UNESCO AI Competency Framework for Students
A global blueprint for AI curricula and future-ready students
Introduction
The opportunities and challenges created by the rapid expansion of Artificial Intelligence (AI) capabilities make it vital that education systems equip students with the knowledge, skills, and values they will need to:
Become effective users of AI
Become informed and responsible evaluators of AI
Become active co-creators of ethical and sustainable AI
The UNESCO AI Competency Framework for Students (AI CFS) aims to provide the human-centered and ethically-grounded approach needed to achieve this goal by:
Providing a global blueprint to inform the design of AI curricula.
Guiding curricular design with key learning objectives at different mastery levels.
Recommending a roadmap for implementing and adapting AI curricula.
Key Principles
The AI CFS is organized around 5 key principles:
Fostering a critical approach to AI
Students should develop the critical thinking skills needed to assess when and how AI should be used, question the assumed benefits of AI, and consider its potential risks - including its impact on society and the environment.
Prioritizing human-centred interaction with Al
Students should develop the competencies needed to use AI in ways that serve human capabilities, protect human dignity, and promote justice and sustainability.
Encouraging environmentally sustainable Al
Students need to understand the potential negative environmental impact of AI development and deployment.
Promoting inclusivity in AI competency development
All students should have equal access to opportunities for developing Al competencies. AI systems designs should be inclusive and consider the needs of learners from diverse backgrounds and with varying abilities.
Building core AI competencies for lifelong learning
Students should develop core AI competencies providing foundation for lifelong learning and adaptation to new Al technologies, including an ethical and human-centered mindset, knowledge of ethical principles and regulations, and skills to combat bias and promote transparency.
Structure
The AI CFS is structured as a matrix with two dimensions:
Four aspects of AI competencies (matching closely those of the UNESCO AI Competency Framework for teachers):
Human-centered mindset
Ethics of AI
AI techniques and applications
AI system design
Three levels of progression or mastery (matching closely the revised Bloom taxonomy):
Understand
Apply
Create
The intersection of these dimensions creates twelve competency blocks, providing a blueprint for learning outcomes and guiding curricular development.
Progression levels
The three levels represent increasing sophistication, proficiency, and ethical consciousness in understanding, using, and co-creating AI technology.
Level 1: Understand: This level focuses on developing a foundational understanding of AI, its potential impacts, and the ethical issues surrounding it.
Level 2: Apply: Students learn to become responsible and effective users of AI, applying their knowledge and skills to new learning contexts and critically evaluating existing AI tools and methods.
Level 3: Create: Students act as conscientious AI co-creators, developing human-centered solutions to real-world challenges, designing and testing their own AI tools, and considering the broader societal implications of their work.
Aspects
Each aspect encompasses essential elements of students' AI competency:
Human-centered mindset: Focuses on students' values, beliefs, and critical thinking skills applied to AI. It encourages responsible and ethical engagement with AI, prioritizing human needs, rights, and agency.
Ethics of AI: Addresses the social and ethical implications of AI, encouraging students to internalize ethical principles, comply with regulations, and understand AI citizenship.
AI techniques and applications: Integrates conceptual knowledge and operational skills related to data, AI technologies, and specific AI tools and tasks.
AI system design: Covers the skills required for problem scoping, as well as the design, development, and optimization of AI solutions. This challenges students to understand the technicalities of AI.
Specifications
The AI CFS further specifies what each competency aspect entails, at different levels of mastery, in terms of:
Curricular goals (“AI curricula should…”) → In italics below
Suggested pedagogical methods (“Institutions and teachers can consider and adapt the following learning methods…”)
Learning environments (“The following learning settings can be provided and adapted…”)
Understand
Human Agency
Foster an understanding that AI is human-led
Facilitate an understanding on the necessity of exercising sufficient human control over AI
Nurture critical thinking on the dynamic relationship between human agency and machine agency
Embodied Ethics
Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts
Facilitate scenario-based understandings of ethical principles on AI and their
personal implications
Guide the embodied reflection and internalization of ethical principles on AI
AI Foundations
Exemplify the definition and scope of AI
Develop conceptual knowledge on how AI is trained based on data and algorithms
Foster open-minded thinking on AI and an interdisciplinary foundation for AI
Concretize human-centred considerations in the design and use of AI
Problem-Scoping
Scaffold critical thinking skills on when AI should not be used
Support the acquisition and reinforcement of skills in scoping a problem to be
solved by an AI system
Develop skills on assessing AI systems’ need for data, algorithms and computing resources
Apply
Human Accountability
Develop a view that human accountability is a legal obligation of AI creators and AI service providers
Generate the understanding that human accountability is a legal and social
responsibility when using AI in making decisions about humanity
Nurture the personal attitude that human accountability requires personal
competencies to steer the purposeful use of AI
Ethics of AI
Foster self-awareness and habitual compliance with ethical principles for the responsible use of AI
Offer opportunities to reinforce self-discipline in the responsible use of AI
Deepen practical knowledge on the safe use of AI and awareness of locally
applicable regulations
Applications Skills
Offer opportunities to strengthen knowledge and skills on data modelling, engineering and analysis
Provide opportunities to acquire age-appropriate technical skills in AI programming
Encourage students to develop analytical and synthesis skills to leverage open-
source datasets and AI tools
AI System Design
Scaffold the acquisition of methodological knowledge and technical skills on AI architecture
Support the preparation of advanced technical skills and project management
competencies needed by AI system building
Create
AI Society Citizenship
Foster awareness of being a critical AI citizen
Nurture personal and social responsibilities in AI societies
Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI
Ethics by Design
Build awareness and understanding on ‘ethics by design’
Develop a critical attitude to the ethics-by-design principles behind existing AI systems and algorithms
Cultivating social responsibilities to uphold ‘ethics by design’ in regulations on AI
Creating AI Tools
Challenge and enable advanced skills to develop task-based AI tools
Enhance students’ creativity in applying AI knowledge and skills to customize
AI toolkits and coding
Equip students with skills to test and optimize their self-crafted AI tools
Iteration and Feedback
Develop the skills to critique AI systems
Support the building of technical skills and social responsibilities in optimizing, reconfiguring or shutting down an AI system
Foster students’ self-identities as co-creators in the AI era
Applying the Framework
Finally, the report offers 8 strategies (7 applicable to school leaders, and one more relevant to governments) to implement AI curricula effectively with the help of the AI CFS.
Curriculum developers should distinguish between essential, “future-proofing” core competencies and levels of mastery and optional / elective aspects and extensions.
The AI CFS should be implemented through a combination of channels, including informal and extracurricular ones, but also through the integration of core AI competencies into the curriculum, and tailored to local educational contexts.
The AI CFS encourages a shift toward competency-based education, flexible learning schedules, and a scaffolded, spiraling progression.
Implementing the AI CFS requires adequate resources, including teaching materials, digital infrastructure, and access to AI technologies.
Even more importanly, it requires human resources in the form of teacher training and support.
Cohort-based implementations can facilitate AI competency development by promoting social and emotional learning, as well as collaborative knowledge construction. Pedagogical methods should always be tailored to the specific AI competencies being taught.
Competency-based assessments are crucial for measuring mastery levels and informing teaching practices. They should incorporate authentic tasks and be evaluated against predefined standards.
Conclusion
The AI Competency Framework for Students (AI CFS) represents a call to action for schools to move beyond simply adopting and adapting to AI technologies, and instead proactively shaping a future where AI is both ethical and beneficial. The framework emphasizes three key assumptions:
Education should ensure students are equipped to shape AI's future role in society.
Students must be empowered as knowledgeable users, critical evaluators, and future leaders of AI.
Al competency goes beyond technical skills and encompasses a holistic set of conceptual understanding, human-centered values, and ethical principles.
Based on international expertise and multiple iterations and rounds of feedback, this framework provides a global blueprint helping schools provide the AI education future generations will need. However, it also requires refinements based on local contexts, and ongoing adaptation to the rapid evolution of AI technologies.