Five Big Ideas
What is it?
The Five Big Ideas framework defines educational standards for AI literacy in primary and secondary schools. Developed by members of the AI4K12 initiative, it serves as a foundation for AI curriculum design. While our AI curriculum builds upon this framework, we also extend it by explicitly addressing data concepts and the distinction between algorithmic programming and machine learning, which are central to how we teach AI to students.
Perception
Computers perceive the world through sensors. However, perception isn’t passive—it requires interpreting sensor data based on prior knowledge and experience. The ability of computers to "see" and "hear" with accuracy is one of AI’s greatest achievements.
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
Representation & Reasoning
Intelligent systems construct representations of the world and use them to reason. Representation is a core challenge in both natural and artificial intelligence. AI systems process data-based representations through algorithms that generate new insights, but 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
Learning
Computers learn from data. Machine learning is a form of statistical inference that identifies patterns in data. Advances in AI have largely been driven by learning algorithms, which can create new representations. While AI often relies on large datasets provided by humans, some models can generate their own training data.
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
Natural Interaction
For AI to interact naturally with humans, it must integrate various types of knowledge. AI systems need to understand language, recognize emotions, and adapt to social and cultural norms. While today’s AI can process language, its reasoning remains at a childlike level.
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
Societal Impact
AI has both positive and negative effects on society. It reshapes how we work, communicate, and navigate daily life, but also raises ethical concerns. For example, biased training data can create unfair advantages or disadvantages for different groups. Discussing AI’s societal impact and ensuring ethical AI development is essential for responsible innovation.
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