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:
AI4K12
Code.org
(ISTE AI Projects for Secondary) and (ISTE AI Projects for CS Teachers)
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. Gemini vs. BingAI vs. ClaudeAI: Which one? Explain nuanced differences (ChatGPT) (Gemini) (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:
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:
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)