• Explore the purpose and functionality of LLMs and examine the critical aspect of trustworthiness of these models’ outputs
• Examine the reasons why the output of LLMs may not always be reliable and understand that LLMs are machines that make predictions
• Compare LLMs to other technologies to assess their suitability for different purposes
• Evaluate the appropriateness of using LLMs in a variety of authentic scenarios

All Experience AI Lessons are designed to be cross-curricular, and for England-based teachers, the LLM lesson is particularly useful for teaching PSHE (Personal, Social, Health and Economic education).

The LLM lesson is designed as a set of five 10-minute activities, so you have the flexibility to teach the material as a single lesson or over a number of sessions. While we recommend that you teach the activities in the order they come, you can easily adapt them for your learners’ interests and needs. Feel free to take longer than our recommended time and have fun with them.

A new lesson on biology: AI for the Serengeti

We have also been working on an exciting new lesson to introduce AI to secondary school students (ages 11 to 14, Key Stage 3) in the biology classroom. This stand-alone lesson focuses on how AI can help conservationists with monitoring an ecosystem in the Serengeti.

We worked alongside members of the Biology Education Research Group (BERG) at the UK’s Royal Society of Biology to make sure the lesson is relevant and accessible for Key Stage 3 teachers and their learners.

Register your interest if you would like to be one of the first teachers to try out this thought-provoking lesson.  

Webinars to support your teaching

If you want to use the Experience AI materials but would like more support, our new webinar series will help you. You will get your questions answered by the people who created the lessons. Our first webinar covered the six-lesson unit and you can watch the recording now:

September’s webinar: How to use Machine Learning for Kids

Join us to learn how to use Machine Learning for Kids (ML4K), a child-friendly tool for training AI models that is used for project work throughout the Experience AI Lessons. The September webinar will be with Dale Lane, who has spent his career developing AI technology and is the creator of ML4K.

• Thursday 21 September at 4PM (BST) on YouTube.
• Submit your questions in advance, or ask questions in the webinar.

Help shape the future of AI education

We need your feedback like a machine learning model needs data. Here are two ways you can share your thoughts:

1. Fill in our form to tell us how you’ve used the Experience AI materials.
2. Become part of our teacher feedback panel. We meet every half term, and our first session will be held mid-October. Email us to register your interest and we’ll be in touch.

To find out more about how you can use Experience AI to teach AI and machine learning to your learners this school year, visit the Experience AI website.

The post Experience AI: Teach about AI, chatbots, and biology appeared first on Raspberry Pi Foundation.

It’s been hard to miss the newest generation of AI chatbots that companies have released over the last year. News articles and stories about them seem to be everywhere at the moment. So you may have heard of machine learning (ML) chatbot applications such as ChatGPT and LaMDA. These applications are advanced enough to have caused renewed discussions about the Turing Test and whether the chatbot applications are sentient.

Chatbots are not sentient

Without any knowledge of how people create such chatbot applications, it’s easy to imagine how someone might develop an incorrect mental model around these applications being living entities. With some awareness of Sci-Fi stories, you might even start to imagine what they could look like or associate a gender with them.

The reality is that these new chatbots are applications based on a large language model (LLM) — a type of machine learning model that has been trained with huge quantities of text, written by people and taken from places such as books and the internet, e.g. social media posts. An LLM predicts the probable order of combinations of words, a bit like the autocomplete function of a smartphone. Based on these probabilities, it can produce text outputs. LLM chatbot applications run on servers with huge amounts of computing power that people have built in data centres around the world.

Our AI education resources for young people

AI applications are often described as “black boxes” or “closed boxes”: they may be relatively easy to use, but it’s not as easy to understand how they work. We believe that it’s fundamentally important to help everyone, especially young people, to understand the potential of AI technologies and to open these closed boxes to understand how they actually work.

As always, we want to demystify digital technology for young people, to empower them to be thoughtful creators of technology and to make informed choices about how they engage with technology — rather than just being passive consumers.

That’s the goal we have in mind as we’re working on lesson resources to help teachers and other educators introduce KS3 students (ages 11 to 14) to AI and ML. We will release these Experience AI lessons very soon.

Why we avoid describing AI as human-like

Our researchers at the Raspberry Pi Computing Education Research Centre have started investigating the topic of AI and ML, including thinking deeply about how AI and ML applications are described to educators and learners.

To support learners to form accurate mental models of AI and ML, we believe it is important to avoid using words that can lead to learners developing misconceptions around machines being human-like in their abilities. That’s why ‘anthropomorphism’ is a term that comes up regularly in our conversations about the Experience AI lessons we are developing.

To anthropomorphise: “to show or treat an animal, god, or object as if it is human in appearance, character, or behaviour”

https://dictionary.cambridge.org/dictionary/english/anthropomorphize

Anthropomorphising AI in teaching materials might lead to learners believing that there is sentience or intention within AI applications. That misconception would distract learners from the fact that it is people who design AI applications and decide how they are used. It also risks reducing learners’ desire to take an active role in understanding AI applications, and in the design of future applications.

Examples of how anthropomorphism is misleading

Avoiding anthropomorphism helps young people to open the closed box of AI applications. Take the example of a smart speaker. It’s easy to describe a smart speaker’s functionality in anthropomorphic terms such as “it listens” or “it understands”. However, we think it’s more accurate and empowering to explain smart speakers as systems developed by people to process sound and carry out specific tasks. Rather than telling young people that a smart speaker “listens” and “understands”, it’s more accurate to say that the speaker receives input, processes the data, and produces an output. This language helps to distinguish how the device actually works from the illusion of a persona the speaker’s voice might conjure for learners.

Another example is the use of AI in computer vision. ML models can, for example, be trained to identify when there is a dog or a cat in an image. An accurate ML model, on the surface, displays human-like behaviour. However, the model operates very differently to how a human might identify animals in images. Where humans would point to features such as whiskers and ear shapes, ML models process pixels in images to make predictions based on probabilities.

Better ways to describe AI

The Experience AI lesson resources we are developing introduce students to AI applications and teach them about the ML models that are used to power them. We have put a lot of work into thinking about the language we use in the lessons and the impact it might have on the emerging mental models of the young people (and their teachers) who will be engaging with our resources.

It’s not easy to avoid anthropomorphism while talking about AI, especially considering the industry standard language in the area: artificial intelligence, machine learning, computer vision, to name but a few examples. At the Foundation, we are still training ourselves not to anthropomorphise AI, and we take a little bit of pleasure in picking each other up on the odd slip-up.

Here are some suggestions to help you describe AI better:

The purpose of our AI education resources

If we are correct in our approach, then whether or not the young people who engage in Experience AI grow up to become AI developers, we will have helped them to become discerning users of AI technologies and to be more likely to see such products for what they are: data-driven applications and not sentient machines.

If you’d like to get involved with Experience AI and use our lessons with your class, you can start by visiting us at experience-ai.org.

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