AI Educator Certificate

Every single day Artificial Intelligence is being used to help us in a variety of ways, such as making sure that we arrive at our desired destinations. AI is here to stay and only seems to be getting more advanced and ubiquitous. In order to prepare students for an AI-filled future, we must begin AI literacy now and help to demystify the use and mechanisms of AI. This begins with a robust training program for educators so that they can teach about it and even begin to use AI tools in the classroom. This training will provide a comprehensive exploration into AI to help build an AI literacy and equip teachers with practical AI tools that can be used in the classroom across all subject areas.

The AI Educator Certificate training will take place over 3 days consisting of 90 mins of asynchronous training followed by 90 mins of synchronous time for educators to work on assignments. Completion of all activities will result in receiving the AI Educator Certificate.

Audience: K-12 Classroom Teachers, School Leaders

Duration: 3 days - each day consisting of 90 mins of asynchronous training followed by 90 mins of synchronous time for educators to work on assignments

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Topics of the AI Educator Certificate program:

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Day 1

Introduction to AI

In this section we will briefly explore how AI has emerged and how AI has advanced.

• History of AI

• Types of AI

• AI in every field

AI in Education
In this section we will learn about ways that AI is being used in Education systems as well as how the teaching of AI is unfolding in schools.

• AI Tutors

• AI grading

• AI learning

AI Pedagogy

In this section we will explore frameworks about teaching AI to students

• Big 5 ideas in AI education

• Constructionism and AI

• Tinkering and AI

Homework:

Read and discuss articles about the impacts of AI in education

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Day 2

Practical Machine Learning tools

In this section we will explore some practical tools that educators can use in the classroom for image, sound, and pose classification.

• What is Machine Learning?

• Predictions based on training

• How to train your AI

Bias in the Algorithm

In this section we will explore the nature of bias, how it appears, and how to train an AI to reduce bias.

• Negative effects of bias

• Pre-trained Neural Networks

• Adjusting for bias in training data

Make your own AI tool

In this section, you will learn to use and train a Machine Learning tool

• Develop a model

• Train the Neural Network

• Test its ability to correctly predict

Homework:

Complete the development of your AI tool

Day 3

Programming and AI (no coding experience needed)

In this section, we will explore how programming environments like Scratch can easily employ AI technologies.

• Design a face recognizing video game

• Explore how students can learn to program with AI

Advanced Programming in AI (no coding experience needed)

In this section, we will combine what we learned about training data and programming to make a custom interface

• Teach a computer to recognise and categorize with text-based inputs

• Explore strategies for teaching students to program AI

Going Further
In this section, we will explore some tools and resources for going further

• MIT Curriculum

• ISTE Educator Resources

• Online AI toolkits

• Using AI across the subject areas

Homework:

Complete one of the programming challenges

Please contact info@consiliencelearning.org
if you would like to offer this course for your faculty.

Instructor:

Mark Barnett is currently pursuing his PhD in Computer Engineering and is studying how students can benefit from learning about AI in ways that help them self-reflect on their own learning process by learning how AI learns. Mark has also developed a globally used AI curriculum that teaches the fundamentals of AI image-recognition. Mark has worked in education for over 15 years as an International educator, consultant, and Ed-tech leader who has provided training in topics such as Maker Learning, Project Based Learning, and Constructionist Pedagogy.