An AI literacy framework

From her research, Shuchi has developed a framework for teaching about AI that is structured as four interlocking components, each representing a key area of understanding:

• Basic understanding of AI, which refers to foundational knowledge such as what AI is, types of AI systems, and the capabilities of AI technologies
• Ethics and human–AI relationship, which includes the role of humans in regard to AI, ethical considerations, and public perceptions of AI
• Computational thinking/literacy, which relates to how AI works, including building AI applications and training machine learning models
• Data literacy, which addresses the importance of data, including examining data features, data visualisation, and biases

This framework shows the multifaceted nature of AI literacy, which involves an understanding of both technical aspects and ethical and societal considerations. 

Shuchi emphasised the importance of learning about AI ethics, highlighting the topic of bias. There are many ways that bias can be embedded in applications of AI and machine learning, including through the data sets that are used and the design of machine learning models. Shuchi discussed supporting learners to engage with the topic through exploring bias in facial recognition software, sharing activities and resources to use in the classroom that can prompt meaningful discussion, such as this talk by Joy Buolamwini. She also highlighted the Kapor Foundation’s Responsible AI and Tech Justice: A Guide for K–12 Education, which contains questions that educators can use with learners to help them to carefully consider the ethical implications of AI for themselves and for society. 

Computational thinking and AI

In computer science education, computational thinking is generally associated with traditional rule-based programming — it has often been used to describe the problem-solving approaches and processes associated with writing computer programs following rule-based principles in a structured and logical way. However, with the emergence of machine learning, Shuchi described a need for computational thinking frameworks to be expanded to also encompass data-driven, probabilistic approaches, which are foundational for machine learning. This would support learners’ understanding and ability to work with the models that increasingly influence modern technology.

Example activities from research studies

Shuchi shared that a variety of pedagogies have been used in recent research projects on AI education, ranging from hands-on experiences, such as using APIs for classification, to discussions focusing on ethical aspects. You can find out more about these pedagogies in her award-winning paper Teaching AI to K-12 Learners: Lessons, Issues and Guidance. This plurality of approaches ensures that learners can engage with AI and machine learning in ways that are both accessible and meaningful to them.

Shuchi shared examples of activities from two research projects that she has led:

• CS Frontiers engaged high school students in a number of activities involving using NetsBlox and accessing real-world data sets. For example, in one activity, students participated in data science activities such as creating data visualisations to answer questions about climate change. 
• AI & Cybersecurity for Teens explored approaches to teaching AI and machine learning to 13- to 15-year-olds through the use of cybersecurity scenarios. The project aimed to provide learners with insights into how machine learning models are designed, how they work, and how human decisions influence their development. An example activity guided students through building a classification model to analyse social media accounts to determine whether they may be bot accounts or accounts run by a human.

Closing thoughts

At the end of her talk, Shuchi shared some final thoughts addressing teaching about AI to K–12 learners: 

• AI learning requires contextualisation: Think about the data sets, ethical issues, and examples of AI tools and systems you use to ensure that they are relatable to learners in your context.
• AI should not be a solution in search of a problem: Both teachers and learners need to be educated about AI before they start to use it in the classroom, so that they are informed consumers.

Join our next seminar

In our current seminar series, we are exploring teaching about AI and data science. Join us at our next seminar on Tuesday 11 March at 17:00–18:30 GMT to hear Lukas Höper and Carsten Schulte from Paderborn University discuss supporting middle school students to develop their data awareness. 

To sign up and take part in the seminar, click the button below — we will then send you information about joining. We hope to see you there.

I want to join the next seminarThe schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars and recordings page.

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What jobs will be affected by AL, ML, and data science remains to be seen, but it is increasingly clear that students will need to learn something about these topics. There will be new concepts to be taught, new instructional approaches and assessment techniques to be used, new learning activities to be delivered, and we must not neglect the professional development required to help educators master all of this. 

As AI and data science are incorporated into school curricula and teaching and learning materials worldwide, we ask: What’s the research basis for these curricula, pedagogy, and resource choices?

In 2024, we showcased researchers who are investigating how AI can be leveraged to support the teaching and learning of programming. But in 2025, we look at what should be taught about AI, ML, and data science in schools and how we should teach this. 

Our 2025 seminar speakers — so far!

We are very excited that we have already secured several key researchers in the field. 

On 21 January, Shuchi Grover will kick off the seminar series by giving an important overview of AI in the K–12 landscape, including developing both AI literacy and AI ethics. Shuchi will provide concrete examples and recently developed frameworks to give educators practical insights on the topic.

Our second session will focus on a teacher professional development (PD) programme to support the introduction of AI in Upper Bavarian schools. Franz Jetzinger from the Technical University of Munich will summarise the PD programme and share how teachers implemented the topic in their classroom, including the difficulties they encountered.

Again from Germany, Lukas Höper from Paderborn University, with Carsten Schulte will describe important research on data awareness and introduce a framework that is likely to be key for learning about data-driven technology. The pair will talk about the Data Awareness Framework and how it has been used to help learners explore, evaluate, and be empowered in looking at the role of data in everyday applications.  

Our April seminar will see David Weintrop from the University of Maryland introduce, with his colleagues, a data science curriculum called API Can Code, aimed at high-school students. The group will highlight the strategies needed for integrating data science learning within students’ lived experiences and fostering authentic engagement.

Later in the year, Jesús Moreno-Leon from the University of Seville will help us consider the  thorny but essential question of how we measure AI literacy. Jesús will present an assessment instrument that has been successfully implemented in several research studies involving thousands of primary and secondary education students across Spain, discussing both its strengths and limitations.

What to expect from the seminars

Our seminars are designed to be accessible to anyone interested in the latest research about AI education — whether you’re a teacher, educator, researcher, or simply curious. Each session begins with a presentation from our guest speaker about their latest research findings. We then move into small groups for a short discussion and exchange of ideas before coming back together for a Q&A session with the presenter. 

Attendees of our 2024 series told us that they valued that the talks “explore a relevant topic in an informative way“, the “enthusiasm and inspiration”, and particularly the small-group discussions because they “are always filled with interesting and varied ideas and help to spark my own thoughts”. 

The seminars usually take place on Zoom on the first Tuesday of each month at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. 

You can find out more about each seminar and the speakers on our upcoming seminar page. And if you are unable to attend one of our talks, you can watch them from our previous seminar page, where you will also find an archive of all of our previous seminars dating back to 2020.

How to sign up

To attend the seminars, please register here. You will receive an email with the link to join our next Zoom call. Once signed up, you will automatically be notified of upcoming seminars. You can unsubscribe from our seminar notifications at any time.

We hope to see you at a seminar soon!

The post How can we teach students about AI and data science? Join our 2025 seminar series to learn more about the topic appeared first on Raspberry Pi Foundation.

In our most recent research seminar, attendees heard about a research-based initiative called Data Education in Schools. The speakers, Kate Farrell and Professor Judy Robertson from the University of Edinburgh, Scotland, shared how this project aims to empower learners to develop data literacy skills and succeed in a data-driven world.

“Data literacy is the ability to ask questions, collect, analyse, interpret and communicate stories about data.”

– Kate Farrell & Prof. Judy Robertson

Being a data citizen

Scotland’s national curriculum does not explicitly mention data literacy, but the topic is embedded in many subjects such as Maths, English, Technologies, and Social Studies. Teachers in Scotland, particularly in primary schools, have the flexibility to deliver learning in an interdisciplinary way through project-based learning. Therefore, the team behind Data Education in Schools developed a set of cross-curricular data literacy projects. Educators and education policy makers in other countries who are looking to integrate computing topics with other subjects may also be interested in this approach.

The Data Education in Schools projects are aimed not just at giving learners skills they may need for future jobs, but also at equipping them as data citizens in today’s world. A data citizen can think critically, interpret data, and share insights with others to effect change.

Kate and Judy shared an example of data citizenship from a project they had worked on with a primary school. The learners gathered data about how much plastic waste was being generated in their canteen. They created a data visualisation in the form of a giant graph of types of rubbish on the canteen floor and presented this to their local council.

As a result, the council made changes that reduced the amount of plastic used in the canteen. This shows how data citizens are able to communicate insights from data to influence decisions.

A cycle for data literacy projects

Across its projects, the Data Education in Schools initiative uses a problem-solving cycle called the PPDAC cycle. This cycle is a useful tool for creating educational resources and for teaching, as you can use it to structure resources, and to concentrate on areas to develop learner skills.

The five stages of the cycle are: 

1. Problem: Identifying the problem or question to be answered
2. Plan: Deciding what data to collect or use to answer the question
3. Data: Collecting the data and storing it securely
4. Analysis: Preparing, modelling, and visualising the data, e.g. in a graph or pictogram
5. Conclusion: Reviewing what has been learned about the problem and communicating this with others 

Smaller data literacy projects may focus on one or two stages within the cycle so learners can develop specific skills or build on previous learning. A large project usually includes all five stages, and sometimes involves moving backwards — for example, to refine the problem — as well as forwards.

Data literacy for primary school learners

At primary school, the aim of data literacy projects is to give learners an intuitive grasp of what data looks like and how to make sense of graphs and tables. Our speakers gave some great examples of playful approaches to data. This can be helpful because younger learners may benefit from working with tangible objects, e.g. LEGO bricks, which can be sorted by their characteristics. Kate and Judy told us about one learner who collected data about their clothes and drew the results in the form of clothes on a washing line — a great example of how tangible objects also inspire young people’s creativity.

As learners get older, they can begin to work with digital data, including data they collect themselves using physical computing devices such as micro:bit microcontrollers or Raspberry Pi computers.

You can access the seminar slides here.

Free resources for primary (and secondary) schools

For many attendees, one of the highlights of the seminar was seeing the range of high-quality teaching resources for learners aged 3–18 that are part of the Data Education in Schools project. These include: 

• Data 101 videos: A set of 11 videos to help primary and secondary teachers understand data literacy better.
• Data literacy live lessons: Data-related activities presented through live video.
• Lesson resources: Lots of projects to develop learners’ data literacy skills. These are mapped to the Scottish primary and secondary curriculum, but can be adapted for use in other countries too.

More resources are due to be published later in 2023, including a set of prompt cards to guide learners through the PPDAC cycle, a handbook for teachers to support the teaching of data literacy, and a set of virtual data-themed escape rooms.  

You may also be interested in the units of work on data literacy skills that are part of The Computing Curriculum, our complete set of classroom resources to teach computing to 5- to 16-year-olds.

Join our next seminar on primary computing education

At our next seminar we welcome Aim Unahalekhaka from Tufts University, USA, who will share research about a rubric to evaluate young learners’ ScratchJr projects. If you have a tablet with ScratchJr installed, make sure to have it available to try out some activities. The seminar will take place online on Tuesday 6 June at 17.00 UK time, sign up now to not miss out.

To find out more about connecting research to practice for primary computing education, you can see a list of our upcoming monthly seminars on primary (K–5) teaching and learning and watch the recordings of previous seminars in this series.

The post Introducing data science concepts and skills to primary school learners appeared first on Raspberry Pi Foundation.

Genevieve has a background in classical ballet and was also a computing teacher for several years before starting Ready Salted Code, an educational initiative around data-driven dance. She is now coming to the end of her doctoral studies at the University of Cambridge, in which she focuses on raising awareness of data ethics using ballet and brainwave data as narrative tools, working with student Computing teachers.

Why dance and computing?

You may be surprised that there are links between dance, particularly ballet, and computing. Genevieve explained that classical ballet has a strict repetitive routine, using rule-based choreography and algorithms. Her work on data-driven dance had started at the time of the announcement of the new Computing curriculum in England, when she realised the lack of gender balance in her computing classroom. As an expert in both ballet and computing, she was driven by a desire to share the more creative elements of computing with her learners.

Genevieve has been working with a technologist and a choreographer for several years to develop ballets that generate biometric data and include visualisation of such data — hence her term ‘data-driven dance’. This has led to her developing a second focus in her PhD work on how Computing students can discuss questions of ethics based on the kind of biometric and brainwave data that Genevieve is collecting in her research. Students need to learn about the ethical issues surrounding data as part of their Computing studies, and Genevieve has been working with student teachers to explore ways in which her research can be used to give examples of data ethics issues in the Computing curriculum.

Collecting data during dances

Throughout her talk, Genevieve described several examples of dances she had created. One example was [arra]stre, a project that involved a live performance of a dance, plus a series of workshops breaking down the computer science theory behind the performance, including data visualisation, wearable technology, and images triggered by the dancers’ data.

Much of Genevieve’s seminar was focused on the technologies used to capture movement data from the dancers and the challenges this involves. For example, some existing biometric tools don’t capture foot movement — which is crucial in dance — and also can’t capture movements when dancers are in the air. For some of Genevieve’s projects, dancers also wear headsets that allow collection of brainwave data.

Due to interruptions to her research design caused by the COVID-19 pandemic, much of Genevieve’s PhD research took place online via video calls. New tools had to be created to capture dance performances within a digital online setting. Her research uses webcams and mobile phones to record the biometric data of dancers at 60 frames per second. A number of processes are then followed to create a digital representation of the dance: isolating the dancer in the raw video; tracking the skeleton data; using post pose estimation machine learning algorithms; and using additional software to map the joints to the correct place and rotation.

Are your brainwaves personal data?

It’s clear from Genevieve’s research that she is collecting a lot of data from her research participants, particularly the dancers. The projects include collecting both biometric data and brainwave data. Ethical issues tied to brainwave data are part of the field of neuroethics, which comprises the ethical questions raised by our increasing understanding of the biology of the human brain.

Teaching learners to be mindful about how to work with personal data is at the core of the work that Genevieve is doing now. She mentioned that there are a number of ethics frameworks that can be used in this area, and highlighted the UK government’s Data Ethics Framework as being particularly straightforward with its three guiding principles of transparency, accountability, and fairness. Frameworks such as this can help to guide a classroom discussion around the security of the data, and whether the data can be used in discriminatory ways.

Data ethics provides lots of material for discussion in Computing classrooms. To exemplify this, Genevieve recorded her own brainwaves during dance, research, and rest activities, and then shared the data during workshops with student computing teachers. In our seminar Genevieve showed two visualisations of her own brainwave data (see the images above) and discussed how the student computing teachers in her workshops had felt that one was more “personal” than the other. The same brainwave data can be presented as a spreadsheet, or a moving graph, or an image. Student computing teachers felt that the graph data (shown above) felt more medical, and more like permanent personal data than the visualisation (shown above), but that the actual raw spreadsheet data felt the most personal and intrusive.

Watch the recording of Genevieve’s seminar to see her full talk:

You can also access her slides and the links she shared in her talk.

More to explore

There are a variety of online tools you can use to explore augmented reality: for example try out Posenet with the camera of your device.

Genevieve’s seminar used the title ME++, which refers to the data self and the human self: both are important and of equal value. Genevieve’s use of this term is inspired by William J. Mitchell’s book Me++: The Cyborg Self and the Networked City. Within his framing, the I in the digital world is more than the I of the physical world and highlights the posthuman boundary-blurring of the human and non-human. 

Genevieve’s work is also inspired by Luciani Floridi’s philosophical work, and his book Ethics of Information might be something you want to investigate further. You can also read ME++ Data Ethics of Biometrics Through Ballet and AR, a paper by Genevieve about her doctoral work

Join our next seminar

In our final two seminars for this year we are exploring further aspects of cross-disciplinary computing. Just this week, Conrad Wolfram of Wolfram Technologies joined us to present his ideas on maths and a core computational curriculum. We will share a summary and recording of his talk soon.

On 8 November, Tracy Gardner and Rebecca Franks from the Raspberry Pi Foundation team will close out this series by presenting work we have been doing on computing education in non-formal settings. Sign up now to join us for this session:

We will shortly be announcing the theme of a brand-new series of seminars starting in January 2023.  

The post Data ethics for computing education through ballet and biometrics appeared first on Raspberry Pi Foundation.

In this post, we’ll show you how kids use the projects in the ‘More Python’ path, what they can make by following the path, and how the path structure helps them become confident and independent digital makers.

Python coding for kids: Our learning paths

Our ‘Introduction to Python’ learning path is the perfect place to start learning how to use Python, a text-based programming language. When we launched the Intro path in February, we explained why Python is such a popular, useful, and accessible programming language for young people.

Because Python has so much to offer, we have created a second Python path for young people who have learned the basics in the first path. In this new set of six projects, learners will discover new concepts and see how to add different types of real data to their programs.

Key questions answered

Who is this path for?

We have written the projects in this path with young people around the age of 10 to 13 in mind. To code in a text-based language, a young person needs to be familiar with using a keyboard, due to the typing involved. Learners should have already completed the ‘Introduction to Python’ project path, as they will build on the learning from that path.

How do young people learn with the projects? 

Young people need access to a web browser to complete our project paths. Each project contains step-by-step instructions for learners to follow, and tick boxes to mark when they complete each step. On top of that, the projects have steps for learners to:

• Reflect on what they have covered in the project
• Share their projects with others
• See suggestions to upgrade their projects

Young people also have the option to sign up for an account with us so they can save their progress at any time and collect badges.

While learners follow the project instructions in this project path, they write their code into Trinket, a free web-based coding platform accessible in a browser. Each project contains a link to a starter Trinket, which includes everything to get started writing Python code — no need to install any additional software.

If they prefer, however, young people also have the option of instead writing their code in a desktop-based programming environment, such as Thonny, as they work through the projects.

What will young people learn?  

To use data in their Python programs, the project instructions show learners how to:

• Create and use lists
• Create and use dictionaries
• Read data from a data file

The projects support learners as they explore new concepts of digital visual media and: 

• Create charts using the Python library Pygal
• Plot pins on a map
• Create randomised artwork

In each project, learners reflect and answer questions about their work, which is important for connecting the project’s content to their pre-existing knowledge.

As they work through the projects, learners see different ways to present data and then decide how they want to present their data in the final project in the path. You’ll find out what the projects are on the path page, or at the bottom of this blog post.

The project path helps learners become independent coders and digital makers, as each project contains slightly less support than the one before. You can read about how our project paths are designed to increase young people’s independence, and explore our other free learning paths for young coders

How long will the path take to complete?

We’ve designed the path to be completed in around six one-hour sessions, with one hour per project, at home, in school, or at a coding club. The project instructions encourage learners to add code to upgrade their projects and go further if they wish. This means that young people might want to spend a little more time getting their projects exactly as they imagine them.

What can young people do next?

Use Unity to create a 3D world

Unity is a free development environment for creating 3D virtual environments, including games, visual novels, and animations, all with the text-based programming language C#. Our ‘Introduction to Unity’ project path for keen coders shows how to make 3D worlds and games with collectibles, timers, and non-player characters.

Take part in Coolest Projects Global

At the end of the ‘More Python’ path, learners are encouraged to register a project they’ve made using their new coding skills for Coolest Projects Global, our free and world-leading online technology showcase for young tech creators. The project they register will become part of the online gallery, where members of the Coolest Projects community can celebrate each other’s creations.

We welcome projects from all young people, whether they are beginners or experienced coders and digital makers. Coolest Projects Global is a unique opportunity for young people to share their ingenuity with the world and with other young people who love coding and creating with digital technology.

Details about the projects in ‘More Python’

The ‘More Python’ path is structured according to our Digital Making Framework, with three Explore project, two Design projects, and a final Invent project.

Explore project 1: Charting champions

In this Explore project, learners discover the power of lists in Python by creating an interactive chart of Olympic medals. They learn how to read data from a text file and then present that data as a bar chart.

Explore project 2: Solar system

In this Explore project, learners create a simulation of the solar system. They revisit the drawing and animation skills that they learned in the ‘Introduction to Python’ project path to produce animated planets orbiting the sun. The animation is based on real data taken from a data file to simulate the speed that the planets move at as they orbit. The simulation is also interactive, using dictionaries to display data about the planets that have been selected.

Explore project 3: Codebreaker

The final Explore project gets learners to build on their knowledge of lists and dictionaries by creating a program that encodes and decodes a message using an Atbash cipher. The Atbash cipher was originally developed in the Hebrew language. It takes the alphabet and matches it to its reverse order to create a secret message. They also create a script that checks how many times certain letters have been used in an encoded message, so that they can discover patterns.

Design project 1: Encoded art

The first Design project allows learners to create fun pieces of artwork by encoding the letters of their name into images, patterns, or drawings. Learners can choose the images that will be produced for each letter, and whether these appear at random or in a geometric pattern.
Learners are encouraged to share their encoded artwork in the community library, where there are lots of fun projects to discover already. In this project, learners apply all of the coding skills and knowledge covered in the Explore projects, including working with dictionaries and lists.

Design project 2: Mapping data

In the next Design project, learners access data from a data file and use it to create location pins on a world map. They have six datasets to choose from, so they can use one that interests them. They can also choose from a variety of maps and design their own pin to truly personalise their projects.

Invent project: Persuasive data presentation

This project is designed to use all of the skills and knowledge covered in this path, and most of the skills from the ‘Introduction to Python’ path. Learners can choose from eight datasets to create data visualisations. They are also given instructions on how to access and prepare other datasets if they want to visualise data about a different topic.
Once learners have chosen their dataset, they can decide how they want it to be displayed. This could be a chart, a map with pins, or a unique data visualisation. There are lots of example projects to provide inspiration for learners. One of our favourites is the ISS Expedition project, which places flags on the ISS depending on the expedition number you enter.

The post Python coding for kids: Moving beyond the basics appeared first on Raspberry Pi Foundation.

We can’t talk about AI education — or computer science education more generally — without considering the context in which we deliver it, and the societal issues surrounding computing, AI, and data. For this International Women’s Day, I’m writing about the intersection of AI and gender, particularly with respect to gender bias in machine learning.

The quest for gender equality

Gender inequality is everywhere, and researchers, activists, and initiatives, and governments themselves, have struggled since the 1960s to tackle it. As women and girls around the world continue to suffer from discrimination, the United Nations has pledged, in its Sustainable Development Goals, to achieve gender equality and to empower all women and girls.

While progress has been made, new developments in technology may be threatening to undo this. As Susan Leavy, a machine learning researcher from the Insight Centre for Data Analytics, puts it:

Artificial intelligence is increasingly influencing the opinions and behaviour of people in everyday life. However, the over-representation of men in the design of these technologies could quietly undo decades of advances in gender equality.

Susan Leavy, 2018 [1]

Gender-biased data

In her 2019 award-winning book Invisible Women: Exploring Data Bias in a World Designed for Men [2], Caroline Criado Perez discusses the effects of gender-biased data. She describes, for example, how the designs of cities, workplaces, smartphones, and even crash test dummies are all based on data gathered from men. She also discusses that medical research has historically been conducted by men, on male bodies.

Looking at this problem from a different angle, researcher Mayra Buvinic and her colleagues highlight that in most countries of the world, there are no sources of data that capture the differences between male and female participation in civil society organisations, or in local advisory or decision making bodies [3]. A lack of data about girls and women will surely impact decision making negatively. 

Bias in machine learning

Machine learning (ML) is a type of artificial intelligence technology that relies on vast datasets for training. ML is currently being use in various systems for automated decision making. Bias in datasets for training ML models can be caused in several ways. For example, datasets can be biased because they are incomplete or skewed (as is the case in datasets which lack data about women). Another example is that datasets can be biased because of the use of incorrect labels by people who annotate the data. Annotating data is necessary for supervised learning, where machine learning models are trained to categorise data into categories decided upon by people (e.g. pineapples and mangoes).

In order for a machine learning model to categorise new data appropriately, it needs to be trained with data that is gathered from everyone, and is, in the case of supervised learning, annotated without bias. Failing to do this creates a biased ML model. Bias has been demonstrated in different types of AI systems that have been released as products. For example:

Facial recognition: AI researcher Joy Buolamwini discovered that existing AI facial recognition systems do not identify dark-skinned and female faces accurately. Her discovery, and her work to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives, is narrated in the 2020 documentary Coded Bias. 

Natural language processing: Imagine an AI system that is tasked with filling in the missing word in “Man is to king as woman is to X” comes up with “queen”. But what if the system completes “Man is to software developer as woman is to X” with “secretary” or some other word that reflects stereotypical views of gender and careers? AI models called word embeddings learn by identifying patterns in huge collections of texts. In addition to the structural patterns of the text language, word embeddings learn human biases expressed in the texts. You can read more about this issue in this Brookings Institute report. 

Not noticing

There is much debate about the level of bias in systems using artificial intelligence, and some AI researchers worry that this will cause distrust in machine learning systems. Thus, some scientists are keen to emphasise the breadth of their training data across the genders. However, other researchers point out that despite all good intentions, gender disparities are so entrenched in society that we literally are not aware of all of them. White and male dominance in our society may be so unconsciously prevalent that we don’t notice all its effects.

As sociologist Pierre Bourdieu famously asserted in 1977: “What is essential goes without saying because it comes without saying: the tradition is silent, not least about itself as a tradition.” [4]. This view holds that people’s experiences are deeply, or completely, shaped by social conventions, even those conventions that are biased. That means we cannot be sure we have accounted for all disparities when collecting data.

What is being done in the AI sector to address bias?

Developers and researchers of AI systems have been trying to establish rules for how to avoid bias in AI models. An example rule set is given in an article in the Harvard Business Review, which describes the fact that speech recognition systems originally performed poorly for female speakers as opposed to male ones, because systems analysed and modelled speech for taller speakers with longer vocal cords and lower-pitched voices (typically men).

The article recommends four ways for people who work in machine learning to try to avoid gender bias:

• Ensure diversity in the training data (in the example from the article, including as many female audio samples as male ones)
• Ensure that a diverse group of people labels the training data
• Measure the accuracy of a ML model separately for different demographic categories to check whether the model is biased against some demographic categories
• Establish techniques to encourage ML models towards unbiased results

What can everybody else do?

The above points can help people in the AI industry, which is of course important — but what about the rest of us? It’s important to raise awareness of the issues around gender data bias and AI lest we find out too late that we are reintroducing gender inequalities we have fought so hard to remove. Awareness is a good start, and some other suggestions, drawn out from others’ work in this area are:

Improve the gender balance in the AI workforce

Having more women in AI and data science, particularly in both technical and leadership roles, will help to reduce gender bias. A 2020 report by the World Economic Forum (WEF) on gender parity found that women account for only 26% of data and AI positions in the workforce. The WEF suggests five ways in which the AI workforce gender balance could be addressed:

1. Support STEM education
2. Showcase female AI trailblazers
3. Mentor women for leadership roles
4. Create equal opportunities
5. Ensure a gender-equal reward system

Ensure the collection of and access to high-quality and up-to-date gender data

We need high-quality dataset on women and girls, with good coverage, including country coverage. Data needs to be comparable across countries in terms of concepts, definitions, and measures. Data should have both complexity and granularity, so it can be cross-tabulated and disaggregated, following the recommendations from the Data2x project on mapping gender data gaps.

Educate young people about AI

At the Raspberry Pi Foundation we believe that introducing some of the potential (positive and negative) impacts of AI systems to young people through their school education may help to build awareness and understanding at a young age. The jury is out on what exactly to teach in AI education, and how to teach it. But we think educating young people about new and future technologies can help them to see AI-related work opportunities as being open to all, and to develop critical and ethical thinking.

In our AI education seminars we heard a number of perspectives on this topic, and you can revisit the videos, presentation slides, and blog posts. We’ve also been curating a list of resources that can help to further AI education — although there is a long way to go until we understand this area fully. 

We’d love to hear your thoughts on this topic.

References

[1] Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, 14–16.

[2] Perez, C. C. (2019). Invisible Women: Exploring Data Bias in a World Designed for Men. Random House.

[3] Buvinic M., Levine R. (2016). Closing the gender data gap. Significance 13(2):34–37 

[4] Bourdieu, P. (1977). Outline of a Theory of Practice (No. 16). Cambridge University Press. (p.167)

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The story of artificial intelligence (AI) is a story about humans trying to understand what makes them human. Some of the episodes in this story are fascinating. These could help your learners catch a glimpse of what this field is about and, with luck, compel them to investigate further.                   

The imitation game

In 1950, Alan Turing published a philosophical essay titled Computing Machinery and Intelligence, which started with the words: “I propose to consider the question: Can machines think?” Yet Turing did not attempt to define what it means to think. Instead, he suggested a game as a proxy for answering the question: the imitation game. In modern terms, you can imagine a human interrogator chatting online with another human and a machine. If the interrogator does not successfully determine which of the other two is the human and which is the machine, then the question has been answered: this is a machine that can think.

This imitation game is now a fiercely debated benchmark of artificial intelligence called the Turing test. Notice the shift in focus that Turing suggests: thinking is to be identified in terms of external behaviour, not in terms of any internal processes. Humans are still the yardstick for intelligence, but there is no requirement that a machine should think the way humans do, as long as it behaves in a way that suggests some sort of thinking to humans.

In his essay, Turing also discusses learning machines. Instead of building highly complex programs that would prescribe every aspect of a machine’s behaviour, we could build simpler programs that would prescribe mechanisms for learning, and then train the machine to learn the desired behaviour. Turing’s text provides an excellent metaphor that could be used in class to describe the essence of machine learning: “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. We have thus divided our problem into two parts: the child-programme and the education process.”

It is remarkable how Turing even describes approaches that have since been evolved into established machine learning methods: evolution (genetic algorithms), punishments and rewards (reinforcement learning), randomness (Monte Carlo tree search). He even forecasts the main issue with some forms of machine learning: opacity. “An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil’s behaviour.”

The evolution of a definition

The term ‘artificial intelligence’ was coined in 1956, at an event called the Dartmouth workshop. It was a gathering of the field’s founders, researchers who would later have a huge impact, including John McCarthy, Claude Shannon, Marvin Minsky, Herbert Simon, Allen Newell, Arthur Samuel, Ray Solomonoff, and W.S. McCulloch.   

The simple and ambitious definition for artificial intelligence, included in the proposal for the workshop, is illuminating: ‘making a machine behave in ways that would be called intelligent if a human were so behaving’. These pioneers were making the assumption that ‘every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it’. This assumption turned out to be patently false and led to unrealistic expectations and forecasts. Fifty years later, McCarthy himself stated that ‘it was harder than we thought’.

Modern definitions of intelligence are of distinctly different flavour than the original one: ‘Intelligence is the quality that enables an entity to function appropriately and with foresight in its environment’ (Nilsson). Some even speak of rationality, rather than intelligence: ‘doing the right thing, given what it knows’ (Russell and Norvig).

Read the whole of this brief history of AI in Hello World #12

In the full article, which you can read in the free PDF copy of the issue, George looks at:

• Early advances researchers made from the 1950s onwards while developing games algorithms, e.g. for chess.
• The 1997 moment when Deep Blue, a purpose-built IBM computer, beating chess world champion Garry Kasparov using a search approach.
• The 2011 moment when Watson, another IBM computer system, beating two human Jeopardy! champions using multiple techniques to answer questions posed in natural language.
• The principles behind artificial neural networks, which have been around for decades and are now underlying many AI/machine learning breakthroughs because of the growth in computing power and availability of vast datasets for training.
• The 2017 moment when AlphaGo, an artificial neural network–based computer program by Alphabet’s DeepMind, beating Ke Jie, the world’s top-ranked Go player at the time.

More on machine learning and AI education in Hello World #12

In your free PDF of Hello World issue 12, you’ll also find:

• An interview with University of Cambridge statistician David Spiegelhalter, whose work shaped some of the foundations of AI, and who shares his thoughts on data science in schools and the limits of AI 
• An introduction to Popbots, an innovative project by MIT to open AI to the youngest learners
• An article by Ken Kahn, researcher in the Department of Education at the University of Oxford, on using the block-based Snap! language to introduce your learners to natural language processing
• Unplugged and online machine learning activities for learners age 7 to 16 in the regular ‘Lesson plans’ section
• And lots of other relevant articles

You can also read many of these articles online on the Hello World website.

Find more resources for AI and data science education

In Hello World issue 16, the focus is on all things data science and data literacy for your learners. As always, you can download a free copy of the issue. And on our Hello World podcast, we chat with practicing computing educators about how they bring AI, AI ethics, machine learning, and data science to the young people they teach.

If you want a practical introduction to the basics of machine learning and how to use it, take our free online course.

There are still many open questions about what good AI and data science education looks like for young people. To learn more, you can watch our panel discussion about the topic, and join our monthly seminar series to hear insights from computing education researchers around the world.

We are also collating a growing list of educational resources about these topics based on our research seminars, seminar participants’ recommendations, and our own work. Find the resource list here.

The post Snapshots from the history of AI, plus AI education resources appeared first on Raspberry Pi Foundation.

In the second seminar of the series, we were excited to hear from Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from the University of Paderborn, Germany, who presented on the topic of teaching AI and machine learning (ML) from a data-centric perspective. Their talk raised the question of whether and how AI and ML should be taught differently from other themes in the computer science curriculum at school.

Machine behaviour — a new field of study?

The rationale behind the speakers’ work is a concept they call hybrid interaction system, referring to the way that humans and machines interact. To explain this concept, Carsten referred to a 2019 article published in Nature by Iyad Rahwan and colleagues: Machine behaviour. The article’s authors propose that the study of AI agents (complex and simple algorithms that make decisions) should be a separate, cross-disciplinary field of study, because of the ubiquity and complexity of AI systems, and because these systems can have both beneficial and detrimental impacts on humanity, which can be difficult to evaluate. (Our previous seminar by Mhairi Aitken highlighted some of these impacts.) The authors state that to study this field, we need to draw on scientific practices from across different fields, as shown below:

In establishing their argument, the authors compare the study of animal behaviour and machine behaviour, citing that both fields consider aspects such as mechanism, development, evolution and function. They describe how part of this proposed machine behaviour field may focus on studying individual machines’ behaviour, while collective machines and what they call ‘hybrid human-machine behaviour’ can also be studied. By focusing on the complexities of the interactions between machines and humans, we can think both about machines shaping human behaviour and humans shaping machine behaviour, and a sort of ‘co-behaviour’ as they work together. Thus, the authors conclude that machine behaviour is an interdisciplinary area that we should study in a different way to computer science.

Carsten and his team said that, as educators, we will need to draw on the parameters and frameworks of this machine behaviour field to be able to effectively teach AI and machine learning in school. They argue that our approach should be centred on data, rather than on code. I believe this is a challenge to those of us developing tools and resources to support young people, and that we should be open to these ideas as we forge ahead in our work in this area.

Ideas or artefacts?

In the interpretation of computational thinking popularised in 2006 by Jeanette Wing, she introduces computational thinking as being about ‘ideas, not artefacts’. When we, the computing education community, started to think about computational thinking, we moved from focusing on specific technology — and how to understand and use it — to the ideas or principles underlying the domain. The challenge now is: have we gone too far in that direction?

Carsten argued that, if we are to understand machine behaviour, and in particular, human-machine co-behaviour, which he refers to as the hybrid interaction system, then we need to be studying   artefacts as well as ideas.

Throughout the seminar, the speakers reminded us to keep in mind artefacts, issues of bias, the role of data, and potential implications for the way we teach.

Studying machine learning: a different focus

In addition, Carsten highlighted a number of differences between learning ML and learning other areas of computer science, including traditional programming:

1. The process of problem-solving is different. Traditionally, we might try to understand the problem, derive a solution in terms of an algorithm, then understand the solution. In ML, the data shapes the model, and we do not need a deep understanding of either the problem or the solution.
2. Our tolerance of inaccuracy is different. Traditionally, we teach young people to design programs that lead to an accurate solution. However, the nature of ML means that there will be an error rate, which we strive to minimise. 
3. The role of code is different. Rather than the code doing the work as in traditional programming, the code is only a small part of a real-world ML system. 

These differences imply that our teaching should adapt too.

ProDaBi: a programme for teaching AI, data science, and ML in secondary school

In Germany, education is devolved to state governments. Although computer science (known as informatics) was only last year introduced as a mandatory subject in lower secondary schools in North Rhine-Westphalia, where Paderborn is located, it has been taught at the upper secondary levels for many years. ProDaBi is a project that researchers have been running at Paderborn University since 2017, with the aim of developing a secondary school curriculum around data science, AI, and ML.

The ProDaBi curriculum includes:

• Two modules for 11- to 12-year-olds covering decision trees and data awareness (ethical aspects), introduced this year
• A short course for 13-year-olds covering aspects of artificial intelligence, through the game Hexapawn
• A set of modules for 14- to 15-year-olds, covering data science, data exploration, decision trees, neural networks, and data awareness (ethical aspects), using Jupyter notebooks
• A project-based course for 18-year-olds, including the above topics at a more advanced level, using Codap and Jupyter notebooks to develop practical skills through projects; this course has been running the longest and is currently in its fourth iteration

Although the ProDaBi project site is in German, an English translation is available.

Our speakers described example activities from three of the modules:

• Hexapawn, a two-player game inspired by the work of Donald Michie in 1961. The purpose of this activity is to support learners in reflecting on the way the machine learns. Children can then relate the activity to the behavior of AI agents such as autonomous cars. An English version of the activity is available. 
• Data cards, a series of activities to teach about decision trees. The cards are designed in a ‘Top Trumps’ style, and based on food items, with unplugged and digital elements. 
• Data awareness, a module focusing on the amount of data an individual can generate as they move through a city, in this case through the mobile phone network. Children are encouraged to reflect on personal data in the context of the interaction between the human and data-driven artefact, and how their view of the world influences their interpretation of the data that they are given.

Questioning how we should teach AI and ML at school

There was a lot to digest in this seminar: challenging ideas and some new concepts, for me anyway. An important takeaway for me was how much we do not yet know about the concepts and skills we should be teaching in school around AI and ML, and about the approaches that we should be using to teach them effectively. Research such as that being carried out in Paderborn, demonstrating a data-centric approach, can really augment our understanding, and I’m looking forward to following the work of Carsten and his team.

Carsten and colleagues ended with this summary and discussion point for the audience:

“‘AI education’ requires developing an adequate picture of the hybrid interaction system — a kind of data-driven, emergent ecosystem which needs to be made explicitly to understand the transformative role as well as the technological basics of these artificial intelligence tools and how they are related to data science.”

You can catch up on the seminar, including the Q&A with Carsten and his colleagues, here:

Join our next seminar

This seminar really extended our thinking about AI education, and we look forward to introducing new perspectives from different researchers each month. At our next seminar on Tuesday 2 November at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Professor Matti Tedre and Henriikka Vartiainen (University of Eastern Finland). The two Finnish researchers will talk about emerging trajectories in ML education for K-12. We look forward to meeting you there.

Carsten and their colleagues are also running a series of seminars on AI and data science: you can find out about these on their registration page.

You can increase your own understanding of machine learning by joining our latest free online course!

[1] Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., … & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.

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Four key areas of AI are machine learning, robotics, computer vision, and natural language processing. Other advances in computing technology mean we can now store and efficiently analyse colossal amounts of data (big data); consequently, data science was formed as an interdisciplinary field combining mathematics, statistics, and computer science. Data science is often presented as intertwined with machine learning, as data scientists commonly use machine learning techniques in their analysis.

AI impacts everyone, so we need to teach young people about it

AI and data science have recently received huge amounts of attention in the media, as machine learning systems are now used to make decisions in areas such as healthcare, finance, and employment. These AI technologies cause many ethical issues, for example as explored in the film Coded Bias. This film describes the fallout of researcher Joy Buolamwini’s discovery that facial recognition systems do not identify dark-skinned faces accurately, and her journey to push for the first-ever piece of legislation in the USA to govern against bias in the algorithms that impact our lives. Many other ethical issues concerning AI exist and, as highlighted by UNESCO’s examples of AI’s ethical dilemmas, they impact each and every one of us.

So how do such advances in technology impact the education of young people? In the UK, a recent Royal Society report on machine learning recommended that schools should “ensure that key concepts in machine learning are taught to those who will be users, developers, and citizens” — in other words, every child. The AI Roadmap published by the UK AI Council in 2020 declared that “a comprehensive programme aimed at all teachers and with a clear deadline for completion would enable every teacher confidently to get to grips with AI concepts in ways that are relevant to their own teaching.” As of yet, very few countries have incorporated any study of AI and data science in their school curricula or computing programmes of study.

Partnering with The Alan Turing Institute for a new seminar series

Here at the Raspberry Pi Foundation, AI, machine learning, and data science are important topics both in our learning resources for young people and educators, and in our programme of research. So we are delighted to announce that starting this autumn we are hosting seven free, online seminars on the topic of AI, machine learning, and data science education, in partnership with The Alan Turing Institute.

The Alan Turing Institute is the UK’s national institute for data science and artificial intelligence and does pioneering work in data science research and education. The Institute conducts many different strands of research in this area and has a special interest group focused on data science education. As such, our partnership around the seminar series enables us to explore our mutual interest in the needs of young people relating to these technologies.

This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice […].

Dr Matt Forshaw, The Alan Turing Institute

Dr Matt Forshaw, National Skills Lead at The Alan Turing Institute and Senior Lecturer in Data Science at Newcastle University, says: “We are delighted to partner with the Raspberry Pi Foundation to bring you this seminar series on AI, machine learning, and data science. This promises to be an outstanding series drawing from international experts who will share examples of pedagogic best practice and cover critical topics in education, highlighting ethical, fair, and safe use of these emerging technologies.”

Our free seminar series about AI, machine learning, and data science

At our computing education research seminars, we hear from a range of experts in the field and build an international community of researchers, practitioners, and educators interested in this important area. Our new free series of seminars runs from September 2021 to February 2022, with some excellent and inspirational speakers:

• Tues 7 September: Dr Mhairi Aitken from The Alan Turing Institute will share a talk about AI ethics, setting out key ethical principles and how they apply to AI before discussing the ways in which these relate to children and young people.
• Tues 5 October: Professor Carsten Schulte, Yannik Fleischer, and Lukas Höper from Paderborn University in Germany will use a series of examples from their ProDaBi programme to explore whether and how AI and machine learning should be taught differently from other topics in the computer science curriculum at school. The speakers will suggest that these topics require a paradigm shift for some teachers, and that this shift has to do with the changed role of algorithms and data, and of the societal context.
• Tues 2 November: Professor Matti Tedre and Dr Henriikka Vartiainen from the University of Eastern Finland will focus on machine learning in the school curriculum. Their talk will map the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education.
• Tues 7 December: Professor Rose Luckin from University College London will be looking at the breadth of issues impacting the teaching and learning of AI.
• Tues 11 January: We’re delighted that Dr Dave Touretzky and Dr Fred Martin (Carnegie Mellon University and University of Massachusetts Lowell, respectively) from the AI4K12 Initiative in the USA will present some of the key insights into AI that the researchers hope children will acquire, and how they see K-12 AI education evolving over the next few years.
• Tues 1 February: Tara Chklovski (Technovation) will speak about teaching young to use AI to tackle the sustainable development goals
• Tues 1 March: Stefania Druga (University of Washington) will present her work on democratising AI education with and for families

How you can join our online seminars

All seminars start at 17:00 UK time (18:00 Central European Time, 12 noon Eastern Time, 9:00 Pacific Time) and take place in an online format, with a presentation, breakout discussion groups, and a whole-group Q&A.

Sign up now and we’ll send you the link to join on the day of each seminar — don’t forget to put the dates in your diary!

In the meantime, you can explore some of our educational resources related to machine learning and data science:

• Try out our Scratch project guides introducing machine learning and data science concepts: Smart classroom assistant, Journey to school, Alien language, Did you like it?, and Electricity generation
• For young people wanting to use Python, we have a five-project learning journey that introduces concepts and practical tools for machine vision
• For educators, we will launch a new, free online course on teaching machine learning this autumn!
• The latest issue of our free computing education magazine Hello World focuses on data science and data literacy — you can download the PDF now

The post Educating young people in AI, machine learning, and data science: new seminar series appeared first on Raspberry Pi Foundation.

When I was searching for contributors for this issue of Hello World, a pattern quickly began to emerge: “Data? You want to speak to Kate.” Kate Farrell is director of curriculum development and professional learning on the Data Education in Schools project, part of the Data-Driven Innovation Skills Gateway in Scotland. With the project developing teaching materials, professional development, and even qualifications for schools that want to teach data education to learners aged 3–18, “It’s not the kind of role that fits easily on a business card,” she laughs.

The project started in 2019, with the team looking at the Scottish curriculum and mapping out where data could be embedded and how it could be used to support various subjects. “We know that teachers are under stress and won’t be able to deliver extra stuff, so we’re looking to understand how we get better at doing data literacy within the rest of the curriculum,” Kate explains. “How do we provide and support opportunities to look at data in the rest of the curriculum in cool new ways?”

The team runs monthly seminars drawing upon this theme, to help teachers see its applicability across all subjects. “We like taking topics that you wouldn’t instantly think are about data science. Yes, the sciences, computer science, and maths are where you would expect it, but there are huge amounts of data and data use in geography, music, social studies, and even PE.”

One example is the DataFit series of lessons for upper primary and lower secondary students, with a mission to simultaneously increase data literacy and physical activity literacy. This includes an introduction to activity-monitoring devices, such as step counters on phones. The lesson has the twin aims of teaching students how monitoring steps or sleep activity can be a positive thing, and also encouraging them to reflect on how they feel about their phone collecting their personal data.

“A lot of students don’t realise their phone is keeping track of their step count, just by virtue of it sitting in their pockets,” Kate muses. “It’s been interesting to see just how little some learners know about the data that’s being kept and tracked about them.”

Data Education in Schools ran a similarly themed workshop for students aged 10–11, with a series of events in an imagined Data Town being examined, to investigate how data can impact our lives. The day started by giving each student a cardboard mobile phone on which they could install apps in the form of stickers if they gave the town certain pieces of information about themselves, such as their favourite colour or football team. “Some kids would just install anything, give up any data, because they wanted the stickers – just like many kids will just download any app,” Kate explains. The apps and associated products then developed as they gathered more data, which was then presented back to the students. The purpose was to get students to reflect on how they felt about the products and how they used their data.

Later in the workshop, the mayor of Data Town announced that the town had sold the data to an advertising company who wanted to know people’s favourite colour, and to a gym who wanted to know their fitness data to help them decide the location of a new branch. “This meant a series of ‘aha’ moments for students, as they realised what sharing their data meant. Some of the kids who had opted not to collect the stickers were suddenly very smug!”

The project keeps a balance in the story it tells about data, with teaching materials encompassing both the risks of data collection and the huge benefits it can bring. “That is our main aim: how can we help learners use data to make their lives and the lives of their communities better — data for social good.” In the Data Town workshop, students also chose to share data with hospitals and researchers, and later found that this had helped them to develop new medicines. “We didn’t just want to send across the message that sharing data is bad. Yes, you can share your data, but be aware who you’re sharing it with, who you’re trusting with it.”

The materials that Data Education in Schools has produced use a framework called PPDAC: Problem, Plan, Data, Analysis, and Conclusion. This is an established approach to statistical literacy, and using this data problem-solving cycle in a real-world context is a powerful way to engage learners with data topics. “The aim is to empower students with the tools to be campaigning, to be making real-world changes to their lives and their communities using data.”

Kate gives a simple example of how a class could look at how much plastic their canteen is using, collecting the data on plastic products and then using that data to make the case to reduce their plastic consumption.

The project has also worked with Scottish exam board SQA to develop a National Progression Award in Data Science; they believe it is the world’s first data science school qualification. The award is aimed at upper secondary students, colleges, and workplaces as an introductory qualification in data science. It carries the same ethos as their materials for younger learners: to help students understand how data is used in society, both negatively and positively, and develop skills to help them make better decisions.

“I want people to realise that although data science sounds scary, it’s so important to learners’ lives these days. We’ve seen it with the pandemic. Being able to interpret and analyse data is hugely important. We need learners to be able to look at the news, and their social media stream, and question what they’re looking at, or ask: where is the evidence? This is so important, whether or not they go on to become a data scientist… although we’d love it if they did!”

Subscribe to Hello World for free

Issue 16 of Hello World focuses on data science and data literacy; it is full of teaching ideas and inspiration to help you and your students use data to make decisions and to make sense of the world. Also in this issue:

• Key digital skills for young people with SEND
• Top tips and case studies on how to run a successful computing club
• Reflections on decolonising the computing curriculum
• And more

Subscribe now to get each new digital issue straight to your inbox! And if you’re based in the UK and do paid or unpaid work in education, you can subscribe for free print issues.

PS Have you listened to our Hello World podcast yet? Episode 4 has just come out, and it’s great! Listen and subscribe wherever you get your podcasts.

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