AI in Grades 7-8
Some concepts you might cover:
- AI impact and ramifications of misuse
- Ethics and Social Implications of AI
- MoralMachine (a human perspective on moral decisions made by machine intelligence)
- Coding and programming (Scratch and Python)
- Student participation in AI projects and competitions
Examples:
- Describe how the quality of data determines the success of an AI application
- Explain how AI systems pose a potential threat to equal opportunities
- Identify common types of AI application
- Identify the parts of a system that are AI and the parts that are not
- Compare data-driven models and rule-based models
- Describe how the data life cycle is applied to an AI system
- Train a machine learning model
- Explain the difference between training and test data
- Evaluate the performance of a decision-tree model vs. how a neural network learning algorithm works.
Resources you might use:
AI for Anyone
AI4K12
Code.org
(ISTE AI Projects for Secondary) and (ISTE AI Projects for CS Teachers)
Introduction to Python (or any online resource to learn introductory Python such as PythonWiki )
ElementsofAI (University of Helsinki, Introductory course, Part 2)
Teachable Machine (by Google, good introduction to available tools to inform AI)
Teach a computer to play a game (will draw a decision tree so students can inspect it and see which feature is referenced at each node.)
eCraft2Learn (enables students, non-expert programmers, to build AI programs)
AI in Grades 9-10
Some concepts you might cover:
- AI and Machine Learning
- Ethics and Social Implications of AI
- MoralMachine (a human perspective on moral decisions made by machine intelligence)
- Coding and programming (Scratch and Python)
- Student participation in AI projects and competitions
Examples:
- Natural language Processing and Generative AI
- ChatGPT vs. Bard vs. BingAI vs. ClaudeAI: Which one? Explain nuanced differences (ChatGPT) (Bard) (Bing AI - with alternative browsers) ( ClaudeAI )
- Describe how a machine learning model is trained
- Name ethical standards and guidelines for creating and using AI
- Compare the advantages and disadvantages of supervised learning algorithms
- Train models
- Evaluate whether a model is fit or not fit for purpose
- Identify a neural network as a supervised learning algorithm
- Identify the different components of a neural network and describe their purpose
- Evaluate the performance of a neural network
Resources you might use:
AI for Anyone
AI4K12
Code.org
CS50 Introduction to AI with Python (one of Harvard’s famous mooc courses)
Python AI (How to build a Neural Network & Make Predictions (also, good descriptions of Deep Learning, Machine Learning, etc.))
Python for NLP
ElementsofAI (University of Helsinki, Introductory course, Part 2)
Photoshop + generative AI
AI in Grades 11-12
Some concepts you might cover:
- Machine Learning, Deep Learning and Neural Networks
- Ethics and Social Implications of AI
- MoralMachine (a human perspective on moral decisions made by machine intelligence)
- Coding and programming (Scratch and Python)
- Student participation in AI projects and competitions
Examples:
- Describe the main AI paradigms
- Describe the potential social, cultural, and economic impacts of AI
- Compare AI learning types (supervised, unsupervised, reinforcement)
- Compare AI task types (classification, regression, clustering, generative, decision- making)
- Identify different AI engines (e.g. decision trees, k-nearest neighbors, neural networks, linear regression)
- Explain that different engines have different levels of explainability
- Choose the right algorithm to solve a particular problem
- Describe the role of weights and back propagation during the training of a neural network
Resources you might use:
AI for Anyone
AI4K12
Code.org
AI vs Machine Learning vs Deep Learning vs Neural Networks (a deep dive into nuances of these technologies)
TensorFlow (Machine Learning Platform)
Jupyter/Jupyter Notebooks &
Try Jupyter (user interface to learn and code, often used in AI tutorial models)
Google Colab (ability to write and execute Python in browser)
Intel - Teaching AI (lesson plans and project guides)
Edutopia (tips and resources for introducing students to AI)