AI, machine learning and data science seminar series: Resources

AI Ethics and Engagement with Children and Young People (7 Sept 2021)

From Mhairi Aitken at The Alan Turing Institute, we learned about AI ethics and engagement with children and young people (see her slides).

Exploring the data-driven world: Teaching AI and ML from a data-centric perspective (5 Oct 2021)

The ProDaBi project team at the University of Paderborn presented findings about their secondary school data science, AI, and ML curriculum, and shared a host of teaching resources and academic papers:

• An unplugged activity for teaching AI: Matchbox educable noughts and crosses engine

• An unplugged activity for teaching machine learning: Sweet machine by Computer Science for Fun (CS4FN)

• A ‘Human vs Machine!’ board game (English translation available soon)

• Food data cards for learning about classification (English translation available soon)

• A teaching module on the exploration of location data (English translation available soon)

• An academic paper about research on machine behaviour

ML education for K-12: emerging trajectories (2 Nov 2021)

Professor Matti Tedre and Dr Henriikka Vartiainen from the University of Eastern Finland shared their research on teaching machine learning in schools, and what changes to computational thinking are needed. They shared:

• A classroom tool for creating machine learning apps

• An academic paper on teaching machine learning in K–12 classrooms and pedagogical and technological trajectories

• An academic paper about the changes needed to computational thinking (CT 2.0)

What is it about AI that makes it useful for teachers and learners? (7 Dec 2021)

Professor Rose Luckin from University College London shared her work on the ways in which AI can be used to support the teaching and learning process. She shared:

• The Ethical Framework for AI in Education, which she helped to develop

• An AI readiness webinar series for educators, available from Prof Luckin’s research accelerator programme EDUCATE Ventures

• A forthcoming book she has co-authored on AI for school teachers

• Her book Machine Learning and Human Intelligence: The future of education for the 21st century

Teaching Artificial Intelligence in K-12 (11 Jan 2022)

Professor David S. Touretzky from Carnegie Mellon University and Professor Fred Martin from the University of Massachusetts Lowell shared their work as part of the AI4K12 Initiative. They shared:

• AI4K12’s AI resource directory, which includes links to various demos including those referenced by Dave in the presentation

• AI4K12’s ‘Five Big Ideas in AI’ poster (available in 17 languages)

• AI4K12’s grade band progression charts for Big Ideas 1-3 (progression chart for Big Idea 4 coming soon!)

• Calypso software, designed for teaching AI thinking. There is a free simulator version that runs in a browser, or a Cosmo robot version

• Dale Lane’s book Machine Learning for Kids, based on the website of the same name

Teaching youth to use AI to tackle the Sustainable Development Goals (1 Feb 2022)

Tara Chklovski, CEO of Technovation, shared learnings from Technovation’s work inspiring and supporting girls and young women to use technology to tackle complex real-world problems. She shared:

• Technovation’s curriculum, which includes lesson plans and video tutorials

• Technovation’s impact reports, which examine how their programmes affect participants

• App Inventor, the block-based programming environment used by Technovation for their beginner curriculum. App Inventor also offers several AI extensions

• An example of a presentation used to promote Technovation sessions

Democratizing AI education with and for families (1 Mar 2022)

Stefania Druga from the University of Washington shared her work exploring how children and their families interact with and make sense of the growing collection of “smart” inter-connected devices in the world around them. She shared:

• Her paper Family as a Third Space for AI Literacies: How do children and parents learn about AI together?

• A blogpost she has published about her research

• Her AI Playground webpage, where you can access the AI literacy activities that families undertook as part of Stefania’s research project

• Her list of publications, which include this paper on how children’s perceptions of machine intelligence change when training & coding smart programs

• Takeuchi and Stevens’ framework on joint media engagement

• The 4As AI Literacy Framework for Families

• Blakeley H. Payne’s (MIT Media Lab) AI Ethics curriculum for middle school students

Experience AI

Experience AI is a learning programme that offers cutting-edge secondary school resources on AI and machine learning for teachers and their students. Developed in partnership by the Raspberry Pi Foundation and Google DeepMind, the programme aims to support teachers around the world in the exciting and fast-moving area of AI, and get young people passionate about the subject.

Teacher professional development resources

• An Introduction to machine learning online course

• An online course about discussing ethics and the impact of technology in the classroom

• Issue 23 of our Hello World magazine for computing educators, with teaching and AI as the main focus

• Issue 16 of our Hello World magazine, with data science as the main focus

• Blog posts about AI education

Formal education resources

Within The Computing Curriculum, we include classroom lesson materials that build data literacy and fundamental data science skills:

• For learners in key stage 1 (age 5–7):

  • Year 1 Grouping Data

  • Year 2 Pictograms

• For learners in key stage 2 (age 7–11):

  • Year 3 Branching databases

  • Year 4 Data logging

  • Year 5 Flat file databases

  • Year 6 Introduction to spreadsheets

• For learners in key stage 3 (age 11–14):

  • Year 7 Spreadsheets

  • Year 9 Introduction to data science

• For learners in key stage 4 (age 14–16):

  • Impacts of technology

  • Databases and SQL

  • Spreadsheets

On our Ada Computer Science online learning platform for students age 14 and up and their school teachers, we include content to help learners increase their computing, AI literacy, and data science skills:

• Artificial intelligence

• Machine learning

• Data structures

• Database concepts

• Big data

• Impacts of technology

Resources related to AI ethics

• Coded Bias, a documentary that explores the fallout of MIT Media Lab researcher Joy Buolamwini’s discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the USA to govern against bias in the algorithms that impact us all

• Algorithm Literacy Project, an algorithmic literacy project from Kids Code Jeunesse and the Canadian Commission for UNESCO

• An Ethics of Artificial Intelligence Curriculum for Middle School Students, from MIT

• Best Resources to Teach AI Ethics in the K-12 Classroom, a blog post from Catherine Yeo

• The Most Likely Machine, an AI ethics activity for pre-teens from the design firm Artefact

• MIT RAISE (Responsible AI for Social Empowerment and Education), a new initiative at Massachusetts Institute of Technology with a mission to innovate learning and education in the era of AI

Classroom resources for teaching machine learning and AI

• The Artificial Intelligence (AI) for K-12 initiative (AI4K12): big ideas of AI, AI curriculum guidelines, resources for the classroom, and teacher professional development, sponsored by AAAI and CSTA

• DAILy, a middle school AI curriculum from MIT

• RAISE – MIT AI Literacy units, free K-12 lessons on AI

• A list of AI and ML resources, from Annabel Lindner

• A list of AI education learning materials and websites, from Machine Learning for Kids

• Curriculum materials for teaching AI, from Exploring Computer Science

• Unplugged ML/AI activities, from Teaching London Computing

• Dancing with AI lesson plans for teaching AI with dance and Scratch, from MIT Media Lab

• A set of AI learning activities, from Steamlabs

• Video lessons on AI for early and upper elementary (free account needed to access), from CS is Elementary

• Clickschool resources for teaching AI to ages 11-1

• India’s CBSE (Central Board of Secondary Education) AI curriculum for Grades 8-12

Software for teaching young people about machine learning and AI

• Machine Learning For Kids (includes learning activities that use Scratch and Python)

• Google’s Teachable Machine (includes learning activities)

• IBM’s Watson (uses the Machine Learning For Kids platform, includes learning activities)

• eCraft2Learn’s Snap! extensions for building AI programs (includes learning activities)

• Animo Lab, a free prototype of an AI simulation game designed to help kids and adults learn about reinforcement learning (RL) and modern AI in a fun and digestible way

• https://playground.tensorflow.org/, a tool for demonstrating and tinkering with a neural network

Classroom resources for teaching data science

• Common Online Data Analysis Platform (CODAP), educational software for data analysis, suitable for grades 6–14, includes interactive games and activities

• A collection of datasets for teaching data rather than ML, from the Data Education in Schools team at the University of Edinburgh

• Kaggle offers open-source datasets

• Edge Impulse for projects that collect data using electronic sensors

• Data Science lesson activities for primary learners, from Data Education in Schools

• Data Clubs lesson activities for middle school students

• Free online student-facing introduction to Data Science Course

Reading and teacher professional development

• Tech Trends In Practice: The 25 Technologies That Are Driving The 4th Industrial Revolution, book by Bernard Marr

• Reuters article about ML, facial recognition and ethics

• IBM AI education content for K-12 teachers on Mindspark

• The data investigation process for teaching the data science from the Friday Institute

• A presentation from the AI4K12 Elementary Working Group on integrating AI into elementary/primary teaching

• Reuters article about Amazon’s attempt to use AI to filter job applications