Five Big Ideas
What it's about
The Five Big Ideas is a framework that defines educational standards and offers guidance to curriculum designers creating AI learning materials for elementary and secondary schools. It was developed by members of the AI4K12 initiative. While our AI curriculum is based on this document, it doesn’t explicitly emphasize the concept of data or clearly separate algorithmic approaches from machine learning (even though that’s how we typically explain AI to students). You can explore the structure of the Five Big Ideas below [full document available here].
Big Idea #1: Perception
Computers perceive the world using sensors. Perception is the extraction of meaning from sensory information using knowledge. The transformation from signal to meaning takes place in stages, with increasingly abstract features and higher level knowledge applied at each stage.
Sensing
1-A-I Living Things
1-A-II Computer Sensors
1-A-III Digital Encoding
Processing
1-B-I Sensing vs. Perception
1-B-II Feature Extraction
1-B-III Abstraction Pipeline: Language
1-B-IV Abstraction Pipeline: Vision
Domain Knowledge
1-C-I Types of Domain Knowledge
1-C-II Inclusivity
Big Idea #2: Representation & Reasoning
Intelligent systems work with a representation of the world and use it to reason. Representation is one of the fundamental challenges of intelligence—both natural and artificial. Computers construct representations based on data, and these representations are used by algorithms that generate new insights from what is already known. Even though intelligent systems can analyze complex problems, they do not think like humans.
Representation
2-A-I Abstraction
2-A-II Symbolic representations
2-A-III Data structures
2-A-IV Feature vectors
Search
2-B-I State spaces and operators
2-B-II Combinatorial search
Reasoning
2-C-I Types of reasoning problems
2-C-II Reasoning algorithms
Big Idea #3: Learning
Computers can learn from data. Machine learning is a form of statistical inference that identifies patterns in data. Many areas of AI have advanced significantly thanks to learning algorithms that generate new representations. For this approach to be successful, a large amount of data—usually provided by humans—is often required, although in some cases the machine can collect it on its own.
Nature of Learning
3-A-I Humans vs. machines
3-A-II Finding patterns in data
3-A-III Training a model
3-A-IV Constructing vs. using a reasoner
3-A-V Adjusting internal representations
3-A-VI Learning from experience
Neural networks
3-B-I Structure of a neural network
3-B-II Weight adjustment
Datasets
3-C-I Feature sets
3-C-II Large datasets
3-C-III Bias
Big Idea #4: Natural Interaction
Intelligent systems require many types of knowledge to interact with humans in a natural way. They must understand human languages, recognize facial expressions and emotions, and use cultural and social conventions to draw conclusions from observed behavior. All of these tasks are challenging. Today’s AI systems can use language, but their intelligence has not yet surpassed the level of child-like thinking.
Natural Language
4-A-I Structure of language
4-A-II Ambiguity of language
4-A-III Reasoning about text
4-A-IV Applications
Commonsense Reasoning
4-B-I
Understanding Emotion
4-C-I
Philosophy of Mind
4-D-I
Big Idea #5: Societal Impact
AI can have both positive and negative impacts on society. The technology is changing how we work, travel, communicate, and care for one another. However, we must be cautious about the potential negative consequences that may arise. For example, bias in the data used to train an AI system could lead to certain individuals being disadvantaged compared to others. That’s why it’s important to discuss the impact AI has on our society and to consider criteria for designing and deploying ethical AI systems.
Ethical AI
5-A-I Diversity of Interests and Disparate Impacts
5-A-II Ethical Design Criteria
5-A-III Practicing Ethical Design
AI & Culture
5-B-I AI in Daily Life
5-B-II Trust and Responsibility
AI & the Economy
5-C-I Impacts of AI on Sectors of Society
5-C-II Effects on Employment
AI for Social Good
5-D-I Democratization of AI Technology
5-D-II Using AI to Solve Societal Problems