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Developing the AI Framework: Considerations

Levels of connectivity and access

Existing system capacity

Local contexts and cultures

Individual barriers…

Varying Systems

Varying mandates/policies

Degrees of system autonomy

Curriculum structures and philosophies

Years of enrolment…

Varying Contexts

AIK-12 Curriculum Mapping

Pedagogical Taxonomies/Models

Existing Academic and Technical Work

UNESCO Policy Guidelines

Existing Competency Frameworks

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Framework Design Decisions and Foundations

A developmental framework

Competency-based education

Experiential Learning

Constructionism

Computational Action

“I can design and create personally meaningful AI applications”

“I am a member of a larger community of AI-engaged citizens”

“Children learn best when they are actively engaged in constructing something that has

personal meaning to them.”

– Seymour Papert

Knowledge, skills, attributes/values applied in (and to) a context

Head

Heart

Hands

Concrete Experience

Reflective Observation

Abstract Conceptualization

Active Experimentation

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Aspects	Progression
Aspects	Understand	Apply	Create
Human-centred mindset	Critical Reflections on AI	Safe and Responsible Use	Self-actualization in the AI Era
Ethics of AI	Human Agency	Ethics by Design	AI Citizenship
AI Foundations	Data, Algorithms, and Models	Programming and Data Analysis	Modeling and Visual Representations
AI skills	AI Techniques and Applications	Practical AI Skills	Creating AI Products
AI for problem solving	Problem Scoping	Co-design	Co-creation and Feedback Loops

A proposed AI competency framework for school students

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Understand: Critical Reflections on AI	Apply: Safe and Responsible Use	Create: Self-actualization in the AI Era
Defines examples of AI in everyday life Critically discusses benefits, limitations, and risks of AI Gradually builds and expresses an understanding of human rights, social justice, inclusion, equity… Describes the connections between technology and social change	Develops values on personal usage of AI based on trade-offs Takes action to protect personal privacy and security Practices safe use of AI Identifies risks to human rights, human dignity, equity & inclusion, bias amplification…	Pursues personal fulfillment in the AI era Develops a growth mindset, resilience and persistence Develops and defends views on long-term impact of AI on society

Human-centred mindset

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Understand: Human Agency	Apply: Ethics by Design	Create: AI Citizenship
Understands ethical principles linked to AI: do-no-harm, safety and security, fairness, non-discrimination, the right to privacy, data protection, human oversight and determination, transparency, explainability Recognizes AI as human-led Understands the critical steps of AI development Describes ethical challenges that may arise in AI (bias, privacy, security, transparency, explainability…)	Assesses the purpose of AI products beyond what is explicitly stated Communicates the benefits and potential ethical challenges of AI products engaged Explains how the decisions of AI creators impact system outcomes Proposes (or implements) modifications to address ethical concerns	Pursues ways to reduce the environmental costs of AI Reflects on existing inequities in developing AI, and root causes Advocates for ethical and responsible AI use in society Contributes to the co-creation of human- centred and inclusive societies

Ethics of AI

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Understand: Data, Algorithms, and Models	Apply: Programming and Data Analysis	Create: Models and Visual Representations
Describes basic functions and algorithms Understands how data is collected, processed, and used Understands the principles of data ownership & privacy Understands the goals, benefits, and limitations of modeling data Understands sources of bias (human, data, algorithmic)	Constructs a database, and performs common operations on it Executes pre-processing techniques to ensure robust and fair data Analyzes data through models to extract meaning Uses programming skills to implement basic algorithms and models	Creates visual representations of data using appropriate techniques Creates abstractions of AI systems using flowcharts, diagrams and pseudocode Evaluates existing algorithms and models for specific use cases

AI Foundations

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Understand: AI Techniques and Applications	Apply: Practical AI Skills	Create: Creating AI Products
Knows key AI techniques Understands how AI techniques are applied across categories of AI technologies and domains Based on real use cases, evaluates accessibility and the linguistic / cultural diversity of AI Describes the challenge of explainability for different types of algorithms and models	Uses open-source AI tools and programming libraries Debugs pre-existing AI systems Designs and implements testing strategies for AI systems Implements basic AI techniques for specific applications	Improves existing open source AI models to fit specific goals Evaluates existing AI models for specific problem needs (e.g. across culture or language) Skillfully develops AI products using multiple AI tools to solve problems Assesses the limitations and risks of personal and peer AI creations

AI skills